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Статті в журналах з теми "Prior informatif"

Автор: Grafiati

Опубліковано: 21 червня 2022

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1

Hasanah, Uswatul, Ferra Yanuar, and Dodi Devianto. "PENDUGAAN PARAMETER PADA DISTRIBUSI GAMMA DENGAN METODE BAYES." Jurnal Matematika UNAND 7, no. 4 (February 19, 2019): 81. http://dx.doi.org/10.25077/jmu.7.4.81-86.2018.

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Penelitian ini membahas tentang pendugaan parameter pada distribusi Gamma dengan parameter α diketahui. Metode pendugaan parameter yang digunakan adalah metode Bayes dengan dua distribusi prior, yaitu distribusi prior konjugat dan distribusi prior non-informatif. Distribusi prior konjugat yang diperoleh adalah distribusi Gamma (α , , β, ) dan distribusi prior non-informatif diperoleh dengan melakukan metode perluasan Jeffrey sehingga menghasilkan prior Jeffrey adalah 1 β2k .Kata Kunci: Metode Bayes, Distribusi prior, Metode Jeffrey, Distribusi Gamma

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2

Rahmadiah, Annisa. "INFERENSI BAYESIAN PADA DISTRIBUSI EKSPONENSIAL." Jurnal Matematika UNAND 7, no. 4 (February 19, 2019): 93. http://dx.doi.org/10.25077/jmu.7.4.93-99.2018.

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Penelitian ini dilakukan dengan tujuan menduga parameter pada distribusi Eksponensial dengan metode Bayes. Pendugaan parameter dilakukan secara analitik dengan menggunakan distribusi Gamma sebagai prior konjugat dan distribusi prior Jeffrey sebagai prior non-informatif. Setelah itu, dilakukan evaluasi penduga menggunakan metode AIC. Berdasarkan studi analitik diperoleh bahwa distribusi Gamma sebagai distribusi prior konjugat lebih baik dibandingkan dengan prior Jeffrey dalam hal pendugaan parameter.Kata Kunci: Metode Bayes, distribusi prior, distribusi posterior, fungsi likelihood, metode AIC

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3

Yani, Resti Nanda, Ferra Yanuar, and Hazmira Yozza. "INFERENSI BAYESIAN UNTUK 2 DARI DISTRIBUSI NORMAL DENGAN BERBAGAI DISTRIBUSI PRIOR." Jurnal Matematika UNAND 7, no. 2 (May 1, 2018): 132. http://dx.doi.org/10.25077/jmu.7.2.132-139.2018.

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Abstrak. Pada penelitian ini dilakukan pendugaan parameter variansi (2) dari dis-tribusi Normal dengan mean () diketahui. Pendugaan parameter variansi (2) terse-but dilakukan secara analitik dengan menggunakan distribusi Invers Gamma sebagaiprior konjugat, metode Jerey sebagai prior non-informatif dan distribusi Uniform se-bagai prior non-konjugat. Pada penelitian ini kriteria evaluasi penduga yang digunakanadalah MSE dan sifat tak bias. Berdasarkan studi analitik diperoleh bahwa distribusiInvers Gamma sebagai prior konjugat merupakan prior terbaik diantara dua distribusiprior lainnya.Kata Kunci: Inferensi statistika, metode Bayes, distribusi prior, fungsi likelihood, dis-tribusi Normal, Invers Gamma, metode Jerey, distribusi Uniform

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4

Febriani, Dini, Sugito Sugito, and Alan Prahutama. "ANALISIS METODE BAYESIAN MENGGUNAKAN NON-INFORMATIF PRIOR UNIFORM DISKRIT PADA SISTEM ANTREAN PELAYANAN GERBANG TOL MUKTIHARJO." Jurnal Gaussian 10, no. 3 (December 30, 2021): 337–45. http://dx.doi.org/10.14710/j.gauss.v10i3.32783.

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The growth rate of the traffic that is high resulting in congestion on the road network system. One of the government's efforts in addressing the issue with the build highways to reduce congestion, especially in large cities. One of the queuing phenomena that often occurs in the city of Semarang is the queue at the Toll Gate Muktiharjo, that the queue of vehicles coming to make toll payment. This study aims to determine how the service system at the Toll Gate Muktiharjo. This can be known by getting a queue system model and a measure of system performance from the distribution of arrival and service. The distribution of arrival and service are determined by finding the posterior distribution using the Bayesian method. The bayesian method combine the likelihood function of the sample and the prior distribution. The likelihood function is a negative binomial. The prior distribution used a uniform discrete. Based on the calculations and analysis, it can be concluded that the queueing system model at the Toll Gate Muktiharjo is a (Beta/Beta/5):(GD/∞/∞). The queue simulation obtained that the service system Toll Gate Muktiharjo is optimal based on the size of the system performance because busy probability is higher than jobless probability.

