A probabilistic network for the diagnosis of acute cardiopulmonary diseases
This work addresses the problem of medical diagnosis for acute cardiopulmonary diseases, but it is incremental as it applies existing probabilistic methods to a specific domain.
The authors developed a probabilistic network for diagnosing acute cardiopulmonary diseases by collaborating with expert physicians to define a directed acyclic graph and using Bayesian methods to estimate conditional probabilities from hospital data, achieving satisfactory Concordance Index values for selected diseases and reasonable inference on fictitious cases.
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.