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Gower, Robert M., and Peter Richtárik. "Randomized Iterative Methods for Linear Systems." SIAM Journal on Matrix Analysis and Applications 36, no. 4 (2015): 1660–90. http://dx.doi.org/10.1137/15m1025487.
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Loizou, Nicolas, and Peter Richtárik. "Convergence Analysis of Inexact Randomized Iterative Methods." SIAM Journal on Scientific Computing 42, no. 6 (2020): A3979—A4016. http://dx.doi.org/10.1137/19m125248x.
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Xing, Lili, Wendi Bao, Ying Lv, Zhiwei Guo, and Weiguo Li. "Randomized Block Kaczmarz Methods for Inner Inverses of a Matrix." Mathematics 12, no. 3 (2024): 475. http://dx.doi.org/10.3390/math12030475.
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In this paper, two randomized block Kaczmarz methods to compute inner inverses of any rectangular matrix A are presented. These are iterative methods without matrix multiplications and their convergence is proved. The numerical results show that the proposed methods are more efficient than iterative methods involving matrix multiplications for the high-dimensional matrix.
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Zhao, Jing, Xiang Wang, and Jianhua Zhang. "Randomized average block iterative methods for solving factorised linear systems." Filomat 37, no. 14 (2023): 4603–20. http://dx.doi.org/10.2298/fil2314603z.
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Recently, some randomized iterative methods are proposed to solve large-scale factorised linear systems. In this paper, we present two randomized average block iterative methods which still take advantage of the factored form and need not perform the entire matrix. The new methods are pseudoinverse-free and can be implemented for parallel computation. Furthermore, we analyze their convergence behaviors and obtain the exponential convergence rate. Finally, some numerical examples are carried out to show the effectiveness of our new methods.
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Zhang, Yanjun, and Hanyu Li. "Splitting-based randomized iterative methods for solving indefinite least squares problem." Applied Mathematics and Computation 446 (June 2023): 127892. http://dx.doi.org/10.1016/j.amc.2023.127892.
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Yunak, O., M. Klymash, O. Shpur, and V. Mrak. "MATHEMATICAL MODEL OF FRACTAL STRUCTURES RECOGNITION USING NEURAL NETWORK TECHNOLOGY." Information and communication technologies, electronic engineering 3, no. 1 (2023): 1–9. http://dx.doi.org/10.23939/ictee2023.01.001.
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The article goes about the methods of training a neural network to recognize fractal structures with the rotation of iteration elements by means of an improved randomized system of iteration functions. Parameters of fractal structures are used to calculate complex parameters of physical phenomena. They are an effective tool in scientific works and used to calculate quantitative indicators in technical tasks. The calculation of these parameters is a very difficult mathematical problem. This is caused by the fact that it is very difficult to describe the mathematical model of the fractal image, it is difficult to determine the parameters of the iterative functions. The neural network learning will allow you to quickly determine the parameters of the first iterations of the fractal based on the finished fractal image and basing on them to determine the parameters of the iterative functions. The improved system of randomized iterative functions (SRIF) will allow to describe the mathematical process and to develop the software for generating fractal structures with the possibility of rotating elements of iterations. In its turn, this will make it possible to form an array of data for training a neural network. The trained neural network will be able to determine the parameters of the figures of the first iterations by means of which it will be possible to build a system of iterative functions. It will help to reproduce a fractal structure qualitatively. This approach can be used for three-dimensional fractal structures. After setting the parameters of the first iterations of the fractal, it will be possible to determine the geometric structure which is the basis of the fractal structure. In the future, this approach may be included in the system for recognizing objects under fractal structures, for example, under masking nets.
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Sabelfeld, Karl K. "Randomized Monte Carlo algorithms for matrix iterations and solving large systems of linear equations." Monte Carlo Methods and Applications 28, no. 2 (2022): 125–33. http://dx.doi.org/10.1515/mcma-2022-2114.
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Abstract Randomized scalable vector algorithms for calculation of matrix iterations and solving extremely large linear algebraic equations are developed. Among applications presented in this paper are randomized iterative methods for large linear systems of algebraic equations governed by M-matrices. The crucial idea of the randomized method is that the iterations are performed by sampling random columns only, thus avoiding not only matrix-matrix but also matrix-vector multiplications. The suggested vector randomized methods are highly efficient for solving linear equations of high dimension, the computational cost depends only linearly on the dimension.
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Popkov, Yuri S., Yuri A. Dubnov, and Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning." Mathematics 11, no. 17 (2023): 3651. http://dx.doi.org/10.3390/math11173651.
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This paper is devoted to problem-oriented reinforcement methods for the numerical implementation of Randomized Machine Learning. We have developed a scheme of the reinforcement procedure based on the agent approach and Bellman’s optimality principle. This procedure ensures strictly monotonic properties of a sequence of local records in the iterative computational procedure of the learning process. The dependences of the dimensions of the neighborhood of the global minimum and the probability of its achievement on the parameters of the algorithm are determined. The convergence of the algorithm with the indicated probability to the neighborhood of the global minimum is proved.
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Xing, Lili, Wendi Bao, and Weiguo Li. "On the Convergence of the Randomized Block Kaczmarz Algorithm for Solving a Matrix Equation." Mathematics 11, no. 21 (2023): 4554. http://dx.doi.org/10.3390/math11214554.
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A randomized block Kaczmarz method and a randomized extended block Kaczmarz method are proposed for solving the matrix equation AXB=C, where the matrices A and B may be full-rank or rank-deficient. These methods are iterative methods without matrix multiplication, and are especially suitable for solving large-scale matrix equations. It is theoretically proved that these methods converge to the solution or least-square solution of the matrix equation. The numerical results show that these methods are more efficient than the existing algorithms for high-dimensional matrix equations.
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Shcherbakova, Elena M., Sergey A. Matveev, Alexander P. Smirnov, and Eugene E. Tyrtyshnikov. "Study of performance of low-rank nonnegative tensor factorization methods." Russian Journal of Numerical Analysis and Mathematical Modelling 38, no. 4 (2023): 231–39. http://dx.doi.org/10.1515/rnam-2023-0018.
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Abstract In the present paper we compare two different iterative approaches to constructing nonnegative tensor train and Tucker decompositions. The first approach is based on idea of alternating projections and randomized sketching for factorization of tensors with nonnegative elements. This approach can be useful for both TT and Tucker formats. The second approach consists of two stages. At the first stage we find the unconstrained tensor train decomposition for the target array. At the second stage we use this initial approximation in order to fix it within moderate number of operations and obtain the factorization with nonnegative factors either in tensor train or Tucker model. We study the performance of these methods for both synthetic data and hyper-spectral image and demonstrate the clear advantage of the latter technique in terms of computational time and wider range of possible applications.
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Sergeenko, Anna, and Oleg Granichin. "Sensor network control based on randomized and multi-agent approaches." Cybernetics and Physics, Volume 11, 2022, Number 2 (September 30, 2022): 94–105. http://dx.doi.org/10.35470/2226-4116-2022-11-2-94-105.
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In this paper, a development of randomized and multiagent algorithms is presented. The examples and their advantages are discussed. Different combined algorithms which are applicable for the multi-sensor multitarget tracking problem are shown. These algorithms belong to the class of methods used in derivative-free optimization and has proven efficacy in the problems including significant non-statistical uncertainties. The new algorithm which is an Accelerated consensus-based SPSA algorithm is validated through simulation.The main feature of that algorithm, combining the SPSA techniques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal’s limitation.
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Bao, Rong, Yongdong Li, Hongguang Wang, and Chunliang Liu. "A Multi-Constrained Optimization Method for THz Backward Wave Oscillators." Applied Sciences 12, no. 20 (2022): 10583. http://dx.doi.org/10.3390/app122010583.
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The current design period for various backward wave oscillators (BWOs) is still at least several months. How to find the best structure parameters with an efficient and stable optimization method is a problem facing researchers in both scientific research and engineering work. In this paper, a non-randomized iterative optimization method is proposed. It applies orthogonal design methods to find local solutions that can provide optimal ‘gradient direction’ for several successive next iteration steps. An evaluation function is designed to distinguish the better ones from the local solutions in the multi-constrained optimization of such BWOs. Optimizations from different starting points are performed separately for a global optimal solution. Two BWOs at different frequency ranges are optimized using the proposed method. The validity and stability of the method are verified. It is believed that the method can provide the global optimum and shorten the design period of THz BWOs.
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Mullane, Sarah L., Dana R. Epstein, and Matthew P. Buman. "The “House of Quality for Behavioral Science”—a user-centered tool to design behavioral interventions." Translational Behavioral Medicine 9, no. 4 (2018): 810–18. http://dx.doi.org/10.1093/tbm/iby084.
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Abstract Within the behavioral field, a plethora of conceptual frameworks and tools have been developed to improve transition from efficacy to effectiveness trials; however, they are limited in their ability to support new, iterative intervention design decision-making methodologies beyond traditional randomized controlled trial design. Emerging theories suggest that researchers should employ engineering based user-centered design (UCD) methods to support more iterative intervention design decision-making in the behavioral field. We present, an adaptation of a UCD tool used in the engineering field—the Quality Function Deployment “House of Quality” correlation matrix, to support iterative intervention design decision-making and documentation for multicomponent behavioral interventions and factorial trial designs. We provide a detailed description of the adapted tool—“House of Quality for Behavioral Science”, and a step-by-step use-case scenario to demonstrate the early identification of intervention flaws and prioritization of requirements. Four intervention design flaws were identified via the tool application. Completion of the relationship correlation matrix increased requirement ranking variance for the researcher (σ2 = 0.47 to 7.19) and participant (σ2 = 0.56 to 3.89) perspective. Requirement prioritization (ranking) was facilitated by factoring in the strength of the correlation between each perspective and corresponding importance. A correlational matrix tool such as the “House of Quality for Behavioral Science” may provide a structured, UCD approach that balances researcher and participant needs and identifies design flaws for pragmatic behavioral intervention design. This tool may support iterative design decision-making for multicomponent and factorial trial designs.