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5

Fu, Ying, Xi Wu, Xiaohua Li, Kun He, Yi Zhang, and Jiliu Zhou. "Image Motion Restoration Using Fractional-Order Gradient Prior." Informatica 26, no. 4 (January 1, 2015): 621–34. http://dx.doi.org/10.15388/informatica.2015.67.

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6

Hyun, Jeong-Hoon. "주관적 성과평가에서 전년도 성과정보가 관대화경향에 미치는 영향". Korean Governmental Accounting Review 19, № 1 (30 квітня 2021): 167–218. http://dx.doi.org/10.15710/kgar.2021.19.1.167.

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7

Pandey, Vijay Kumar, Rajeev Pandey, and Mayank Trivedi. "Bayesian Method in Linear Model and Constant Time Series Model Using Non-Informative Prior Under Phenology." Mathematical Journal of Interdisciplinary Sciences 3, no. 2 (March 30, 2015): 183–91. http://dx.doi.org/10.15415/mjis.2015.32016.

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8

CHANDRA, N., and V. K. RATHAUR. "Bayesian Estimation of Augmented Exponential Strength Reliability Models Under Non-informative Priors." Mathematical Journal of Interdisciplinary Sciences 5, no. 1 (September 5, 2016): 15–31. http://dx.doi.org/10.15415/mjis.2016.51002.

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9

Myungjin Cho, Myungjin Cho. "Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information." Chinese Optics Letters 13, no. 7 (2015): 070301–70304. http://dx.doi.org/10.3788/col201513.070301.

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10

Kalaylioglu, Zeynep, and Haydar Demirhan. "A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements." Statistical Methods in Medical Research 26, no. 6 (November 6, 2015): 2885–96. http://dx.doi.org/10.1177/0962280215615003.

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Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.

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11

Cooper, G. F., and Y. Shen. "A New Prior for Bayesian Anomaly Detection." Methods of Information in Medicine 49, no. 01 (2010): 44–53. http://dx.doi.org/10.3414/me09-01-0008.

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Summary Objectives: Bayesian anomaly detection computes posterior probabilities of anomalous events by combining prior beliefs and evidence from data. However, the specification of prior probabilities can be challenging. This paper describes a Bayesian prior in the context of disease outbreak detection. The goal is to provide a meaningful, easy-to-use prior that yields a posterior probability of an outbreak that performs at least as well as a standard frequentist approach. If this goal is achieved, the resulting posterior could be usefully incorporated into a decision analysis about how to act in light of a possible disease outbreak. Methods: This paper describes a Bayesian method for anomaly detection that combines learning from data with a semi-informative prior probability over patterns of anomalous events. A univariate version of the algorithm is presented here for ease of illustration of the essential ideas. The paper describes the algorithm in the context of disease-outbreak detection, but it is general and can be used in other anomaly detection applications. For this application, the semi-informative prior specifies that an increased count over baseline is expected for the variable being monitored, such as the number of respiratory chief complaints per day at a given emergency department. The semi-informative prior is derived based on the baseline prior, which is estimated from using historical data. Results: The evaluation reported here used semi-synthetic data to evaluate the detection performance of the proposed Bayesian method and a control chart method, which is a standard frequentist algorithm that is closest to the Bayesian method in terms of the type of data it uses. The disease-outbreak detection performance of the Bayesian method was statistically significantly better than that of the control chart method when proper baseline periods were used to estimate the baseline behavior to avoid seasonal effects. When using longer baseline periods, the Bayesian method performed as well as the control chart method. The time complexity of the Bayesian algorithm is linear in the number of the observed events being monitored, due to a novel, closed-form derivation that is introduced in the paper. Conclusions: This paper introduces a novel prior probability for Bayesian outbreak detection that is expressive, easy-to-apply, computationally efficient, and performs as well or better than a standard frequentist method.

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12

Ahmad Izaham, Ameera Raudah, and Nichollas E. Scott. "Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching." Molecular & Cellular Proteomics 19, no. 9 (June 23, 2020): 1561–74. http://dx.doi.org/10.1074/mcp.tir120.002100.

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Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

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13

Pedroza, Claudia, Weilu Han, Van Thi Thanh Truong, Charles Green, and Jon E. Tyson. "Performance of informative priors skeptical of large treatment effects in clinical trials: A simulation study." Statistical Methods in Medical Research 27, no. 1 (December 13, 2015): 79–96. http://dx.doi.org/10.1177/0962280215620828.

