Optimal testing policies for diagnosing patients with intermediary probability of disease

作者：

Highlights：

• We introduce a shortest path algorithm to derive optimal policies for disease diagnosis.

• The algorithm makes use of a Bayesian approach to derive pos-test probabilities given the result.

• A dynamic programming algorithm is used to find the optimal sequence of tests up to diagnosis

• The algorithm is guaranteed to reach a posterior probability that warrants immediate diagnosis.

摘要

•We introduce a shortest path algorithm to derive optimal policies for disease diagnosis.•The algorithm makes use of a Bayesian approach to derive pos-test probabilities given the result.•A dynamic programming algorithm is used to find the optimal sequence of tests up to diagnosis•The algorithm is guaranteed to reach a posterior probability that warrants immediate diagnosis.

论文关键词：Healthcare problems,Stochastic shortest path,Diagnosis

论文评审过程：Received 20 March 2017, Revised 11 October 2018, Accepted 17 November 2018, Available online 5 December 2018, Version of Record 13 June 2019.

论文官网地址：https://doi.org/10.1016/j.artmed.2018.11.005