DEEP REINFORCEMENT LEARNING FOR COST-EFFECTIVE MEDICAL DIAGNOSIS

Zheng Yu, Yikuan Li, Joseph C. Kim, Kaixuan Huang, Yuan Luo, Mengdi Wang

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

Abstract

Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highly imbalanced; therefore, we aim to maximize the F1 score instead of the error rate. However, optimizing the non-concave F1 score is not a classic RL problem, thus invalidating standard RL methods. To remedy this issue, we develop a reward shaping approach, leveraging properties of the F1 score and duality of policy optimization, to provably find the set of all Pareto-optimal policies for budget-constrained F1 score maximization. To handle the combinatorially complex state space, we propose a Semi-Model-based Deep Diagnosis Policy Optimization (SM-DDPO) framework that is compatible with end-to-end training and online learning. SM-DDPO is tested on diverse clinical tasks: ferritin abnormality detection, sepsis mortality prediction, and acute kidney injury diagnosis. Experiments with real-world data validate that SM-DDPO trains efficiently and identify all Pareto-front solutions. Across all tasks, SM-DDPO is able to achieve state-of-the-art diagnosis accuracy (in some cases higher than conventional methods) with up to 85% reduction in testing cost. Core codes are available on GitHub.

Original languageEnglish (US)
StatePublished - 2023
Event11th International Conference on Learning Representations, ICLR 2023 - Kigali, Rwanda
Duration: May 1 2023May 5 2023

Conference

Conference11th International Conference on Learning Representations, ICLR 2023
Country/TerritoryRwanda
CityKigali
Period5/1/235/5/23

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Computer Science Applications
  • Education
  • Linguistics and Language

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