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One of the main advantages of Bayesian analyses of clinical trials is their ability to formally incorporate skepticism about large treatment effects through the use of informative priors. We conducted a simulation study to assess the performance of informative normal, Student- t, and beta distributions in estimating relative risk (RR) or odds ratio (OR) for binary outcomes. Simulation scenarios varied the prior standard deviation (SD; level of skepticism of large treatment effects), outcome rate in the control group, true treatment effect, and sample size. We compared the priors with regards to bias, mean squared error (MSE), and coverage of 95% credible intervals. Simulation results show that the prior SD influenced the posterior to a greater degree than the particular distributional form of the prior. For RR, priors with a 95% interval of 0.50–2.0 performed well in terms of bias, MSE, and coverage under most scenarios. For OR, priors with a wider 95% interval of 0.23–4.35 had good performance. We recommend the use of informative priors that exclude implausibly large treatment effects in analyses of clinical trials, particularly for major outcomes such as mortality.

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14

Patra, Lakshmi Kanta, Suchandan Kayal, and Somesh Kumar. "Measuring Uncertainty Under Prior Information." IEEE Transactions on Information Theory 66, no. 4 (April 2020): 2570–80. http://dx.doi.org/10.1109/tit.2020.2970408.

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15

Engerer, Volkmar Paul, and Fatima Sabir. "Information professionals meet Arthur Prior." Journal of Librarianship and Information Science 52, no. 1 (September 25, 2018): 288–305. http://dx.doi.org/10.1177/0961000618799527.

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The paper investigates the supporting role of information professionals in interdisciplinary digital research projects. It identifies three encounter scenarios in which information professionals meet domain researchers (as research librarian, as information specialist, and as iHumanist) and links them to the domain researchers ‘modes of orientation’. We examine these theoretical distinctions by discussing three cases from our own practical engagement in the Prior project. Our discussion shows that the scenarios help to understand information professionals’ supporting work, explain the conflicts that emerge, and explicate information professionals’ shifting conceptions of what they are doing in terms of the shifting encounter scenarios. In short, the paper presents methodological and theoretical insights that can be useful in understanding encounters between information professionals and domain researchers.

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16

Rapp, Amon, and Federica Cena. "Personal informatics for everyday life: How users without prior self-tracking experience engage with personal data." International Journal of Human-Computer Studies 94 (October 2016): 1–17. http://dx.doi.org/10.1016/j.ijhcs.2016.05.006.

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17

Rajendran, P., and M. Madheswaran. "An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure." Journal of Medical Systems 36, no. 2 (June 25, 2010): 747–64. http://dx.doi.org/10.1007/s10916-010-9542-8.

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18

Seuren, Lucas M. "Oh (/o/) als ontvanger van informatie in sociale interactie." Tijdschrift voor Taalbeheersing 41, no. 2 (October 1, 2019): 363–89. http://dx.doi.org/10.5117/tvt2019.2.002.seur.

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Abstract Oh (/o/) as information receipt in Dutch talk-in-interactionRecently there has been an increased interest cross-linguistically in how speakers use interjections in everyday interaction. A particularly productive line of inquiry deals with what are known as change-of-state tokens, interjections with which speakers claim that there has been a shift in their cognitive state such as their knowledge, understanding, attention, etc. In this paper I explore the variability of the Dutch interjection Oh /o/. Focusing on its use in response to informing turns, I argue that as a free-standing particle speakers use it to claim that the information in that prior turn was in some way unexpected, either because it contradicted what the speaker claimed he or she knew, or because it contradicted some presupposition that was encoded in an earlier question. I subsequently discuss the most frequent ways in which Oh is combined with other turn components, showing how it is used to respond to announcements of valenced news, to do now-remembering, and to make claims of now-understanding. In closing I show that when Oh prefaces additional turn components such as oké (‘okay’), each component deals with a different action-implication of the ongoing sequence and that Oh is used to receive the information being conveyed.

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19

Brakenhoff, TB, KCB Roes, and S. Nikolakopoulos. "Bayesian sample size re-estimation using power priors." Statistical Methods in Medical Research 28, no. 6 (May 2, 2018): 1664–75. http://dx.doi.org/10.1177/0962280218772315.

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The sample size of a randomized controlled trial is typically chosen in order for frequentist operational characteristics to be retained. For normally distributed outcomes, an assumption for the variance needs to be made which is usually based on limited prior information. Especially in the case of small populations, the prior information might consist of only one small pilot study. A Bayesian approach formalizes the aggregation of prior information on the variance with newly collected data. The uncertainty surrounding prior estimates can be appropriately modelled by means of prior distributions. Furthermore, within the Bayesian paradigm, quantities such as the probability of a conclusive trial are directly calculated. However, if the postulated prior is not in accordance with the true variance, such calculations are not trustworthy. In this work we adapt previously suggested methodology to facilitate sample size re-estimation. In addition, we suggest the employment of power priors in order for operational characteristics to be controlled.

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20

Harrison, James H. "Pathology Informatics Questions and Answers From the University of Pittsburgh Pathology Residency Informatics Rotation." Archives of Pathology & Laboratory Medicine 128, no. 1 (January 1, 2004): 71–83. http://dx.doi.org/10.5858/2004-128-71-piqaaf.