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Hurley, James. "Meta-analysis of Clinical Studies of Diagnostic Tests: Developments in How the Receiver Operating Characteristic “Works”." Archives of Pathology & Laboratory Medicine 135, no. 12 (2011): 1585–90. http://dx.doi.org/10.5858/arpa.2011-0016-so.
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Meta-analytic summaries are needed on clinical studies of diagnostic tests. Meta-analyses on clinical studies of diagnostic tests commonly use the receiver operating characteristic method, which differs conceptually and computationally from the more widely known meta-analytic methods applicable in other contexts, such as in studies of randomized controlled trials. Important conceptual differences for clinical studies of diagnostic tests versus randomized controlled trials are that the study subpopulations are not defined by random allocation and the test threshold typically varies across studies to accommodate “rule in” versus “rule out” testing strategies. The receiver operating characteristic method has evolved substantially in the past decade, and the most recent approaches use multilevel regression methods that require iterative computational solutions to estimate the influence of the study-level variables. Using current methodology, a meta-analysis on clinical studies of diagnostic tests can address questions relevant to the clinical application of a diagnostic test that cannot be answered at the level of the individual study.
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Armin, Julie S., Uma Nair, Peter Giacobbi, Gayle Povis, Yessenya Barraza, and Judith S. Gordon. "Developing a Guided Imagery Telephone-Based Tobacco Cessation Program for a Randomized Controlled Trial." Tobacco Use Insights 13 (January 2020): 1179173X2094926. http://dx.doi.org/10.1177/1179173x20949267.
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Background/Aims: Guided imagery is an evidence-based, multi-sensory, cognitive process that can be used to increase motivation and achieve a desired behavior. Quitlines are effective, standard care approaches for tobacco cessation; however, utilization of quitlines is low. Using guided imagery-based interventions for smoking cessation may appeal to smokers who do not utilize traditional quitline services. This paper reports the development of program materials for a randomized controlled feasibility trial of a guided imagery-based smoking cessation intervention. The objective of the formative work was to ensure that program materials are inclusive of groups that are less likely to use quitlines, including men and racial/ethnic minority tobacco users. Methods: A three-phase process was used to complete formative assessment: (1) integration of evidence-based cessation practices into program development; (2) iterative small group interviews (N = 46) to modify the program; and (3) pilot-testing the coaching protocol and study process among a small sample of smokers (N = 5). Results: The Community Advisory Board and project consultants offered input on program content and study recruitment based on their knowledge of minority communities with whom they conduct outreach. Small group interview participants included members of underserved quitline populations (52.37% non-white; 55.56% men). Only 28.26% of participants had prior experience with guided imagery, but others described the use of similar mindfulness and meditation practices. Participant feedback was incorporated into program materials and protocols. Discussion: Iteratively collected feedback and pilot testing influenced program content and delivery and informed study processes for a randomized controlled feasibility trial of a telephone-delivered, guided imagery-based intervention.
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Lowe, Cabella, Harry Hanuman Sing, Mitchell Browne, Meshari F. Alwashmi, William Marsh, and Dylan Morrissey. "Usability Testing of a Digital Assessment Routing Tool: Protocol for an Iterative Convergent Mixed Methods Study." JMIR Research Protocols 10, no. 5 (2021): e27205. http://dx.doi.org/10.2196/27205.
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Background Musculoskeletal conditions account for 16% of global disability, resulting in a negative effect on millions of patients and an increasing burden on health care utilization. Digital technologies that improve health care outcomes and efficiency are considered a priority; however, innovations are often inadequately developed and poorly adopted. Further, they are rarely tested with sufficient rigor in clinical trials—the gold standard for clinical proof of efficacy. We have developed a new musculoskeletal Digital Assessment Routing Tool (DART) that allows users to self-assess and be directed to the right care. DART requires usability testing in preparation for clinical trials. Objective This study will use the iterative convergent mixed methods design to assess and mitigate all serious usability issues to optimize user experience and adoption. Using this methodology, we will provide justifiable confidence to progress to full-scale randomized controlled trials when DART is integrated into clinical management pathways. This study protocol will provide a blueprint for future usability studies of mobile health solutions. Methods We will collect qualitative and quantitative data from 20-30 participants aged 18 years and older for 4 months. The exact number of participants recruited will be dependent on the number of iterative cycles required to reach the study end points. Building on previous internal testing and stakeholder involvement, quantitative data collection is defined by the constructs within the ISO 9241-210-2019 standard and the system usability scale, providing a usability score for DART. Guided by the participant responses to quantitative questioning, the researcher will focus the qualitative data collection on specific usability problems. These will then be graded to provide the rationale for further DART system improvements throughout the iterative cycles. Results This study received approval from the Queen Mary University of London Ethics of Research Committee (QMREC2018/48/048) on June 4, 2020. At manuscript submission, study recruitment was on-going, with data collection to be completed and results published in 2021. Conclusions This study will provide evidence concerning mobile health DART system usability and acceptance determining system improvements required to support user adoption and minimize suboptimal system usability as a potential confounder within subsequent noninferiority clinical trials. Success should produce a safe effective system with excellent usability, facilitating quicker and easier patient access to appropriate care while reducing the burden on primary and secondary care musculoskeletal services. This deliberately rigorous approach to mobile health innovation could be used as a guide for other developers of similar apps. International Registered Report Identifier (IRRID) DERR1-10.2196/27205
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Ray, Abhishek, Mario Ventresca, and Karthik Kannan. "A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions." Information Systems Research 32, no. 4 (2021): 1099–114. http://dx.doi.org/10.1287/isre.2021.1031.
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Iterative combinatorial auctions are known to resolve bidder preference elicitation problems. However, winner determination is a known key bottleneck that has prevented widespread adoption of such auctions, and adding a time-bound to winner determination further complicates the mechanism. As a result, heuristic-based methods have enjoyed an increase in applicability. We add to the growing body of work in heuristic-based winner determination by proposing an ant colony metaheuristic–based anytime algorithm that produces optimal or near-optimal winner determination results within specified time. Our proposed algorithm resolves the speed versus accuracy problem and displays superior performance compared with 20 past state-of-the-art heuristics and two exact algorithms, for 94 open test auction instances that display a wide variety in bid-bundle composition. Furthermore, we contribute to the literature in two predominant ways: first, we represent the winner determination problem as one of finding the maximum weighted path on a directed cyclic graph; second, we improve upon existing ant colony heuristic–based exploration methods by implementing randomized pheromone updating and randomized graph pruning. Finally, to aid auction designers, we implement the anytime property of the algorithm, which allows auctioneers to stop the algorithm and return a valid solution to the winner determination problem even if it is interrupted before computation ends.
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Bruce, Beau B., Robert El-Kareh, John W. Ely, et al. "Methodologies for evaluating strategies to reduce diagnostic error: report from the research summit at the 7th International Diagnostic Error in Medicine Conference." Diagnosis 3, no. 1 (2016): 1–7. http://dx.doi.org/10.1515/dx-2016-0002.
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AbstractIn this article we review current evidence on strategies to evaluate diagnostic error solutions, discuss the methodological challenges that exist in investigating the value of these strategies in patient care, and provide recommendations for methods that can be applied in investigating potential solutions to diagnostic errors. These recommendations were developed iteratively by the authors based upon initial discussions held during the Research Summit of the 7th Annual Diagnostic Error in Medicine Conference in September 2014. The recommendations include the following elements for designing studies of diagnostic research solutions: (1) Select direct and indirect outcomes measures of importance to patients, while also practical for the particular solution; (2) Develop a clearly-stated logic model for the solution to be tested; (3) Use rapid, iterative prototyping in the early phases of solution testing; (4) Use cluster-randomized clinical trials where feasible; (5) Avoid simple pre-post designs, in favor of stepped wedge and interrupted time series; (6) Leverage best practices for patient safety research and engage experts from relevant domains; and (7) Consider sources of bias and design studies and their analyses to minimize selection and information bias and control for confounding. Areas of diagnostic error mitigation research identified for further attention include: role of competing diagnoses, understanding the impacts of organizational culture, timing of diagnosis, and sequencing of research studies. Future research will likely require novel clinical, health services, and qualitative research methods to address the age-old problem of arriving at an accurate diagnosis.
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Willcox, Jane C., Daniel Chai, Lawrence J. Beilin, et al. "Evaluating Engagement in a Digital and Dietetic Intervention Promoting Healthy Weight Gain in Pregnancy: Mixed Methods Study." Journal of Medical Internet Research 22, no. 6 (2020): e17845. http://dx.doi.org/10.2196/17845.
