Relevant bibliographies by topics / Prediction of survival; Probability; Time models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Prediction of survival; Probability; Time models'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Prediction of survival; Probability; Time models"

1

Lan, Yu, and Daniel F. Heitjan. "Adaptive parametric prediction of event times in clinical trials." Clinical Trials 15, no. 2 (2018): 159–68. http://dx.doi.org/10.1177/1740774517750633.

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Background: In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions. Purpose: Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric meth
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Gensheimer, Michael F., and Balasubramanian Narasimhan. "A scalable discrete-time survival model for neural networks." PeerJ 7 (January 25, 2019): e6257. http://dx.doi.org/10.7717/peerj.6257.

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There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD).
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Ren, Kan, Jiarui Qin, Lei Zheng, et al. "Deep Recurrent Survival Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4798–805. http://dx.doi.org/10.1609/aaai.v33i01.33014798.

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Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential p
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Li, Kan, and Sheng Luo. "Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease." Statistical Methods in Medical Research 28, no. 2 (2017): 327–42. http://dx.doi.org/10.1177/0962280217722177.

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In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and sur
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Alemazkoor, Negin, Conrad J. Ruppert, and Hadi Meidani. "Survival analysis at multiple scales for the modeling of track geometry deterioration." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232, no. 3 (2017): 842–50. http://dx.doi.org/10.1177/0954409717695650.

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Defects in track geometry have a notable impact on the safety of rail transportation. In order to make the optimal maintenance decisions to ensure the safety and efficiency of railroads, it is necessary to analyze the track geometry defects and develop reliable defect deterioration models. In general, standard deterioration models are typically developed for a segment of track. As a result, these coarse-scale deterioration models may fail to predict whether the isolated defects in a segment will exceed the safety limits after a given time period or not. In this paper, survival analysis is used
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Sun, Zhaohong, Wei Dong, Jinlong Shi, Kunlun He, and Zhengxing Huang. "Attention-Based Deep Recurrent Model for Survival Prediction." ACM Transactions on Computing for Healthcare 2, no. 4 (2021): 1–18. http://dx.doi.org/10.1145/3466782.

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Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attenti
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Tan, Ping, Lu Yang, Hang Xu, and Qiang Wei. "Novel perioperative parameters-based nomograms for survival outcomes in upper tract urothelial carcinoma after radical nephroureterectomy." Journal of Clinical Oncology 37, no. 7_suppl (2019): 414. http://dx.doi.org/10.1200/jco.2019.37.7_suppl.414.

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414 Background: Recently, several postoperative nomograms for cancer-specific survival (CSS) after radical nephroureterectomy (RNU) were proposed, while they did not incorporate the same variables; meanwhile, many preoperative blood-based parameters, which were recently reported to be related to survival, were not included in their models. In addition, no nomogram for overall survival (OS) was available to date. Methods: The full data of 716 patients were available. The whole cohort was randomly divided into two cohorts: the training cohort for developing the nomograms (n = 508) and the valida
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Liu, Xing-Rong, Yudi Pawitan, and Mark Clements. "Parametric and penalized generalized survival models." Statistical Methods in Medical Research 27, no. 5 (2016): 1531–46. http://dx.doi.org/10.1177/0962280216664760.

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We describe generalized survival models, where g( S( t| z)), for link function g, survival S, time t, and covariates z, is modeled by a linear predictor in terms of covariate effects and smooth time effects. These models include proportional hazards and proportional odds models, and extend the parametric Royston–Parmar models. Estimation is described for both fully parametric linear predictors and combinations of penalized smoothers and parametric effects. The penalized smoothing parameters can be selected automatically using several information criteria. The link function may be selected base
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Andrinopoulou, Eleni-Rosalina, D. Rizopoulos, Johanna JM Takkenberg, and E. Lesaffre. "Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data." Statistical Methods in Medical Research 26, no. 4 (2015): 1787–801. http://dx.doi.org/10.1177/0962280215588340.