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Abstract Context.—Effective pathology practice increasingly requires familiarity with concepts in medical informatics that may cover a broad range of topics, for example, traditional clinical information systems, desktop and Internet computer applications, and effective protocols for computer security. To address this need, the University of Pittsburgh (Pittsburgh, Pa) includes a full-time, 3-week rotation in pathology informatics as a required component of pathology residency training. Objective.—To teach pathology residents general informatics concepts important in pathology practice. Design.—We assess the efficacy of the rotation in communicating these concepts using a short-answer examination administered at the end of the rotation. Because the increasing use of computers and the Internet in education and general communications prior to residency training has the potential to communicate key concepts that might not need additional coverage in the rotation, we have also evaluated incoming residents' informatics knowledge using a similar pretest. Data Sources.—This article lists 128 questions that cover a range of topics in pathology informatics at a level appropriate for residency training. These questions were used for pretests and posttests in the pathology informatics rotation in the Pathology Residency Program at the University of Pittsburgh for the years 2000 through 2002. With slight modification, the questions are organized here into 15 topic categories within pathology informatics. The answers provided are brief and are meant to orient the reader to the question and suggest the level of detail appropriate in an answer from a pathology resident. Results.—A previously published evaluation of the test results revealed that pretest scores did not increase during the 3-year evaluation period, and self-assessed computer skill level correlated with pretest scores, but all pretest scores were low. Posttest scores increased substantially, and posttest scores did not correlate with the self-assessed computer skill level recorded at pretest time. Conclusions.—Even residents who rated themselves high in computer skills lacked many concepts important in pathology informatics, and posttest scores showed that residents with both high and low self-assessed skill levels learned pathology informatics concepts effectively.

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21

Meslin, S., W. Zheng, R. Day, E. Tay, and M. Baysari. "Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation." Applied Clinical Informatics 09, no. 04 (October 2018): 849–55. http://dx.doi.org/10.1055/s-0038-1676039.

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Introduction Drug–drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review. Methods After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the agreement between multiple drug interaction compendia with regards to the severity of these alerts. A subset of DDI alerts (n = 13), with associated patient information, were presented to an expert panel to reach a consensus on whether each alert should be included in the CPOE. Results There was poor agreement between compendia in their classifications of DDI severity (Krippendorff's α: 0.03; 95% confidence interval: –0.07 to 0.14). Only 10% of DDI alerts were classed as severe by all compendia. On the other hand, the panel reached consensus on 12 of the 13 alerts that were presented to them regarding whether they should be included in the CPOE. Conclusion Using an expert panel and allowing them to discuss their views openly likely resulted in high agreement on what alerts should be included in a CPOE system. Presenting alerts in the context of patient cases allowed panelists to identify the conditions under which alerts were clinically relevant. The poor agreement between compendia suggests that this methodology may not be ideal for the evaluation of DDI alerts. Performing preimplementation review of DDI alerts before they are enabled provides an opportunity to minimize the risk of alert fatigue before prescribers are exposed to false-positive alerts.

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22

Shulman, N., and M. Feder. "The Uniform Distribution as a Universal Prior." IEEE Transactions on Information Theory 50, no. 6 (June 2004): 1356–62. http://dx.doi.org/10.1109/tit.2004.828152.

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23

Chambers, R. Andrew, and Susan K. Conroy. "Network Modeling of Adult Neurogenesis: Shifting Rates of Neuronal Turnover Optimally Gears Network Learning according to Novelty Gradient." Journal of Cognitive Neuroscience 19, no. 1 (January 2007): 1–12. http://dx.doi.org/10.1162/jocn.2007.19.1.1.

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Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In “adult” networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis-neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders.

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24

Thall, Peter F., Moreno Ursino, Véronique Baudouin, Corinne Alberti, and Sarah Zohar. "Bayesian treatment comparison using parametric mixture priors computed from elicited histograms." Statistical Methods in Medical Research 28, no. 2 (September 5, 2017): 404–18. http://dx.doi.org/10.1177/0962280217726803.

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A Bayesian methodology is proposed for constructing a parametric prior on two treatment effect parameters, based on graphical information elicited from a group of expert physicians. The motivating application is a 70-patient randomized trial to compare two treatments for idiopathic nephrotic syndrome in children. The methodology relies on histograms of the treatment parameters constructed manually by each physician, applying the method of Johnson et al. (2010). For each physician, a marginal prior for each treatment parameter characterized by location and precision hyperparameters is fit to the elicited histogram. A bivariate prior is obtained by averaging the marginals over a latent physician effect distribution. An overall prior is constructed as a mixture of the individual physicians’ priors. A simulation study evaluating several versions of the methodology is presented. A framework is given for performing a sensitivity analysis of posterior inferences to prior location and precision and illustrated based on the idiopathic nephrotic syndrome trial.