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Background Early excess and inadequate gestational weight gain (GWG) have been associated with negative outcomes for mother and child. The use of digital media to deliver pregnancy lifestyle interventions is increasing, but there is little data on participant engagement. The Pregnancy Lifestyle Activity and Nutrition (PLAN) intervention pilot study was an electronic health and dietetic-delivered intervention program promoting healthy GWG in early pregnancy. Objective This study aims to explore the interactions of participants with the program and to assess its acceptability. Methods This study uses both quantitative and qualitative methods using data from parent randomized controlled trial (ACTRN12617000725369). Quantitative data from 22 participants in the intervention arm who completed the study provided measures of the interactions participants had with the digital components of the program and with dietetic consultations. A descriptive qualitative analysis employed semistructured interviews with 9 participants to elicit views on the acceptability of the intervention and its components. Results The electronic delivery of information and recording of weight from 8 to 20 weeks of gestation were universally accepted. Component (face-to-face dietitian, weight tracker, website information delivery, and SMS goal prompting) acceptability and engagement differed between individuals. A total of 4 key themes emerged from the qualitative analysis: supporting lifestyle change, component acceptability and value, delivery platforms, and engagement barriers. Conclusions The PLAN intervention and its delivery via a blend of personal dietetic consultations and digital program delivery was found to be acceptable and valuable to pregnant women. Individuals responded differently to various components, emphasizing the importance of including women in the development of lifestyle interventions and allowing participants to choose and tailor programs. Larger randomized controlled trials using these insights in a broader section of the community are needed to inform the iterative development of practical, time-efficient, and cost-effective ways of supporting optimal GWG with the potential to optimize outcomes for pregnant women and their child.
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Davtyan, Aram, Sepehr Sameni, Llukman Cerkezi, Givi Meishvili, Adam Bielski, and Paolo Favaro. "KOALA: A Kalman Optimization Algorithm with Loss Adaptivity." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6471–79. http://dx.doi.org/10.1609/aaai.v36i6.20599.
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Optimization is often cast as a deterministic problem, where the solution is found through some iterative procedure such as gradient descent. However, when training neural networks the loss function changes over (iteration) time due to the randomized selection of a subset of the samples. This randomization turns the optimization problem into a stochastic one. We propose to consider the loss as a noisy observation with respect to some reference optimum. This interpretation of the loss allows us to adopt Kalman filtering as an optimizer, as its recursive formulation is designed to estimate unknown parameters from noisy measurements. Moreover, we show that the Kalman Filter dynamical model for the evolution of the unknown parameters can be used to capture the gradient dynamics of advanced methods such as Momentum and Adam. We call this stochastic optimization method KOALA, which is short for Kalman Optimization Algorithm with Loss Adaptivity. KOALA is an easy to implement, scalable, and efficient method to train neural networks. We provide convergence analysis and show experimentally that it yields parameter estimates that are on par with or better than existing state of the art optimization algorithms across several neural network architectures and machine learning tasks, such as computer vision and language modeling. The project page with the code and the supplementary materials is available at https://araachie.github.io/koala/.
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Burke, Holly M., Catherine Packer, Akuzike Zingani, et al. "Testing a counseling message for increasing uptake of self-injectable contraception in southern Malawi: A mixed-methods, clustered randomized controlled study." PLOS ONE 17, no. 10 (2022): e0275986. http://dx.doi.org/10.1371/journal.pone.0275986.
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Objective While self-injection of subcutaneous depot medroxyprogesterone acetate (DMPA-SC) has well-documented benefits, uptake may be improved by addressing client concerns such as fear of self-injury and low self-efficacy. However, current training materials for family planning providers do not address these concerns. We used an iterative process with family planning providers and clients, male community leaders and partners, and stakeholders in Malawi to develop a counseling message addressing user-centered concerns about self-injection. We report on our testing of the effectiveness of this evidence-based message for increasing self-injection uptake in the context of full method choice. Methods We randomized 60 public facilities across two districts in southern Malawi to orient their providers to the message (treatment) or not (control). After strengthening data quality, we extracted routine service delivery data from the facilities six months before and after introducing the message. We compared pre- and post-orientation trends for the treatment and control groups using generalized linear mixed models. We conducted eight focus group discussions with a sample of providers oriented to the message. Results The message was feasible to implement and highly acceptable to providers. During June 2020–June 2021, 16,593 new clients used injectables in Mangochi district (52% DMPA-SC; 15% self-injected). In Thyolo district, 7,761 new clients used injectables during July 2020–July 2021 (29% DMPA-SC; 14% self-injected). We observed high variability in number of clients and self-injection uptake across facilities and over time, indicating inconsistent offering of self-injection. In both districts, we found significant increases in self-injection in treatment facilities after message introduction. However, this increase was not sustained, especially when DMPA-SC was unavailable or about to expire. Conclusion Based on the study findings, we recommend the evidence-based message be used in programs offering DMPA-SC self-injection services. However, effective use of the message is contingent upon a consistent supply of DMPA-SC.
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Chen, Jinghui, Yu Cheng, Zhe Gan, Quanquan Gu, and Jingjing Liu. "Efficient Robust Training via Backward Smoothing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6222–30. http://dx.doi.org/10.1609/aaai.v36i6.20571.
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Adversarial training is so far the most effective strategy in defending against adversarial examples. However, it suffers from high computational costs due to the iterative adversarial attacks in each training step. Recent studies show that it is possible to achieve fast Adversarial Training by performing a single-step attack with random initialization. However, such an approach still lags behind state-of-the-art adversarial training algorithms on both stability and model robustness. In this work, we develop a new understanding towards Fast Adversarial Training, by viewing random initialization as performing randomized smoothing for better optimization of the inner maximization problem. Following this new perspective, we also propose a new initialization strategy, backward smoothing, to further improve the stability and model robustness over single-step robust training methods. Experiments on multiple benchmarks demonstrate that our method achieves similar model robustness as the original TRADES method while using much less training time (~3x improvement with the same training schedule).
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Brint, Steve, Michael Jacewicz, Marika Kiessling, Jody Tanabe, and William Pulsinelli. "Focal Brain Ischemia in the Rat: Methods for Reproducible Neocortical Infarction Using Tandem Occlusion of the Distal Middle Cerebral and Ipsilateral Common Carotid Arteries." Journal of Cerebral Blood Flow & Metabolism 8, no. 4 (1988): 474–85. http://dx.doi.org/10.1038/jcbfm.1988.88.
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This article describes a 3-year experience with focal neocortical ischemia in three rat strains. Multiple groups of adult Wistar (n = 50), Fisher 344 (n = 31), and spontaneously hypertensive (n = 72) rats were subjected to permanent occlusion of the distal middle cerebral (MCA) and ipsilateral common carotid arteries (CCA). Twenty-four hours later the animals were killed, and frozen brain sections were stained with hematoxylin and eosin to demarcate infarcted tissue. The infarct volume for each section was quantified with an image analyzer, and the total infarct volume was calculated with an iterative program that summed all interval volumes. Neocortical infarct volume was the largest and most reproducible in the spontaneously hypertensive rats (SHR). Statistical power analysis to project the numbers of animals necessary to detect a 25 or 50% change in infarct volume with α = 0.05 and β = 0.2 revealed that only the SHR model was practical in terms of requisite animals: i.e., <10 animals per group. Tandem occlusion of the distal MCA and ipsilateral CCA in the SHR strain provides a surgically simple method for causing large neocortical infarcts with reproducible topography and volume. The interanimal variability in infarct volume that occurs even in the SHR strain dictates that randomized, concomitant controls are necessary in each study to ensure the accurate assessment of experimental manipulations or pharmacologic therapies.
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Gautam, Mahesh, Aziz Ullah, and Manish Raj Pathak. "Low Dose Computed Tomography in Diagnosis of Ureteric Calculus in Obese Patients." Journal of Nobel Medical College 9, no. 1 (2020): 27–31. http://dx.doi.org/10.3126/jonmc.v9i1.29464.
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Background: Standard dose computed tomography is standard imaging modality in diagnosis of urolithiasis. The introduction of low dose techniques results in decrease radiation dose without significant change in image quality. However, the image quality of low dose computed tomography is affected by skin fold thickness and subcutaneous abdominal adipose tissue. The aim of this study to evaluate stone location, size, and density using low dose computed tomography compared with standard dose computed tomography in obese population.
 Material and Methods: This non-randomized non-inferiority trial includes 120 patient having BMI≥25kg/m2 with acute ureteric colic. The low dose and standard dose computed tomography were performed accordingly. Effective radiation doses were calculated from dose-length product obtained from scan report using conversion factor of 0.015. The images were reconstructed using iterative reconstruction algorithm. Effective dose, number and size of stone, Hounsfield Unit value of stone and image quality was assessed.
 Results: Stones were located in 69 (57.5%) in right and 51 (42.5%) in left ureter. There was no statistical difference in mean diameter, number and density of stones in low dose as compared with standard dose. The radiation dose was significantly lower with low dose. (3.68 mSv) The delineation of the ureter, outline of the stones and image quality in low dose was overall sufficient for diagnosis. No images of low dose scan were subjectively rated as non-diagnostics.
 Conclusion: Low dose computed tomography with iterative reconstruction technique is as effective as standard dose in diagnosis of ureteric stones in obese patients with lower effective radiation dose.
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de la Torre, Angel, Joaquin T. Valderrama, Jose C. Segura, Isaac M. Alvarez, and Jesus Garcia-Miranda. "Subspace-constrained deconvolution of auditory evoked potentials." Journal of the Acoustical Society of America 151, no. 6 (2022): 3745–57. http://dx.doi.org/10.1121/10.0011423.