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Nowadays there is an increased medical interest in personalized medicine and tailoring decision making to the needs of individual patients. Within this context our developments are motivated from a Dutch study at the Cardio-Thoracic Surgery Department of the Erasmus Medical Center, consisting of patients who received a human tissue valve in aortic position and who were thereafter monitored echocardiographically. Our aim is to utilize the available follow-up measurements of the current patients to produce dynamically updated predictions of both survival and freedom from re-intervention for futu
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Liu, Chuchu, Anja J. Rueten-Budde, Andreas Ranft, Uta Dirksen, Hans Gelderblom, and Marta Fiocco. "Dynamic prediction of overall survival: a retrospective analysis on 979 patients with Ewing sarcoma from the German registry." BMJ Open 10, no. 10 (2020): e036376. http://dx.doi.org/10.1136/bmjopen-2019-036376.

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ObjectivesThis study aimed at developing a dynamic prediction model for patients with Ewing sarcoma (ES) to provide predictions at different follow-up times. During follow-up, disease-related information becomes available, which has an impact on a patient’s prognosis. Many prediction models include predictors available at baseline and do not consider the evolution of disease over time.SettingIn the analysis, 979 patients with ES from the Gesellschaft für Pädiatrische Onkologie und Hämatologie registry, who underwent surgery and treatment between 1999 and 2009, were included.DesignA dynamic pre
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Dissertations / Theses on the topic "Prediction of survival; Probability; Time models"

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Ripley, Ruth Mary. "Neural network models for breast cancer prognosis." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244721.

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Jones, Margaret. "Point prediction in survival time models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340616.

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Kaponen, Martina. "Prediction of survival time of prostate cancer patients using Cox regression." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354482.

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Rodrigo, Hansapani Sarasepa. "Bayesian Artificial Neural Networks in Health and Cybersecurity." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6940.

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Being in the era of Big data, the applicability and importance of data-driven models like artificial neural network (ANN) in the modern statistics have increased substantially. In this dissertation, our main goal is to contribute to the development and the expansion of these ANN models by incorporating Bayesian learning techniques. We have demonstrated the applicability of these Bayesian ANN models in interdisciplinary research including health and cybersecurity. Breast cancer is one of the leading causes of deaths among females. Early and accurate diagnosis is a critic
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Begum, Mubeena. "Gene expression profiles and clinical parameters for survival prediction in stage II and III colorectal cancer." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001554.

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Yang, Lili. "Joint models for longitudinal and survival data." Thesis, 2014. http://hdl.handle.net/1805/4666.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus on estimating the correlation between the two change points. We adopted a Bayesian approach for parameter estimation and inference. In the second par
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Yuan, Yan. "Prediction Performance of Survival Models." Thesis, 2008. http://hdl.handle.net/10012/3974.

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Statistical models are often used for the prediction of future random variables. There are two types of prediction, point prediction and probabilistic prediction. The prediction accuracy is quantified by performance measures, which are typically based on loss functions. We study the estimators of these performance measures, the prediction error and performance scores, for point and probabilistic predictors, respectively. The focus of this thesis is to assess the prediction performance of survival models that analyze censored survival times. To accommodate censoring, we extend the inverse proba
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Li-Yuan, Chang, and 張瓈元. "An Empirical Study on Default Probability Models- Comparing Discrete-Time Survival Model and Merton's Model." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/65281870518653699527.

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碩士<br>國立交通大學<br>財務金融研究所<br>95<br>Based on the data of Taiwan corporations trading in TSE and OTC，this study used factor analysis to choose variables and according to stock data to construct financial distress prediction models, such as discrete-time survival model and Merton’ s model and then estimated the default probability when company went into bankruptcy. Furthermore, I compared the accuracy of two models. This study classified the variables into four categories, which are financial structure、ability to pay、efficiency of administration and ability to profit. The methods used in analyzing
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Kusiak, Caroline. "Real-Time Dengue Forecasting In Thailand: A Comparison Of Penalized Regression Approaches Using Internet Search Data." 2018. https://scholarworks.umass.edu/masters_theses_2/708.

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Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions
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Στεφάνου, Παύλος. "Development of scale-bridging methodologies and algorithms founded on the outcome of detailed atomistic simulations for the reliable prediction of the viscoelastic properties of polymer melts." Thesis, 2011. http://nemertes.lis.upatras.gr/jspui/handle/10889/4563.

