Bayesian modeling and model–based decision analysis

2015 ◽  
pp. 203-242
Keyword(s):  
Decision Analysis ◽  
Bayesian Modeling ◽  
Model Based

Screening blood donors for hereditary hemochromatosis: Decision analysis model based on a 30-year database

1995 ◽  
Vol 109 (1) ◽  
pp. 177-188 ◽  
Author(s):  
Paul C. Adams ◽  
James C. Gregor ◽  
Ann E. Kertesz ◽  
Leslie S. Valberg
Keyword(s):  
Decision Analysis ◽  
Blood Donors ◽  
Hereditary Hemochromatosis ◽  
Decision Analysis Model ◽  
Analysis Model ◽  
Model Based

scholarly journals Clinical decision analysis and model‐based economic evaluation studies in perinatology: A systematic review

2019 ◽  
Vol 98 (8) ◽  
pp. 967-975 ◽  
Author(s):  
Rohan D'Souza ◽  
Kyra Bonasia ◽  
Prakesh S. Shah ◽  
Kellie E. Murphy ◽  
Beate Sander
Keyword(s):  
Systematic Review ◽  
Economic Evaluation ◽  
Decision Analysis ◽  
Evaluation Studies ◽  
Clinical Decision ◽  
Model Based

scholarly journals Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

2017 ◽  
Vol 1 ◽  
pp. 24-57 ◽  
Author(s):  
Woo-Young Ahn ◽  
Nathaniel Haines ◽  
Lei Zhang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Computational Modeling ◽  
Bayesian Modeling ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Modeling ◽  
Model Based ◽  
Modeling Approaches ◽  
Wide Range ◽  
User Friendly

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.

scholarly journals Cost-effectiveness of increasing vaccination in high-risk adults aged 18–64 Years: a model-based decision analysis

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Angela R. Wateska ◽  
Mary Patricia Nowalk ◽  
Richard K. Zimmerman ◽  
Kenneth J. Smith ◽  
Chyongchiou J. Lin
Keyword(s):  
Cost Effectiveness ◽  
High Risk ◽  
Decision Analysis ◽  
Model Based

scholarly journals Challenges of Ethical Evaluation Models for Intelligent Assistive Technologies. An Initial Ethical Model Based on Linguistic Decision Analysis

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 22
Author(s):  
M. Espinilla ◽  
M. A. Verdejo ◽  
L. González ◽  
C. Nugent ◽  
A.J. Cruz ◽  
...  
Keyword(s):  
Decision Analysis ◽  
Evaluation Process ◽  
Software Tool ◽  
Assistive Technologies ◽  
Ethical Values ◽  
Review Of The Literature ◽  
Ethical Evaluation ◽  
Model Based ◽  
Evaluation Models ◽  
Shed Light

Some ethical evaluation models for Intelligent Assistive Technologies (IATs) have been presented in the literature. However, it has been proven that the vast majority of IATs were designed in the absence of explicit ethical values and considerations. In order to shed light on this problem, this paper presents a review of the literature on ethical values and considerations regarding IATs. According to this review, four challenges are identified to facilitate the evaluation of ethical models in IAT design: (i) Rational method, (ii) uncertainty evaluation process, (iii) evaluation results and aggregation process, (iv) software tool. These challenges are analyzed in order to propose an initial ethical model based on linguistic decision analysis.

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