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Auditory evoked potentials can be estimated by synchronous averaging when the responses to the individual stimuli are not overlapped. However, when the response duration exceeds the inter-stimulus interval, a deconvolution procedure is necessary to obtain the transient response. The iterative randomized stimulation and averaging and the equivalent randomized stimulation with least squares deconvolution have been proven to be flexible and efficient methods for deconvolving the evoked potentials, with minimum restrictions in the design of stimulation sequences. Recently, a latency-dependent filtering and down-sampling (LDFDS) methodology was proposed for optimal filtering and dimensionality reduction, which is particularly useful when the evoked potentials involve the complete auditory pathway response (i.e., from the cochlea to the auditory cortex). In this case, the number of samples required to accurately represent the evoked potentials can be reduced from several thousand (with conventional sampling) to around 120. In this article, we propose to perform the deconvolution in the reduced representation space defined by LDFDS and present the mathematical foundation of the subspace-constrained deconvolution. Under the assumption that the evoked response is appropriately represented in the reduced representation space, the proposed deconvolution provides an optimal least squares estimation of the evoked response. Additionally, the dimensionality reduction provides a substantial reduction of the computational cost associated with the deconvolution. matlab/Octave code implementing the proposed procedures is included as supplementary material.
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Koch, Sabine, Kim M. Unertl, Christoph U. Lehmann, and Kevin R. Dufendach. "A Randomized Trial Comparing Classical Participatory Design to VandAID, an Interactive CrowdSourcing Platform to Facilitate User-centered Design." Methods of Information in Medicine 56, no. 05 (2017): 344–49. http://dx.doi.org/10.3414/me16-01-0098.
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Summary Background: Early involvement of stakeholders in the design of medical software is particularly important due to the need to incorporate complex knowledge and actions associated with clinical work. Standard user-centered design methods include focus groups and participatory design sessions with individual stakeholders, which generally limit user involvement to a small number of individuals due to the significant time investments from designers and end users. Objectives: The goal of this project was to reduce the effort for end users to participate in co-design of a software user interface by developing an interactive web-based crowd- sourcing platform. Methods: In a randomized trial, we compared a new web-based crowdsourcing platform to standard participatory design sessions. We developed an interactive, modular platform that allows responsive remote customization and design feedback on a visual user interface based on user preferences. The responsive canvas is a dynamic HTML template that responds in real time to user preference selections. Upon completion, the design team can view the user’s interface creations through an administrator portal and download the structured selections through a REDCap interface. Results: We have created a software platform that allows users to customize a user interface and see the results of that customization in real time, receiving immediate feedback on the impact of their design choices. Neonatal clinicians used the new platform to successfully design and customize a neonatal handoff tool. They received no specific instruction and yet were able to use the software easily and reported high usability. Conclusions: VandAID, a new web-based crowdsourcing platform, can involve multiple users in user-centered design simultaneously and provides means of obtaining design feedback remotely. The software can provide design feedback at any stage in the design process, but it will be of greatest utility for specifying user requirements and evaluating iterative designs with multiple options.
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Salehi, Bahram, Sina Jarahizadeh, and Amin Sarafraz. "An Improved RANSAC Outlier Rejection Method for UAV-Derived Point Cloud." Remote Sensing 14, no. 19 (2022): 4917. http://dx.doi.org/10.3390/rs14194917.
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A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many algorithms, including the well-known randomized random sample consensus (RANSAC)-based matching, have been developed focusing on the reduction of outliers. RANSAC-based methods, however, have limitations such as increased false positive rates of outliers, and, consequently resulting in fewer inliers, an unnecessary high number of iterations, and high computational time. Such deficiencies possibly result from the random sampling process, the presence of noise, and incorrect assumptions of the initial values. This paper proposes a modified version of RANSAC-based methods, called Empowered Locally Iterative SAmple Consensus (ELISAC). ELISAC improves RANSAC by utilizing three basic modifications individually or in combination. These three modifications are (a) to increase the stability and number of inliers using two Locally Iterative Least Squares (LILS) loops (Basic LILS and Aggregated-LILS), based on the new inliers in each loop, (b) to improve the convergence rate and consequently reduce the number of iterations using a similarity termination criterion, and (c) to remove any possible outliers at the end of the processing loop and increase the reliability of results using a post-processing procedure. In order to validate our proposed method, a comprehensive experimental analysis has been done on two datasets. The first dataset contains the commonly-used computer vision image pairs on which the state-of-the-art RANSAC-based methods have been evaluated. The second dataset image pairs were captured by a drone over a forested area with various rotations, scales, and baselines (from short to wide). The results show that ELISAC finds more inliers with a faster speed (lower computational time) and lower error (outlier) rates compared to M-estimator SAmple Consensus (MSAC). This makes ELISAC an effective approach for image matching and, consequently, for 3D information extraction of very high and super high-resolution imagery acquired by space-borne, airborne, or UAV sensors. In particular, for applications such as forest 3D modeling and tree height estimations where standard matching algorithms are problematic due to spectral and textural similarity of objects (e.g., trees) on image pairs, ELISAC can significantly outperform the standard matching algorithms.
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Abrams, Keith R., Nicholas Latimer, Mayur Amonkar, Ceilidh Stapelkamp, and Michelle Casey. "Adjusting for treatment crossover in the METRIC metastatic melanoma (MM) trial for trametinib: Preliminary analysis." Journal of Clinical Oncology 31, no. 15_suppl (2013): 9040. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.9040.
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9040 Background: In METRIC, a randomized phase III study, trametinib significantly improved PFS (hazard ratio [HR]=0.44 [95% CI 0.31–0.64; p<0.001]) vs chemotherapy (chemo) in patients (pts) with BRAF V600E+ MM and no brain metastases. Median overall survival (OS), a secondary endpoint, has not yet been reached. OS results are likely to underestimate the effect of trametinib as pts progressing on chemo could cross over to experimental treatment (trt). This analysis attempts to adjust for confounding effects of trt crossover on OS in the overall population and first line (1L) subgroup using current METRIC results. Methods: Randomization-based crossover adjustment methods – Rank Preserving Structural Failure Time Models (RPSFTM) and the Iterative Parameter Estimation (IPE) algorithm – were used. We conducted two sets of analyses testing different assumptions regarding the durability of the trt effect. “Trt group” analyses adjusted for crossover under the assumption that the trt effect is maintained until death regardless of trt duration; “On trt – observed” analyses adjusted for crossover under the assumption that the trt effect disappears upon trt discontinuation. Results are presented as HRs. Results: 178 and 95 MM pts were randomized to trametinib and chemo, respectively; 49.5% of chemo pts crossed over to trametinib as of data cut off (Oct. 2011). Median follow-up was 4.9 months and 19.8% deaths occurred across both arms. Crossover adjustment results are presented in the Table. Conclusions: RPSFTM and IPE “trt group” analyses resulted in OS HR point estimates that represented greater trt effects in the overall population and 1L subgroup compared to the unadjusted HRs. Results are exploratory because few deaths have been observed in the current dataset. Future analyses on a mature dataset should produce more robust estimates of the OS trt effect after crossover adjustment. [Table: see text]
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Agarwal, Arnav, John Basmaji, Shannon M. Fernando, et al. "Administration of Parenteral Vitamin C in Patients With Severe Infection: Protocol for a Systematic Review and Meta-analysis." JMIR Research Protocols 11, no. 1 (2022): e33989. http://dx.doi.org/10.2196/33989.
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Background Severe infections are characterized by inflammation and oxidative damage. Ascorbic acid (vitamin C) administration may attenuate oxidative damage and, in turn, reduce vascular endothelial injury in pulmonary and systemic vasculature. Objective We aim to describe a protocol for a living systematic review that will evaluate the effectiveness and safety of parenteral vitamin C administration in adults with severe infections, including those with COVID-19. Methods We searched Ovid MEDLINE, Embase, CINAHL, the Centers for Disease Control and Prevention COVID-19 database, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to March 30, 2021, for randomized controlled trials evaluating parenteral vitamin C versus no parenteral vitamin C in hospitalized adults with severe infection. Eligible studies will include at least 1 arm involving any dose of parenteral vitamin C alone or in combination with other cointerventions and at least 1 arm not involving parenteral vitamin C. The primary outcomes of interest will include in-hospital, 30-day, and 90-day mortality. Title and abstract screening, full-text screening, data extraction, and risk of bias evaluation via a modified Risk of Bias 2.0 tool will be conducted independently and in pairs. We will perform random effects modeling for meta-analyses, in which study weights will be generated by using the inverse variance method. We will assess certainty in effect estimates by using the Grading of Recommendations Assessment, Development and Evaluation methodology. Meta-analyses will be updated iteratively as new trial evidence becomes available. Results Among the 1386 citations identified as of March 30, 2021, a total of 17 eligible randomized controlled trials have been identified as of September 2021. We are in the process of updating the search strategy and associated data analyses. Conclusions The results will be of importance to critical care physicians and hospitalists who manage severe infection and COVID-19 in daily practice, and they may directly inform international clinical guidance. Although our systematic review will incorporate the most recent trial evidence, ongoing trials may change our confidence in the estimates of effects, thereby necessitating iterative updates in the form of a living review. Trial Registration PROSPERO CRD42020209187; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=209187 International Registered Report Identifier (IRRID) RR1-10.2196/33989
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Riddle, Sarah W., Susan N. Sherman, Margo J. Moore, et al. "A Qualitative Study of Increased Pediatric Reutilization After a Postdischarge Home Nurse Visit." Journal of Hospital Medicine 15, no. 9 (2020): 518–25. http://dx.doi.org/10.12788/jhm.3370.