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In this thesis we design and develop algorithms for predicting the rheological behavior of polymer melts based on the results of detailed atomistic simulations and guided by theories of the Dynamics of Polymers and fundamental Principles of Science of the Non-Equilibrium Thermodynamics. More specifically: 1) We propose a new rheological constitutive model for the time evolution of the tensor conformation tensor C of chains in a polymer melt (and hence the stress tensor τ) using the generalized bracket formalism of Beris and Edwards. The new constitutive model includes terms that describe a wh
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Books on the topic "Prediction of survival; Probability; Time models"

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Life time data: Statistical models and methods. World Scientific, 2006.

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Box, George E. P. Time series analysis: Forecasting and control. 4th ed. John Wiley, 2008.

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Box, George E. P. Time series analysis: Forecasting and control. 3rd ed. Prentice Hall, 1994.

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Box, George E. P. Time series analysis: Forecasting and control. 4th ed. John Wiley, 2008.

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Lifetime Data: Statistical Models and Methods. World Scientific Publishing Co Pte Ltd, 2015.

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Box, George E. P. Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics). 4th ed. Wiley-Interscience, 2008.

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Deshpande, Jayant V., and Sudha G. Purohit. Life-time Data: Statistical Models And Methods (Quality, Reliability and Engineering Statistics) (Quality, Reliabiltiy & Engineering Statistics). World Scientific Publishing Company, 2006.

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Box, George E. P. Time Series Analysis: Forecasting & Control. Pearson Education Asia Limited, 2005.

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Jenkins, Gwilym M., Gregory C. Reinsel, and George E. P. Box. Time Series Analysis: Forecasting and Control. Wiley & Sons, Incorporated, John, 2011.

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Jenkins, Gwilym M., Gregory C. Reinsel, and George E. P. Box. Time Series Analysis: Forecasting and Control. Wiley & Sons, Incorporated, John, 2013.

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Book chapters on the topic "Prediction of survival; Probability; Time models"

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"Identification of Prognostic Factors Related to Survival Time: Nonproportional Hazards Models." In Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471458546.ch13.

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"u = 1 u = 1.5u = 2 u = 3 u = 4 u = 5.5u = 6.5u = 7.9 u = 8.9u = 10.7 Survival Probability." In Joint Models for Longitudinal and Time-to-Event Data. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b12208-24.

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Diao, Qian, Jianye Lu, Wei Hu, Yimin Zhang, and Gary Bradski. "DBN Models for Visual Tracking and Prediction." In Bayesian Network Technologies. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-141-4.ch009.

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In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background clutter and occlusion. Such dynamics and background induce nonlinear, nonGaussian and multimodal observation densities. These densities are difficult to model with traditional methods such as Kalman filter models (KFMs) due to their Gaussian assumptions. Dynamic Bayesian networks (DBNs) provide a more general framework in which to solve these problems. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. Under the DBN umbrella, a broad class of learning and inference algorithms for time-series models can be used in visual tracking. Furthermore, DBNs provide a natural way to combine multiple vision cues. In this chapter, we describe some DBN models for tracking in nonlinear, nonGaussian and multimodal situations, and present a prediction method to assist feature extraction part by making a hypothesis for the new observations.
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"Patient 25 Patient 25 Patient 25 Extra 1 year Extra 2 years Extra 4 years Patient 2 Patient 2 Patient 2 Extra 1 year Extra 2 years Extra 4 years Survival Probability." In Joint Models for Longitudinal and Time-to-Event Data. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b12208-21.

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Godara, Deepa, Amit Choudhary, and Rakesh Kumar Singh. "Predicting Change Prone Classes in Open Source Software." In Research Anthology on Usage and Development of Open Source Software. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9158-1.ch034.

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In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.
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Klepac, Goran. "Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries." In Handbook of Research on Novel Soft Computing Intelligent Algorithms. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4450-2.ch017.