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25

Petit, Caroline, Adeline Samson, Satoshi Morita, Moreno Ursino, Jérémie Guedj, Vincent Jullien, Emmanuelle Comets, and Sarah Zohar. "Unified approach for extrapolation and bridging of adult information in early-phase dose-finding paediatric studies." Statistical Methods in Medical Research 27, no. 6 (October 5, 2016): 1860–77. http://dx.doi.org/10.1177/0962280216671348.

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The number of trials conducted and the number of patients per trial are typically small in paediatric clinical studies. This is due to ethical constraints and the complexity of the medical process for treating children. While incorporating prior knowledge from adults may be extremely valuable, this must be done carefully. In this paper, we propose a unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults. The dose-range is calculated under three extrapolation options, linear, allometry and maturation adjustment, using adult pharmacokinetic data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose–toxicity and dose–efficacy relationships are obtained using early-phase adult toxicity and efficacy data at several dose levels. Priors are integrated into the dose-finding process through Bayesian model selection or adaptive priors. This calibrates the model to adjust for misspecification, if the adult and pediatric data are very different. We performed a simulation study which indicates that incorporating prior adult information in this way may improve dose selection in children.

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Berner, Eta S., Amanda D. Dorsey, Robert L. Garrie, and Haiyan Qu. "Assessment-based health informatics curriculum improvement." Journal of the American Medical Informatics Association 23, no. 4 (June 5, 2016): 813–18. http://dx.doi.org/10.1093/jamia/ocw073.

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Abstract Objective Informatics programs need assurance that their curricula prepare students for intended roles as well as ensuring that students have mastered the appropriate competencies. The objective of this study is to describe a method for using assessment data to identify areas for curriculum, student selection, and assessment improvement. Materials and Methods A multiple-choice examination covering the content in the Commission for Health Accreditation of Informatics and Information Management Education curricular facets/elements was developed and administered to 2 cohorts of entering students prior to the beginning of the program and to the first cohort after completion of the first year’s courses. The reliability of the examination was assessed using Cronbach’s alpha. Content validity was assessed by having 2 raters assess the match of the items to the Commission for Health Accreditation of Informatics and Information Management Education requirements. Construct validation included comparison of exam performance of instructed vs uninstructed students. Criterion-related validity was assessed by examining the relationship of background characteristics to exam performance and by comparing examination performance to graduate Grade Point Average (GPA). Results Reliability of the examination was 0.91 and 0.82 (Cohort 1 pre/post-tests) and 0.43 (Cohort 2 pretest). Both raters judged 76% of the test items as appropriate. There were statistically significant differences between the instructed (Cohort 1 post-test) and uninstructed (Cohort 2 pretest) students (t = 2.95 P < .01), as well as between the Cohort 1 pre/post-tests (t = 6.52, P < .001). Neither the background variables nor the graduate GPA were significantly correlated with the examination scores. Conclusion We found that the examination had generally good psychometric properties and the exceptions could be used to identify areas for curriculum and assessment improvement.

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Bowman, J. H. "The development of description in cataloguing prior to ISBD." Aslib Proceedings 58, no. 1/2 (January 2006): 34–48. http://dx.doi.org/10.1108/00012530610648662.

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MacNab, Ying C. "On Gaussian Markov random fields and Bayesian disease mapping." Statistical Methods in Medical Research 20, no. 1 (June 14, 2010): 49–68. http://dx.doi.org/10.1177/0962280210371561.

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We discuss the nature of Gaussian Markov random fields (GMRFs) as they are typically formulated via full conditionals, also named conditional autoregressive or CAR formulations, to represent small area relative risks ensemble priors within a Bayesian hierarchical model framework for statistical inference in disease mapping and spatial regression. We present a partial review on GMRF/CAR and multivariate GMRF prior formulations in univariate and multivariate disease mapping models and communicate insights into various prior characteristics for representing disease risks variability and ‘spatial interaction.’ We also propose convolution prior modifications to the well known BYM model for attainment of identifiability and Bayesian robustness in univariate and multivariate disease mapping and spatial regression. Several illustrative examples of disease mapping and spatial regression are presented.

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Bongers, Quinn, Ken Kunisaki, and Muna Irfan. "0546 An Informatics Approach to Assessing Quality of Care Metrics for Restless Legs Syndrome/Willis-Ekbom Disease." Sleep 45, Supplement_1 (May 25, 2022): A241. http://dx.doi.org/10.1093/sleep/zsac079.543.