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BACKGROUND: The Hospital to Home Outcomes (H2O) trial was a 2-arm, randomized controlled trial that assessed the effects of a nurse home visit after a pediatric hospital discharge. Children randomized to the intervention had higher 30-day postdischarge reutilization rates compared with those with standard discharge. We sought to understand perspectives on why postdischarge home nurse visits resulted in higher reutilization rates and to elicit suggestions on how to improve future interventions. METHODS: We sought qualitative input using focus groups and interviews from stakeholder groups: parents, primary care physicians (PCP), hospital medicine physicians, and home care registered nurses (RNs). A multidisciplinary team coded and analyzed transcripts using an inductive, iterative approach. RESULTS: Thirty-three parents participated in interviews. Three focus groups were completed with PCPs (n = 7), 2 with hospital medicine physicians (n = 12), and 2 with RNs (n = 10). Major themes in the explanation of increased reutilization included: appropriateness of patient reutilization; impact of red flags/warning sign instructions on family’s reutilization decisions; hospital-affiliated RNs “directing traffic” back to hospital; and home visit RNs had a low threshold for escalating care. Major themes for improving design of the intervention included: need for improved postdischarge communication; individualizing home visits—one size does not fit all; and providing context and framing of red flags. CONCLUSION: Stakeholders questioned whether hospital reutilization was appropriate and whether the intervention unintentionally directed patients back to the hospital. Future interventions could individualize the visit to specific needs or diagnoses, enhance postdischarge communication, and better connect patients and home nurses to primary care.
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Alfonsi, Jeffrey E., Elizabeth E. Y. Choi, Taha Arshad, et al. "Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial." JMIR mHealth and uHealth 8, no. 10 (2020): e22074. http://dx.doi.org/10.2196/22074.
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Background Carbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates. Objective Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy. Methods Iterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA1c level was also assessed. Results Use of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA1c levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study. Conclusions Our results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management. Trial Registration ClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142
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Krishnamurti, Lakshmanan, Diana Ross, Cynthia Sinha, et al. "Comparative Effectiveness of a Web-Based Patient Decision Aid for Therapeutic Options for Sickle Cell Disease: Randomized Controlled Trial." Journal of Medical Internet Research 21, no. 12 (2019): e14462. http://dx.doi.org/10.2196/14462.
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Background Hydroxyurea, chronic blood transfusions, and bone marrow transplantation are efficacious, disease-modifying therapies for sickle cell disease but involve complex risk-benefit trade-offs and decisional dilemma compounded by the lack of comparative studies. A patient decision aid can inform patients about their treatment options, the associated risks and benefits, help them clarify their values, and allow them to participate in medical decision making. Objective The objective of this study was to develop a literacy-sensitive Web-based patient decision aid based on the Ottawa decision support framework, and through a randomized clinical trial estimate the effectiveness of the patient decision aid in improving patient knowledge and their involvement in decision making. Methods We conducted population decisional needs assessments in a nationwide sample of patients, caregivers, community advocates, policy makers, and health care providers using qualitative interviews to identify decisional conflict, knowledge and expectations, values, support and resources, decision types, timing, stages and learning, and personal clinical characteristics. Interview transcripts were coded using QSR NVivo 10. Alpha testing of the patient decision aid prototype was done to establish usability and the accuracy of the information it conveyed, and then was followed by iterative cycles of beta testing. We conducted a randomized clinical trial of adults and of caregivers of pediatric patients to evaluate the efficacy of the patient decision aid. Results In a decisional needs assessment, 223 stakeholders described their preferences, helping to guide the development of the patient decision aid, which then underwent alpha testing by 30 patients and 38 health care providers and iterative cycles of beta testing by 87 stakeholders. In a randomized clinical trial, 120 participants were assigned to either the patient decision aid or standard care (SC) arm. Qualitative interviews revealed high levels of usability, acceptability, and utility of the patient decision aid in education, values clarification, and preparation for decision making. On the acceptability survey, 72% (86/120) of participants rated the patient decision aid as good or excellent. Participants on the patient decision aid arm compared to the SC arm demonstrated a statistically significant improvement in decisional self-efficacy (P=.05) and a reduction in the informed sub-score of decisional conflict (P=.003) at 3 months, with an improvement in preparation for decision making (P<.001) at 6 months. However, there was no improvement in terms of the change in knowledge, the total or other domain scores of decisional conflicts, or decisional self-efficacies at 6 months. The large amount of missing data from survey completion limited our ability to draw conclusions about the effectiveness of the patient decision aid. The patient decision aid met 61 of 62 benchmarks of the international patient decision aid collaboration standards for content, development process, and efficacy. Conclusions We have developed a patient decision aid for sickle cell disease with extensive input from stakeholders and in a randomized clinical trial demonstrated its acceptability and utility in education and decision making. We were unable to demonstrate its effectiveness in improving patient knowledge and involvement in decision making. Trial Registration ClinicalTrials.gov NCT03224429; https://clinicaltrials.gov/ct2/show/NCT03224429 and ClinicalTrials.gov NCT02326597; https://clinicaltrials.gov/ct2/show/NCT02326597
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Sun, Guangling, Yuying Su, Chuan Qin, Wenbo Xu, Xiaofeng Lu, and Andrzej Ceglowski. "Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples." Mathematical Problems in Engineering 2020 (May 11, 2020): 1–17. http://dx.doi.org/10.1155/2020/8319249.
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Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigations have increasingly shown DNNs to be highly vulnerable when adversarial examples are used as input. Here, we present a comprehensive defense framework to protect DNNs against adversarial examples. First, we present statistical and minor alteration detectors to filter out adversarial examples contaminated by noticeable and unnoticeable perturbations, respectively. Then, we ensemble the detectors, a deep Residual Generative Network (ResGN), and an adversarially trained targeted network, to construct a complete defense framework. In this framework, the ResGN is our previously proposed network which is used to remove adversarial perturbations, and the adversarially trained targeted network is a network that is learned through adversarial training. Specifically, once the detectors determine an input example to be adversarial, it is cleaned by ResGN and then classified by the adversarially trained targeted network; otherwise, it is directly classified by this network. We empirically evaluate the proposed complete defense on ImageNet dataset. The results confirm the robustness against current representative attacking methods including fast gradient sign method, randomized fast gradient sign method, basic iterative method, universal adversarial perturbations, DeepFool method, and Carlini & Wagner method.
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Young, E., R. Aiyadurai, U. Cellupica, et al. "The Generalizability of the Paediatric Developmental Passport: A Multi-Site Randomized Controlled Trial." Paediatrics & Child Health 21, Supplement_5 (2016): e67a-e67a. http://dx.doi.org/10.1093/pch/21.supp5.e67a.
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Abstract BACKGROUND: The Pediatric Developmental Passport (passport) is an innovative tracking tool for families of children with autism spectrum disorder (ASD). It provides a mechanism for clearly communicating appropriate regional developmental services, an opportunity track progress in accessing these developmental services and a valuable summary of the developmental care received by that child for pediatrician. A qualitative study with parents and health professionals (developmental pediatricians, developmental nurses, pediatricians) lead to the design and iterative review of the passport. OBJECTIVES: The objective of this study was to determine the general-izability and effectiveness of the passport compared to placebo in a multi-site pragmatic randomized control trial. DESIGN/METHODS: A pragmatic multi-site randomized controlled trial was conducted with families of children between 0-6 years of age diagnosed with ASD. Families from two different models of developmental care were enrolled into the study. One site was a sub-urban developmental consultation clinic and the second site was a shared-care model between developmental pediatricians and general pediatricians in an urban resource restricted area. All families included in the study were randomized to receive the passport or placebo (blank card). Agencies providing Autism specific behaviour therapy (ABA) within each site were contacted directly to obtain accurate contact and access status of recommended developmental services. To determine passport effectivenes a pearson’s chi square test was conducted using a significant p value of &lt;0.05. RESULTS: Forty children with ASD were included and followed in this study. The passport proved to be significantly more effective in aiding families to contact developmental services than the placebo (blank card). A significantly larger portion of families (90.5%) with the passport contacted agencies for ABA (applied behaviour analysis) therapy compared to families with the placebo (61.9%, (p value significant at &lt;0.05). More families with the passport tended to contact ABA in less than 2 months (48%) than the placebo group (35%), but this was not statistically significant. CONCLUSION: The pediatric developmental passport enables families of children newly diagnosed with Autism to contact necessary behavioural services more often than those who did not receive the passport after diagnosis.
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Samuel, P., J. Park, F. Muckle, et al. "P036: A comprehensive quality improvement initiative to prevent falls in the emergency department." CJEM 19, S1 (2017): S90. http://dx.doi.org/10.1017/cem.2017.238.
