A probabilistic network for the diagnosis of acute cardiopulmonary diseases
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Publication:3134244
DOI10.1002/BIMJ.201600206zbMATH Open1383.62281arXiv1609.06864OpenAlexW2525095884WikidataQ48189058 ScholiaQ48189058MaRDI QIDQ3134244
Federico M. Stefanini, Alessandro Magrini, Davide Luciani
Publication date: 9 February 2018
Published in: Biometrical Journal (Search for Journal in Brave)
Abstract: In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented. This paper is a draft version of the article published after peer review in 2018 (https://doi.org/10.1002/bimj.201600206). A panel of expert physicians collaborated to specify the qualitative part, that is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables. The quantitative part, that is the set of all conditional probability distributions defined by each factor, was estimated in the Bayesian paradigm: we applied a special formal representation, characterized by a low number of parameters and a parameterization intelligible for physicians, elicited the joint prior distribution of parameters from medical experts, and updated it by conditioning on a dataset of hospital patient records using Markov Chain Monte Carlo simulation. Refinement was cyclically performed until the probabilistic network provided satisfactory Concordance Index values for a selection of acute diseases and reasonable inference on six fictitious patient cases. The probabilistic network can be employed to perform medical diagnosis on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167 patient findings.
Full work available at URL: https://arxiv.org/abs/1609.06864
Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)
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