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Abstract Introduction Management of restless legs syndrome (RLS, aka Willis-Ekbom Disease) with dopaminergic agents is often complicated by development of augmentation. Clinical practice guidelines and stepwise algorithms suggest that prior to prescribing dopaminergic agents, clinicians should assess iron stores and consider alpha-2-delta agents (e.g. gabapentin). We developed an informatics approach to assess quality of care metrics for RLS management. Methods We used Veterans Affairs (VA) electronic health record data to identify a cohort of patients at a single tertiary care academic VA facility prescribed dopaminergic agents between 01 Jan 2018 to 31 Dec 2019. Patients with any prior codes for Parkinson’s disease were excluded. A random sample of charts were manually reviewed to determine if dopaminergic drugs were being prescribed for RLS or other indications. We then assessed for evidence of prior iron store assessments, iron repletion if appropriate, and alpha-2-delta agents (gabapentin, pregabalin). Results We identified 1160 patients treated with dopaminergic agents and no prior Parkinson’s codes. Chart reviews indicated 95% accuracy of this methodology to identify dopaminergic use for RLS. Evidence of pre-treatment iron storage assessment was missing in 30.2% for ferritin and 33.5% for transferrin saturation. Among those with iron studies present, lack of iron replacement was noted in 34% of those with ferritin <75 mcg/L and 33% of those with transferrin saturation <20%. Prior or concomitant prescriptions of alpha-2-delta agents was present in 59.7% of the cohort. Conclusion Our informatics approach provides an accurate and efficient means to quantify RLS care metrics. Results were most notable for a high proportion of dopaminergic-treated RLS patients without iron assessments and without iron repletion when stores were low. This methodology will inform future quality improvement initiatives to improve the delivery of guideline concordant RLS care. Support (If Any) This material is the result of work supported with resources and the use of facilities at the Minneapolis Veterans Affairs Health Care System, Minneapolis/USA.

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Preuhs, Alexander, Andreas Maier, Michael Manhart, Markus Kowarschik, Elisabeth Hoppe, Javad Fotouhi, Nassir Navab, and Mathias Unberath. "Symmetry prior for epipolar consistency." International Journal of Computer Assisted Radiology and Surgery 14, no. 9 (July 12, 2019): 1541–51. http://dx.doi.org/10.1007/s11548-019-02027-8.

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31

Banerjee, Rahul, Paul George, Cedric Priebe, and Eric Alper. "Medical student awareness of and interest in clinical informatics." Journal of the American Medical Informatics Association 22, e1 (February 28, 2015): e42-e47. http://dx.doi.org/10.1093/jamia/ocu046.

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Abstract Objective We aimed to investigate medical students’ attitudes about Clinical Informatics (CI) training and careers. Materials and Methods We distributed a web-based survey to students at four US allopathic medical schools. Results Five hundred and fifty-seven medical students responded. Interest in CI training opportunities (medical school electives, residency electives, or academic fellowships) surpassed respondents’ prior awareness of these opportunities. Thirty percent of student respondents expressed at least some interest in a CI-related career, but they were no more aware of training opportunities than their peers who did not express such an interest. Discussion Almost one third of medical students who responded to our survey expressed an interest in a CI-related career, but they were generally unaware of CI training and mentoring opportunities available to them. Early outreach to such medical students, through elective classes, professional society incentives, or expert partnerships, may positively influence the size and skill set of the future CI workforce. Conclusion We should work as a field to increase the quantity, quality, and publicity of CI learning opportunities for interested medical students.

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Kubota, Nanako, Yufu Kasahara, Ken Harada, Koji Fujimoto, Tomohisa Okada, and Masato Inoue. "Prior ensemble learning." International Journal of Computer Assisted Radiology and Surgery 16, no. 11 (October 15, 2021): 1937–45. http://dx.doi.org/10.1007/s11548-021-02512-z.

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33

Krieger, Michael. "African-American Catholic Serial Literature Prior to 1960." Serials Librarian 35, no. 3 (September 30, 1998): 55–75. http://dx.doi.org/10.1300/j123v35n03_05.

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34

Nasimi, Fahimeh, Mohammad Reza Khayyambashi, Naser Movahhedinia, and Yee Wei Law. "Exploiting similar prior knowledge for compressing ECG signals." Biomedical Signal Processing and Control 60 (July 2020): 101960. http://dx.doi.org/10.1016/j.bspc.2020.101960.

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Haux, R., and S. Koch. "Improving Bridging from Informatics Theory to Practice." Applied Clinical Informatics 06, no. 04 (2015): 748–56. http://dx.doi.org/10.4338/aci-2015-10-ra-0147.