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Introduction: Patients from all population groups visit the emergency department (ED), with increasing visits by elderly patients. Patient falls in the ED are a significant safety concern, and they can lead to serious injuries and worse outcomes. Toronto Western Hospital’s ED Quality Improvement (QI) team identified as a problem our assessment and management of patients at risk for falls. The aim of this project was to develop a comprehensive and standardized approach to patients at risk of falls in the ED, including implementing timely interventions for fall prevention. Methods: A literature review of existing tools was completed to develop our own reliable and valid fall risk screening tool for ED patients. QI methods were used to devise a comprehensive strategy starting with detection at triage and implementation of action-driven steps at the bedside, through multiple PDSA cycles, randomized audits, surveys, and education. Repeated measurements were undergone throughout the project, as were staff satisfaction surveys. Results: The chart audits showed a five-fold increase in the completion rate of the fall risk screening tool in the ED by the end of the QI initiative (from 10% to 50%). Constructive feedback by an engaged team of nurses was used to iteratively improve the tool, and there was mostly positive feedback on it after various PDSA cycles were completed. The various component of this novel and useful ED-based falls screening tool and bundle will be presented in tables and figures for other leaders to replicate in their EDs. Conclusion: We developed a completely new ED-specific fall risk screening tool through literature review, front-line provider feedback, and iterative PDSA cycles. It was used for the identification, prevention, and management of ED patients with fall risk. We also contributed to a positive change in the culture of a busy ED environment towards the promotion of patient safety. Education and feedback continue to be provided to the ED nurses for reflective practice, and we hope to continue to improve our tool and to share it with other EDs.
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Bhat, Amritha, B. Ramakrishna Goud, Bharat Kalidindi, et al. "Mobile Mental Health in Women’s Community-Based Organizations: Protocol for a Pilot Randomized Controlled Trial." JMIR Research Protocols 12 (February 8, 2023): e42919. http://dx.doi.org/10.2196/42919.
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Background Of every 10 women in rural India, 1 suffers from a common mental disorder such as depression, and untreated depression is associated with significant morbidity and mortality. Several factors lead to a large treatment gap, specifically for women in rural India, including stigma, lack of provider mental health workforce, and travel times. There is an urgent need to improve the rates of detection and treatment of depression among women in rural India without overburdening the scarce mental health resources. Objective We propose to develop, test, and deploy a mental health app, MITHRA (Multiuser Interactive Health Response Application), for depression screening and brief intervention, designed for use in women’s self-help groups (SHGs) in rural India. Methods We will use focus groups with SHG members and community health workers to guide the initial development of the app, followed by iterative modification based on input from a participatory design group consisting of proposed end users of the app (SHG members). The final version of the app will then be deployed for testing in a pilot cluster randomized trial, with 3 SHGs randomized to receive the app and 3 to receive enhanced care as usual. Results This study was funded in June 2021. As of September 2022, we have completed both focus groups, 1 participatory design group, and app development. Conclusions Delivering app-based depression screening and treatment in community settings such as SHGs can address stigma and transportation-related barriers to access to depression care and overcome cultural and contextual barriers to mobile health use. It can also address the mental health workforce shortage. If we find that the MITHRA approach is feasible, we will test the implementation and effectiveness of MITHRA in multiple SHGs across India in a larger randomized controlled trial. This approach of leveraging community-based organizations to improve the reach of depression screening and treatment is applicable in rural and underserved areas across the globe. International Registered Report Identifier (IRRID) DERR1-10.2196/42919
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Nardell, Maria F., Siyaxolisa Sindelo, Elzette Rousseau, et al. "Development of “Yima Nkqo,” a community-based, peer group intervention to support treatment initiation for young adults with HIV in South Africa." PLOS ONE 18, no. 6 (2023): e0280895. http://dx.doi.org/10.1371/journal.pone.0280895.
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Aims Half of young adults diagnosed with HIV in South Africa start antiretroviral therapy (ART). We developed and field tested a facilitator-guided peer support group called Yima Nkqo (“Standing Tall” in isiXhosa) to promote treatment initiation for young adults newly diagnosed with HIV in communities around Cape Town. Methods Following an adapted version of the UK Medical Research Council’s framework for developing complex interventions, we 1) identified evidence on previous interventions to improve ART uptake in sub-Saharan Africa; 2) collected and analyzed qualitative data on the acceptability of our proposed intervention; 3) proposed a theoretical understanding of the process of behavior change; and 4) developed an intervention manual and feedback tools. During field-testing, participant feedback on intervention acceptability, and team feedback on consistency of content delivery and facilitation quality, were analyzed using an iterative, rapid-feedback evaluation approach. In-depth written and verbal summaries were shared in weekly team meetings. Team members interpreted feedback, identified areas for improvement, and proposed suggestions for intervention modifications. Results Based on our formative research, we developed three, 90-minute sessions with content including HIV and ART education, reflection on personal resources and strengths, practice disclosing one’s status, strategies to overcome stressors, and goal setting to start treatment. A lay facilitator was trained to deliver intervention content. Two field testing groups (five and four participants, respectively) completed the intervention. Participants highlighted that strengths of Yima Nkqo included peer support, motivation, and education about HIV and ART. Team feedback to the facilitator ensured optimal consistency of intervention content delivery. Conclusions Iteratively developed in collaboration with youth and healthcare providers, Yima Nkqo is a promising new intervention to improve treatment uptake among young adults with HIV in South Africa. The next phase will be a pilot randomized controlled trial of Yima Nkqo (ClinicalTrials.gov Identifier: NCT04568460).
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Vallejo-Torres, Laura, Lotte Steuten, Bonny Parkinson, Alan J. Girling, and Martin J. Buxton. "Integrating Health Economics Into the Product Development Cycle." Medical Decision Making 31, no. 4 (2010): 596–610. http://dx.doi.org/10.1177/0272989x10388041.
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Background. The probability of reimbursement is a key factor in determining whether to proceed with or abandon a product during its development. The purpose of this article is to illustrate how the methods of iterative Bayesian economic evaluation proposed in the literature can be incorporated into the development process of new medical devices, adapting them to face the relative scarcity of data and time that characterizes the process. Methods. A 3-stage economic evaluation was applied: an early phase in which simple methods allow for a quick prioritization of competing products; a mid-stage in which developers synthesize the data into a decision model, identify the parameters for which more information is most valuable, and explore uncertainty; and a late stage, in which all relevant information is synthesized. A retrospective analysis was conducted of the case study of absorbable pins, compared with metallic fixation, in osteotomy to treat hallux valgus. Results. The results from the early analysis suggest absorbable pins to be cost-effective under the beliefs and assumptions applied. The outputs from the models at the mid-stage analyses show the device to be cost-effective with a high probability. Late-stage analysis synthesizes evidence from a randomized controlled trial and informative priors, which are based on previous evidence. It also suggests that absorbable pins are the most cost-effective strategy, although the uncertainty in the model output increased considerably. Conclusions. This example illustrates how the method proposed allows decisions in the product development cycle to be based on the best knowledge that is available at each stage.
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O’Connor, Ashley, Amy Ladebue, Jamie Peterson, et al. "Creating and testing regulatory focus messages to enhance medication adherence." Chronic Illness 15, no. 2 (2018): 124–37. http://dx.doi.org/10.1177/1742395317753882.
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Objectives Strategies were explored to improve patient adherence to cardioprotective medications by borrowing from a motivational framework used in psychology, regulatory focus theory. The current study is part of a larger randomized control trial and was aimed at understanding what written educational messages, based on patients’ regulatory focus tendency, resonated with each individual as a potential reminder to take medications. This study was also aimed at understanding why messages resonated with the patients. Methods Twenty veterans were tested for regulatory fitand presented with messages dependent on focus tendency. In-person semi-structured interviews were conducted to collect feedback of messages. An iterative analysis drawing primarily on matrix and reflexive team analyses was conducted. Result Six promotion and six prevention messages emerged, such as “team up with your provider to create a combination of medications to prevent illness” and “Live your best life – Take your medications”. Five themes related to types of health messages that spoke to patients’ regulatory fit were discovered: relatability; empowerment and control; philosophy on life; relationship with provider and medications; and vocabulary effect on the impact of messages. Discussion Motivational messages based on regulatory fit may be useful in improving patient medication adherence, leading to improved cardiovascular outcomes.
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Gaume, Jacques, Véronique S. Grazioli, Sophie Paroz, Cristiana Fortini, Nicolas Bertholet, and Jean-Bernard Daeppen. "Developing a brief motivational intervention for young adults admitted with alcohol intoxication in the emergency department – Results from an iterative qualitative design." PLOS ONE 16, no. 2 (2021): e0246652. http://dx.doi.org/10.1371/journal.pone.0246652.
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Background Unhealthy alcohol use among young adults is a major public health concern. Brief motivational interventions for young adults in the Emergency Department (ED) have shown promising but inconsistent results. Methods Based on the literature on brief intervention and motivational interviewing efficacy and active ingredients, we developed a new motivational intervention model for young adults admitted in the ED with alcohol intoxication. Using an iterative qualitative design, we first pre-tested this model by conducting 4 experimental sessions and 8 related semi-structured interviews to evaluate clinicians’ and patients’ perceptions of the intervention’s acceptability and feasibility. We then conducted a consultation meeting with 9 international experts using a nominal group technique. The intervention model was adjusted and finally re-tested by conducting 6 new experimental sessions and 12 related semi-structured interviews. At each round, data collected were analyzed and discussed, and the intervention model updated accordingly. Results Based on the literature, we found 6 axes for developing a new model: High level of relational factors (e.g. empathy, alliance, avoidance of confrontation); Personalized feedback; Enhance discrepancy; Evoke change talk while softening sustain talk, strengthen ability and commitment to change; Completion of a change plan; Devote more time: longer sessions and follow-up options (face-to-face, telephone, or electronic boosters; referral to treatment). A qualitative analysis of the semi-structured interviews gave important insights regarding acceptability and feasibility of the model. Adjustments were made around which information to provide and how, as well as on how to deepen discussion about change with patients having low levels of self-exploration. The experts’ consultation addressed numerous points, such as information and advice giving, and booster interventions. Discussion This iterative, multi-component design resulted in the development of an intervention model embedded in recent research findings and theory advances, as well as feasible in a complex environment. The next step is a randomized controlled trial testing the efficacy of this model.