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This chapter represents the business case in the telecommunication company called Veza, in domain of churn prediction and churn mitigation. The churn project was divided into few stages. Due to limited budget and cost optimization, stage one was concentrated on prospective customer value calculation model based on fuzzy expert system. This helps Veza company to find most valuable telecom subscribers. It also helped company to better understand subscriber portfolio structure. Developed fuzzy expert system also helped Veza company in detection of soft churn. Stage two is profiling and customer segmentation based on time series analysis which provided potential predictors for predictive churn model. The central stage was concentrated on developing traditional predictive churn model based on logistic regression. This calculated probability that subscribers will make churn in next few months. The final stage was dedicated to SNA (Social Network Analysis) model development which found out the most valuable customers from the perspective of existing subscriber network. This model gave the answer that subscribers have the greatest influence on other subscribers in a way what is dangerous if they leave Veza company because they will motivate other subscribers to do the same thing. All three stages made complete churn detection/mitigation solution which take into consideration past behaviour of subscribers, their prospective value, and their strength of influence on other subscribers. This project helped Veza company to decrease churn rate and it gave directions for better understanding customer needs and behaviour which were the base for new product development.
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Klepac, Goran. "Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries." In Business Intelligence. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch023.

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This chapter represents the business case in the telecommunication company called Veza, in domain of churn prediction and churn mitigation. The churn project was divided into few stages. Due to limited budget and cost optimization, stage one was concentrated on prospective customer value calculation model based on fuzzy expert system. This helps Veza company to find most valuable telecom subscribers. It also helped company to better understand subscriber portfolio structure. Developed fuzzy expert system also helped Veza company in detection of soft churn. Stage two is profiling and customer segmentation based on time series analysis which provided potential predictors for predictive churn model. The central stage was concentrated on developing traditional predictive churn model based on logistic regression. This calculated probability that subscribers will make churn in next few months. The final stage was dedicated to SNA (Social Network Analysis) model development which found out the most valuable customers from the perspective of existing subscriber network. This model gave the answer that subscribers have the greatest influence on other subscribers in a way what is dangerous if they leave Veza company because they will motivate other subscribers to do the same thing. All three stages made complete churn detection/mitigation solution which take into consideration past behaviour of subscribers, their prospective value, and their strength of influence on other subscribers. This project helped Veza company to decrease churn rate and it gave directions for better understanding customer needs and behaviour which were the base for new product development.
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Conference papers on the topic "Prediction of survival; Probability; Time models"

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Nemeth, Noel N., Osama M. Jadaan, Eric H. Baker, and John P. Gyekenyesi. "Lifetime Reliability Prediction of Ceramics Subjected to Thermal and Mechanical Cyclic Loads." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27047.

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A methodology is shown for predicting the time-dependent reliability (probability of survival) of ceramic components against catastrophic rupture when subjected to thermal and mechanical cyclic loads. This methodology is based on the Weibull distribution to model stochastic strength and a power law that models subcritical crack growth. Changes in material response that can occur with temperature or time (i.e. changing fatigue and Weibull parameters with temperature or time) are accommodated by segmenting a cycle into discrete time increments. Material properties are assumed to be constant with
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Yim, Solomon C., Tongchate Nakhata, and Erick T. Huang. "Coupled Nonlinear Barge Motions: Part II — Deterministic Models, Stochastic Models and Stability Analysis." In ASME 2004 23rd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/omae2004-51131.

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A computationally efficient quasi-two-degree-of-freedom (Q2DOF) stochastic model and a stability analysis of barges in random seas are presented in this paper. Based on the deterministic 2DOF coupled Roll-Heave model with high-degree polynomial approximation of restoring forces and moments developed in Part I, an attempt is made to further reduce the DOF of the model for efficient stochastic stability analysis by decoupling the heave effects on roll motion, resulting in a one-degree-of-freedom (1DOF) roll-only model. Using the Markov assumption, stochastic differential equations governing the
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Exarchos, Themis P., George Rigas, Yorgos Goletsis, et al. "A dynamic Bayesian network approach for time-specific survival probability prediction in patients after ventricular assist device implantation." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944296.

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Mak, Lawrence, Brian Farnworth, Eugene H. Wissler, et al. "Thermal Requirements for Surviving a Mass Rescue Incident in the Arctic: Preliminary Results." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49471.