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SummaryBackground: In 1962, Methods of Information in Medicine (MIM) began to publish papers on the methodology and scientific fundamentals of managing data, information, and knowledge in biomedicine and health care. Meeting an increasing demand for research about practical implementation of health information systems, the journal Applied Clinical Informatics (ACI) was launched in 2009. Both journals are official journals of the International Medical Informatics Association (IMIA).Objectives: Based on prior analyses, we aimed to describe major topics published in MIM during 2014 and to explore whether theory of MIM influenced practice of ACI. Our objectives were further to describe lessons learned and to discuss possible editorial policies to improve bridging from theory to practice.Methods: We conducted a retrospective, observational study reviewing MIM articles published during 2014 (N=61) and analyzing reference lists of ACI articles from 2014 (N=70). Lessons learned and opinions about MIM editorial policies were developed in consensus by the two authors. These have been influenced by discussions with the journal’s associate editors and editorial board members.Results: The publication topics of MIM in 2014 were broad, covering biomedical and health informatics, medical biometry and epidemiology. Important topics discussed were biosignal interpretation, boosting methodologies, citation analysis, health-enabling and ambient assistive technologies, health record banking, safety, and standards. Nine ACI practice articles from 2014 cited eighteen MIM theory papers from any year. These nine ACI articles covered mainly the areas of clinical documentation and medication-related decision support. The methodological basis they cited from was almost exclusively related to evaluation. We could show some direct links where theory impacted practice. These links are however few in relation to the total amount of papers published.Conclusions: Editorial policies such as publishing systematic methodological reviews and clarification of possible practical impact of theory-focused articles may improve bridging.

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Yu, Jing, Weifeng Zhang, Zhuoqian Yang, Zengchang Qin, and Yue Hu. "Cross-modal learning with prior visual relation knowledge." Knowledge-Based Systems 203 (September 2020): 106150. http://dx.doi.org/10.1016/j.knosys.2020.106150.

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Chou, Jui-Sheng, Dinh-Nhat Truong, and Chih-Fong Tsai. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics." Mathematics 9, no. 6 (March 23, 2021): 686. http://dx.doi.org/10.3390/math9060686.

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Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

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Rashed, Essam A., and Hiroyuki Kudo. "Probabilistic atlas prior for CT image reconstruction." Computer Methods and Programs in Biomedicine 128 (May 2016): 119–36. http://dx.doi.org/10.1016/j.cmpb.2016.02.017.

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39

Masic, Izet. "The History and New Trends of Medical Informatics." Donald School Journal of Ultrasound in Obstetrics and Gynecology 7, no. 3 (2013): 301–12. http://dx.doi.org/10.5005/jp-journals-10009-1298.

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ABSTRACT The breakthrough of the computer and information technologies in all the segments of the society, led to the needs for the computer and information technologies. The knowledge of information technology is now part of general literacy. The computer literacy does not require comprehensive and detailed knowledge of the electronics or programming. Although with the electronic computer which is the invention of our age, the attempts of the construction of the first machine for the processing of the information reach far in the history of human civilization. The only and global function of a computer data processing can be naturally separated into the series of the other elementary operations, as for examples are: ‘the followup of the data, their registration, reproduction, selection, sorting, and comparison’ and so on. The computers are being classified according to ‘the purpose, type and computer size’. According to the purpose the computers it can be of the general and specific purposes. The computers for the general purpose serve for the commercial applications or any other application that is necessary. If medical informatics is regarded as a scientific discipline dealing with theory and practice of information processes in medicine, comprising data communication by information and communication technologies (ICT), with computers as an especially important ICT, then it can be stated that the history medical informatics is connected with the beginnings of computer usage in medicine. The medical informatics is the foundation for understanding and practice of the up-to-day medicine. Its basic tool is the computer, subject of studying and the means by which the aspects and achieve the new knowledge in the studying of a man, his health and disease, and functioning of the total health activities. Current network system possesses the limited global performance in the organization of health care, and that is especially expressed in the clinical medicine, where the computer technology has not received the wanted applications yet. In front of us lies the brilliant future of the medical informatics. It should expect that the application of terminal and personal computers with more simple manners of operation will enable routine use of computer technology by all health professionals in the fields of telemedicine, distance learning (DL) (web-based medical education), application of ICT, medical robotics, genomics, etc. The development of nature languages for communication with the computers and the identification of input voice will make the work simpler. Regarding the future of medical informatics education there are numerous controversies. Everybody agrees that the medical informatics is very significant for the whole health care and for the needs for personnel. However, there is not yet the general agreement regarding the teaching programs, because the medical informatics is very involved and propulsive, what makes the performance of the stable education programs more difficult. There are also not general agreement in which year of studding should transfer the knowledge from medical informatics. The majority of the experts still agree that the priority should be given in later study years, since more and more students enroll the faculties with prior informatics illiteracy, and the comprehension of some medical informatics fields is not possible without prior clinical knowledge. How to cite this article Masic I. The History and New Trends of Medical Informatics. Donald School J Ultrasound Obstet Gynecol 2013;7(3):301-312.

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40

Takahashi, Hayato. "Bayes code with singular prior." Systems and Computers in Japan 34, no. 8 (May 21, 2003): 22–32. http://dx.doi.org/10.1002/scj.10358.

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41

Sapci, A., and H. Sapci. "The Effectiveness of Hands-on Health Informatics Skills Exercises in the Multidisciplinary Smart Home Healthcare and Health Informatics Training Laboratories." Applied Clinical Informatics 08, no. 04 (2017): 1184–96. http://dx.doi.org/10.4338/aci-2017-08-ra-0136.

