A Decision-Tree-Based Bayesian Approach for Chance-Constrained Health Prevention Budget Rationing

Nilson Herazo-Padilla, Vincent Augusto, Benjamin Dalmas, Xiaolan Xie, Bienvenu Bongue

Research output: Contribution to journalArticlepeer-review

Abstract

Medical test selection is a recurring problem in health prevention and consists of proposing a set of tests to each subject for diagnosis and treatment of pathologies. The problem is characterized by the unknown risk probability distribution across the population and two contradictory objectives: minimizing the number of tests and giving the medical test to all at-risk populations. This article sets this problem in a general framework of chance-constrained medical test rationing with unknown subject distribution over an attribute space and unknown risk probability but with a given sample population. A new approach combining decision-tree and Bayesian inference is proposed to allocate relevant medical tests according to the subjects' profile. Case studies on screening of hypertension and diabetes are conducted, and the performance of the proposed approach is evaluated. Significant savings on unnecessary tests are achieved with limited numbers of subjects needing but not receiving necessary tests.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages18
JournalIEEE Transactions on Automation Science and Engineering
DOIs
StateE-pub ahead of print - Apr 16 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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