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Jahani, Nazanin, Sergey Alyaev, Joaquín Ambía, Kristian Fossum, Erich Suter, and Carlos Torres-Verdín. "Enhancing the Detectability of Deep-Sensing Borehole Electromagnetic Instruments by Joint Inversion of Multiple Logs Within a Probabilistic Geosteering Workflow." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 64, no. 1 (2023): 80–91. http://dx.doi.org/10.30632/pjv64n1-2023a6.
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The cost of drilling wells on the Norwegian Continental Shelf is exceptionally high, and hydrocarbon reservoirs are often located in spatially complex rock formations. Optimized well placement with real-time geosteering is crucial to efficiently produce from such reservoirs and reduce exploration and development costs. Geosteering is commonly assisted by repeated formation evaluation based on the interpretation of well logs while drilling. Thus, reliable, computationally efficient, and robust workflows that can interpret well logs and capture uncertainties in real time are necessary for successful well placement. We present a formation evaluation workflow for geosteering that implements an iterative version of an ensemble-based method, namely the approximate Levenberg-Marquardt form of the Ensemble Randomized Maximum Likelihood (LM-EnRML). The workflow jointly estimates the petrophysical and geological model parameters and their uncertainties. This paper demonstrates joint estimation of layer-by-layer water saturation, porosity, and layer-boundary locations and inference of layers’ resistivities and densities. The parameters are estimated by minimizing the statistical misfit between the simulated and the observed measurements for several logs on different scales simultaneously (i.e., shallow-sensing nuclear density and shallow to extra-deep electromagnetic (EM) logs). Numerical experiments performed on a synthetic example verified that the iterative ensemble-based method could estimate multiple petrophysical parameters and decrease their uncertainties in a fraction of the time compared to classical Monte Carlo methods. Extra-deep EM measurements provide the best reliable information for geosteering, and we show that they can be interpreted within the proposed workflow. However, we also observe that the parameter uncertainties noticeably decrease when deep-sensing EM logs are combined with shallow-sensing nuclear density logs. Importantly, the estimation quality increases not only in the proximity of the shallow tool but also extends to the look ahead of the extra-deep EM capabilities. We specifically quantify how shallow data can lead to significant uncertainty reduction of the boundary positions ahead of the bit, which is crucial for geosteering decisions and reservoir mapping.
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Hall, Daniel L., Gloria Y. Yeh, Conall O'Cleirigh, et al. "A Multi-step Approach to Adapting a Mind-Body Resiliency Intervention for Fear of Cancer Recurrence and Uncertainty in Survivorship (IN FOCUS)." Global Advances in Health and Medicine 11 (January 2022): 216495612210746. http://dx.doi.org/10.1177/21649561221074690.
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Background For cancer survivors, there is a paucity of fear of recurrence (FOR) interventions that integrate empirically supported mind-body and psychological skills for managing FOR and are delivered in scalable formats. Objective To adapt an evidence-based resiliency intervention to address FOR among cancer survivors. Methods A multidisciplinary team of researchers, clinicians, and patient stakeholders followed an iterative intervention adaptation process (ORBIT). In Step 1, we sought to define key FOR management skills through a literature review and feedback from stakeholders. In Step 2, we integrated findings into a treatment manual and refined procedures for in-person delivery to groups of cancer survivors, defined as adults who had completed primary cancer treatment for non-metastatic cancer. In Step 3, we conducted a single arm trial to assess initial acceptability and change in FOR severity with 23 cancer survivors (N=4 intervention groups). In Step 4, we conducted additional qualitative interviews with 28 cancer survivors (N=6 focus groups stratified by FOR severity, N=15 individual interviews) to define adaptive and maladaptive strategies for coping with FOR and to identify preferences for delivery. In Step 5, we refined the treatment manual and procedures for testing in a future pilot randomized feasibility trial. Results We identified critical feedback using a combination of qualitative and quantitative methods. The single arm trial suggested preliminary feasibility and sustained reductions in FOR severity, yet need for refinement (i.e., eligibility, delivery modality), prompting additional qualitative interviews for further targeting. The resulting intervention (IN FOCUS) is comprised of virtual, synchronous, group-delivered sessions that offer an integrated approach to FOR management by teaching cognitive-behavioral techniques, meditation, relaxation training, adaptive health behaviors, and positive psychology skills. Sessions are targeted by applying skills to FOR and associated healthcare engagement. Conclusions IN FOCUS is a targeted intervention for teaching mind-body resiliency skills to groups of cancer survivors with elevated FOR. Next steps are testing feasibility in a pilot randomized trial.
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Demetri, George D., Peter Reichardt, Yoon-Koo Kang, et al. "Final overall survival (OS) analysis with modeling of crossover impact in the phase III GRID trial of regorafenib vs placebo in advanced gastrointestinal stromal tumors (GIST)." Journal of Clinical Oncology 34, no. 4_suppl (2016): 156. http://dx.doi.org/10.1200/jco.2016.34.4_suppl.156.
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156 Background: GRID showed that regorafenib (REG) improves progression-free survival (PFS; primary endpoint) vs placebo (PBO) in patients (pts) with advanced GIST after failure of at least imatinib and sunitinib (HR 0.27; 1-sided p < 0.0001). An interim OS analysis at the time of the primary PFS analysis showed a positive trend (HR 0.77; p = 0.199) despite 85% of PBO pts crossing over to REG. An exploratory analysis modeling the impact of crossover on OS suggested a benefit for REG. We present exploratory analyses of OS comparing crossover correction results at different times, including at the planned final OS analysis. Methods: Data cut-off dates for OS analyses were 26 Jan 2012 (final PFS analysis), 31 Jan 2014, and 8 Jun 2015 (final OS analysis). The impact of crossover on OS was modeled using 2 randomization-based methods: rank preserving structural failure time (RPSFT) and iterative parameter estimation (IPE), both considered best choice among correction analytics. Hazard ratios (HRs) and 95% CIs were derived using the Cox model. Results: Pts were randomized (2:1) to REG (n = 133) or PBO (n = 66). Data maturity in terms of deaths of randomized pts was 23%, 70%, and 81%. At data cut-off for the final OS analysis, a total of 162 deaths occurred: REG 109 (82%) and PBO 53 (80%); 7 pts remained on treatment. Compared to the 2012 OS analysis, continued crossover treatment further diluted the treatment effect, evidenced by the increasing HR in the ITT analysis at later times (Table). Conclusions: IPE correction provides more stable estimated median times and HRs than RPSFT. These exploratory analyses modeling the impact of crossover to active drug suggest that REG has a greater OS benefit than noted in the ITT analysis. Such sensitivity analytics are needed to understand fully the benefits of active drugs in crossover trials. Clinical trial information: NCT01271712. [Table: see text]
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Peloquin, C. A., G. S. Jaresko, C. L. Yong, A. C. Keung, A. E. Bulpitt, and R. W. Jelliffe. "Population pharmacokinetic modeling of isoniazid, rifampin, and pyrazinamide." Antimicrobial Agents and Chemotherapy 41, no. 12 (1997): 2670–79. http://dx.doi.org/10.1128/aac.41.12.2670.
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Isoniazid (INH), rifampin (RIF), and pyrazinamide (PZA) are the most important drugs for the treatment of tuberculosis (TB). The pharmacokinetics of all three drugs in the plasma of 24 healthy males were studied as part of a randomized cross-over phase I study of two dosage forms. Subjects ingested single doses of INH at 250 mg, RIF at 600 mg, and PZA at 1,500 mg. Plasma was collected for 36 h and was assayed by high-performance liquid chromatography. The data were analyzed by noncompartmental, iterative two-stage maximum a posteriori probability Bayesian (IT2B) and nonparametric expectation maximization (NPEM) population modeling methods. Fast and slow acetylators of INH had median peak concentrations in plasma (C[max]) of 2.44 and 3.64 microg/ml, respectively, both of which occurred at 1.0 h postdose (time of maximum concentrations of drugs in plasma [T(max)]), with median elimination half-lives (t1/2) of 1.2 and 3.3 h, respectively (by the NPEM method). RIF produced a median C(max) of 11.80 microg/ml, a T(max) of 1.0 h, and a t1/2 of 3.4 h. PZA produced a median C(max) of 28.80 microg/ml, a T(max) of 1.0 h, and a t1/2 of 10.0 h. The pharmacokinetic behaviors of INH, RIF, and PZA were well described by the three methods used. These models can serve as benchmarks for comparison with models for other populations, such as patients with TB or TB with AIDS.
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Christine Kugler, Kari, Amanda E. Tanner, David L. Wyrick, et al. "2526." Journal of Clinical and Translational Science 1, S1 (2017): 82. http://dx.doi.org/10.1017/cts.2017.289.