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Objective This article aimed to evaluate the effectiveness of newly established innovative smart home healthcare and health informatics laboratories, and a novel laboratory course that focuses on experiential health informatics training, and determine students' self-confidence to operate wireless home health monitoring devices before and after the hands-on laboratory course. Materials and Methods Two web-based pretraining and posttraining questionnaires were sent to 64 students who received hands-on training with wireless remote patient monitoring devices in smart home healthcare and health informatics laboratories. Results All 64 students completed the pretraining survey (100% response rate), and 49 students completed the posttraining survey (76% response rate). The quantitative data analysis showed that 95% of students had an interest in taking more hands-on laboratory courses. Sixty-seven percent of students had no prior experience with medical image, physiological data acquisition, storage, and transmission protocols. After the hands-on training session, 75.51% of students expressed improved confidence about training patients to measure blood pressure monitor using wireless devices. Ninety percent of students preferred to use a similar experiential approach in their future learning experience. Additionally, the qualitative data analysis demonstrated that students were expecting to have more courses with hands-on exercises and integration of technology-enabled delivery and patient monitoring concepts into the curriculum. Conclusion This study demonstrated that the multidisciplinary smart home healthcare and health informatics training laboratories and the hands-on exercises improved students' technology adoption rates and their self-confidence in using wireless patient monitoring devices.

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42

Primack, Alice L., and Anita L. Battiste. "Training reference staff prior to consolidating science collections." College & Research Libraries News 50, no. 6 (June 1, 1989): 473–74. http://dx.doi.org/10.5860/crln.50.6.473.

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43

Tari, Luis, Chitta Baral, and Seungchan Kim. "Fuzzy c-means clustering with prior biological knowledge." Journal of Biomedical Informatics 42, no. 1 (February 2009): 74–81. http://dx.doi.org/10.1016/j.jbi.2008.05.009.

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44

Li, Xingang. "Fast Blind Deblurring via Normalized Sparsity Prior." Journal of Information and Computational Science 10, no. 16 (November 1, 2013): 5083–91. http://dx.doi.org/10.12733/jics20102362.

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45

Khosrowjerdi, Mahmood, and Mohammad Iranshahi. "Prior knowledge and information-seeking behavior of PhD and MA students." Library & Information Science Research 33, no. 4 (October 2011): 331–35. http://dx.doi.org/10.1016/j.lisr.2010.04.008.

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46

Seamans, Nancy H. "Electronic theses and dissertations as prior publications: what the editors say." Library Hi Tech 21, no. 1 (March 2003): 56–61. http://dx.doi.org/10.1108/07378830310467409.

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47

Ramachandran, Anil, Sunil Gupta, Santu Rana, Cheng Li, and Svetha Venkatesh. "Incorporating expert prior in Bayesian optimisation via space warping." Knowledge-Based Systems 195 (May 2020): 105663. http://dx.doi.org/10.1016/j.knosys.2020.105663.

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48

Wang, Zikang, Linjing Li, Daniel Zeng, and Xiaofei Wu. "Incorporating prior knowledge from counterfactuals into knowledge graph reasoning." Knowledge-Based Systems 223 (July 2021): 107035. http://dx.doi.org/10.1016/j.knosys.2021.107035.

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49

Moral-García, Serafín, Javier G. Castellano, Carlos J. Mantas, and Joaquín Abellán. "Using extreme prior probabilities on the Naive Credal Classifier." Knowledge-Based Systems 237 (February 2022): 107707. http://dx.doi.org/10.1016/j.knosys.2021.107707.

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Kawamura, M., Y. H. Wu, T. Kudo, and C. c. Chen. "A statistical feature of anomalous seismic activity prior to large shallow earthquakes in Japan revealed by the pattern informatics method." Natural Hazards and Earth System Sciences 14, no. 4 (April 14, 2014): 849–59. http://dx.doi.org/10.5194/nhess-14-849-2014.

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Abstract. To reveal the preparatory processes of large inland earthquakes, we systematically applied the pattern informatics (PI) method to earthquake data of Japan. We focused on 12 large earthquakes with magnitudes greater than M = 6.4 (based on the magnitude scale of the Japan Meteorological Agency) that occurred at depths shallower than 30 km between 2000 and 2010. We examined the relationship between the spatiotemporal locations of these large shallow earthquakes and the locations of PI hotspots, which correspond to grid cells of anomalous seismic activity during a designated time span. Based on a statistical test conducted using Molchan's error diagram, we investigated whether precursory anomalous seismic activity occurred in association with these large earthquakes and, if so, studied the characteristic time spans of such activity. Our results indicate that Japanese inland earthquakes with M ≥ 6.4 are typically preceded by anomalous seismic activity in timescales of 8–10 years.

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