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Maritime and air traffic through the Arctic has increased in recent years. Cruise ship and commercial jet liners carry a large number of passengers. With increased traffic, there is a higher probability that a major disaster could occur. Cruise ship and plane accidents could be catastrophic and may require mass rescue. Due to the remote location, limited search and rescue resources, time for these resources to get to the accident location and large number of survivors, the retrieval time could be several days. Therefore, survivors may be required to survive on their own for days while they awa
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Sokota, Samuel, Ryan D'Orazio, Khurram Javed, Humza Haider, and Russell Greiner. "Simultaneous Prediction Intervals for Patient-Specific Survival Curves." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/828.

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Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models -- known as individual survival distributions (ISDs) -- produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can
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Chen, Haibo, Torgeir Moan, Sverre Haver, and Kjell Larsen. "Prediction of Relative Motion and Probability of Contact Between FPSO and Shuttle Tanker in Tandem Offloading Operation." In ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/omae2002-28101.

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Tandem offloading safety between FPSO and shuttle tanker is under concern. A few collisions between the two vessels have happened in the North Sea in recent years. In these incidents, excessive relative motions (termed as surging and yawing in this paper) between FPSO and tanker are identified as “failure prone situations” which have contributed to the initiation of most collision incidents. To quantitatively assess the probability of surging and yawing events, and more importantly, to effectively reduce their occurrence in tandem offloading operation, we present a simulation-based approach in
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Rahman, Tahrima, Shasha Jin, and Vibhav Gogate. "Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/797.

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Recently there has been growing interest in learning probabilistic models that admit poly-time inference called tractable probabilistic models from data. Although they generalize poorly as compared to intractable models, they often yield more accurate estimates at prediction time. In this paper, we seek to further explore this trade-off between generalization performance and inference accuracy by proposing a novel, partially tractable representation called cutset Bayesian networks (CBNs). The main idea in CBNs is to partition the variables into two subsets X and Y, learn a (intractable) Bayesi
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Kim, Taewung, Kyukwon Bang, Hyun-Yong Jeong, and Stephen Decker. "A Simple Vehicle Model for Path Prediction During Evasive Maneuvers and a Stochastic Analysis on the Crash Probability." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43334.

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Active safety systems are being developed in automotive industry, and an analytical vehicle model is needed in such systems to predict vehicle path to assess the crash probability. However, the bicycle model cannot result in a good correlation with test data and ADAMS simulation results, and other analytical vehicle models which have 8 or 14 degrees of freedom need more computation time. Therefore, in this study a simple analytical vehicle model was proposed to predict vehicle path especially during evasive maneuvers. The analytical vehicle model can predict a vehicle’s path based on the given
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Arild, Øystein, Hans Petter Lohne, Hans Joakim Skadsem, Eric Patrick Ford, and Jon Tømmerås Selvik. "Time-to-Failure Estimation of Barrier Systems in Permanently Plugged and Abandoned Wells." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-96546.

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Abstract With the increasing number of aging fields and wells worldwide, a large number of wells will have to permanently plugged and abandoned in the coming decades. Today’s technical solutions on P&amp;A design are primarily driven by legislations or recognized standards such as NORSOK D-10 or the Oil &amp; Gas UK Well Decommissioning Guidelines. The NORSOK D-010 say that the well should be sealing to “eternity” without providing any link between the recommended solution and time-to-failure. During the last few years, there has been a drive towards a risk-based approach to P&amp;A design. Wi
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Xia, Henian, Nathan Keeney, Brian J. Daley, Adam Petrie, and Xiaopeng Zhao. "Prediction of ICU In-Hospital Mortality Using Artificial Neural Networks." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3768.

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This work aims to predict in-hospital mortality in the open-source Physionet ICU database from features extracted from the time series of physiological variables using neural network models and other machine learning techniques. We developed an effective and efficient greedy algorithm for feature selection, reducing the number of potential features from 205 to a best subset of only 47. The average of five trials of 10-fold cross validation shows an accuracy of (86.23±0.14)%, a sensitivity of (50.29±0.22)%, a specificity of (92.01 ± 0.21)%, a positive prediction value of (50.29±0.50)%, a negati
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