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OBJECTIVES/SPECIFIC AIMS: The goal of this study is to develop an effective and efficient STI preventive intervention among college students following the principles and phases of MOST. METHODS/STUDY POPULATION As part of the preparation phase, an explicit conceptual model, drawing heavily on theory and prior research, was used to translate the existing science into 5 candidate intervention components (ie, descriptive norms, injunctive norms, expectancies, perceived benefits of protective behavioral strategies, and self-efficacy). For the optimization phase, in Fall 2016 all first-year students (n=3547) from 4 universities were recruited to participate. Students were randomized to 1 of 32 different experimental conditions that included a combination of the candidate intervention components. Component effectiveness was evaluated using data from an immediate post-intervention survey on respective component mediators (eg, alcohol and sex-related descriptive norms). After a second factorial experiment (Fall 2017), only those intervention components that meet the pre-specified criteria of day ≥0.15 will be included in the optimized intervention. The evaluation phase will evaluate the effectiveness of the optimized STI preventive intervention via a randomized-control trial (Fall 2018). RESULTS/ANTICIPATED RESULTS: Preliminary results from the first factorial experiment suggest that descriptive norms and injunctive norms intervention components were significantly effective in reducing post-intervention perceived alcohol prevalence (β=−0.28, p<0.001) and approval of alcohol (β=−0.33, p<0.001), and sex-related norms (β=−0.23, p<.001). These results, in combination with process data, are being used to inform revisions of the intervention components to be included in a second factorial screening experiment. DISCUSSION/SIGNIFICANCE OF IMPACT: This study demonstrates how an iterative approach to engineering an STI preventive intervention using MOST can affect the behaviors of college students and serve as a foundation for other translational science.
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Berube, Lauren, Archana Shrestha, Abha Shrestha, Jean-Francois Daneault, Prabin Shakya, and Shristi Rawal. "Development and Testing of a Mobile Application for Management of Gestational Diabetes: A Qualitative and Randomized Trial Protocol." Current Developments in Nutrition 5, Supplement_2 (2021): 721. http://dx.doi.org/10.1093/cdn/nzab046_018.
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Abstract Objectives The prevalence of gestational diabetes mellitus (GDM) is increasing, particularly in low- and middle-income countries (LMICs), such as Nepal. GDM self-management, including intensive diet/lifestyle modifications and glucose monitoring, is critical to maintain glycemic control and prevent adverse maternal and child outcomes. The objective of this study is to develop a culturally appropriate mobile app that supports self-management of GDM, and additionally, test its usability and preliminary efficacy, among patients in a peri-urban hospital setting in Nepal. Methods Dhulikhel Hospital, a community-based tertiary level university hospital in Dhulikhel, Nepal, is the data collection site for this study. In the app development phase, paper prototypes for the GDM app were developed based on expert review and user-centered design approach. Focus groups and in-depth interviews were conducted with GDM patients (n = 6), healthcare providers (n = 5), and family members of GDM patients (n = 3) in order to understand facilitators and barriers to GDM self-management and to gather feedback on the paper prototypes. The final GDM app will be developed based on the user feedback and following an iterative process of product design and user testing. In the clinical trial phase, newly diagnosed GDM patients (n = 60) will be recruited, and randomized to either GDM app + standard care or standard care alone from 28 weeks gestation until delivery. In this proof-of-concept trial, feasibility outcomes will be app usage, self-monitoring adherence, and app usability and acceptability. Exploratory treatment outcomes will be glycemic control measures at 6 weeks postpartum, neonatal birthweight, and rates of labor induction and caesarean delivery. Results Qualitative data from focus groups and in-depth interviews have been collected and analyzed, and we anticipate that the clinical trial will be completed in 2022. Conclusions App-based lifestyle interventions for GDM management are not common, especially in LMICs where its prevalence is rapidly increasing, and as such, our study findings will have important public health relevance for a broader population. Funding Sources NIH/FIC.
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Juarascio, Adrienne, Paakhi Srivastava, Kelsey Clark, Emily Presseller, Stephanie Manasse, and Evan Forman. "A Clinician-Controlled Just-in-time Adaptive Intervention System (CBT+) Designed to Promote Acquisition and Utilization of Cognitive Behavioral Therapy Skills in Bulimia Nervosa: Development and Preliminary Evaluation Study." JMIR Formative Research 5, no. 5 (2021): e18261. http://dx.doi.org/10.2196/18261.
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Background Cognitive behavioral therapy (CBT) for bulimia nervosa (BN) is most effective when patients demonstrate adequate skill utilization (ie, the frequency with which a patient practices or uses therapeutic skills) and skill acquisition (ie, the ability to successfully perform a skill learned in treatment). However, rates of utilization and acquisition of key treatment skills (eg, regular eating, urge management skills, and mood management skills) by the end of the treatment are frequently low; as a result, outcomes from CBT for BN are affected. Just-in-time adaptive interventions (JITAIs) may improve skill acquisition and utilization by delivering real-time interventions during algorithm-identified opportunities for skill practice. Objective In this manuscript, we describe a newly developed JITAI system called CBT+ that is designed to facilitate the acquisition and utilization of CBT for BN treatment skills when used as a treatment augmentation. We also present feasibility, acceptability, and preliminary outcomes data from a small proof-of-concept pilot trial (n=5 patients and n=3 clinicians) designed to identify opportunities for iterative development of CBT+ ahead of a larger ongoing randomized controlled trial. Methods A total of 5 individuals with BN received 16 sessions of outpatient CBT for BN while using the CBT+ app. Data were collected from patients and clinicians to evaluate the feasibility (eg, app use and user adherence), acceptability (eg, qualitative patient and clinician feedback, including usefulness ratings of CBT+ on a 6-point Likert scale ranging from 1=extremely useless to 6=extremely useful), and preliminary outcomes (eg, improvements in skill utilization and acquisition and BN symptoms) of the CBT+ system. Results Patients reported that CBT+ was a relatively low burden (eg, quick and easy-to-use self-monitoring interface), and adherence to in-app self-monitoring was high (mean entries per day 3.13, SD 1.03). JITAIs were perceived as useful by both patients (median rating 5/6) and clinicians (median rating 5/6) for encouraging the use of CBT skills. Large improvements in CBT skills and clinically significant reductions in BN symptoms were observed post treatment. Although preliminary findings indicated that the CBT+ system was acceptable to most patients and clinicians, the overall study dropout was relatively high (ie, 2/5, 40% patients), which could indicate some moderate concerns regarding feasibility. Conclusions CBT+, the first-ever JITAI system designed to facilitate the acquisition and utilization of CBT for BN treatment skills when used as a treatment augmentation, was shown to be feasible and acceptable. The results indicate that the CBT+ system should be subjected to more rigorous evaluations with larger samples and should be considered for wider implementation if found effective. Areas for iterative improvement of the CBT+ system ahead of a randomized controlled trial are also discussed.
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Dabija, Anca, Marcin Kluczek, Bogdan Zagajewski, et al. "Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping." Remote Sensing 13, no. 4 (2021): 777. http://dx.doi.org/10.3390/rs13040777.
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Land cover information is essential in European Union spatial management, particularly that of invasive species, natural habitats, urbanization, and deforestation; therefore, the need for accurate and objective data and tools is critical. For this purpose, the European Union’s flagship program, the Corine Land Cover (CLC), was created. Intensive works are currently being carried out to prepare a new version of CLC+ by 2024. The geographical, climatic, and economic diversity of the European Union raises the challenge to verify various test areas’ methods and algorithms. Based on the Corine program’s precise guidelines, Sentinel-2 and Landsat 8 satellite images were tested to assess classification accuracy and regional and spatial development in three varied areas of Catalonia, Poland, and Romania. The method is dependent on two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM). The bias of classifications was reduced using an iterative of randomized training, test, and verification pixels. The ease of the implementation of the used algorithms makes reproducing the results possible and comparable. The results show that an SVM with a radial kernel is the best classifier, followed by RF. The high accuracy classes that can be updated and classes that should be redefined are specified. The methodology’s potential can be used by developers of CLC+ products as a guideline for algorithms, sensors, and the possibilities and difficulties of classifying different CLC classes.
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Partridge, Stephanie R., Rebecca Raeside, Zoe Latham, et al. "‘Not to Be Harsh but Try Less to Relate to ‘the Teens’ and You’ll Relate to Them More’: Co-Designing Obesity Prevention Text Messages with Adolescents." International Journal of Environmental Research and Public Health 16, no. 24 (2019): 4887. http://dx.doi.org/10.3390/ijerph16244887.
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Text messages remain a preferred way for adolescents to communicate, and recent evidence suggests adolescents would like access to digital healthcare options. However, there is limited evidence for text messages to engage adolescent populations in obesity prevention behaviors. We aimed to co-design a bank of text messages that are evidence-based, acceptable, and engaging for adolescents. An established iterative mixed methods process, consisting of three phases, was used to develop the text message program. The first bank of 145 text messages was drafted based on current evidence, behavior change techniques, and input from researchers and health professionals. A survey was then administered to adolescents and professionals for review of text message content, usefulness, understanding, and age-appropriateness. An adolescent research assistant collaborated with the research team on all three phases. Forty participants (25 adolescents and 15 professionals) reviewed the initial bank of 145 text messages. On average, all reviewers agreed the text messages were easy to understand (13.6/15) and useful (13.1/15). In total, 107 text messages were included in the final text message bank to support behavior change and prevent obesity. This study may guide other researchers or health professionals who are seeking to engage adolescents in the co-design of health promotion or intervention content. Effectiveness of the text message program will be tested in a randomized controlled trial.
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Hannan, Abdul, and Jagadeesh Anmala. "Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA." Water 13, no. 19 (2021): 2790. http://dx.doi.org/10.3390/w13192790.
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The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.