Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning

TL;DR

This paper develops a deep reinforcement learning model inspired by clinical guidelines to improve anemia diagnosis by learning optimal diagnostic sequences from real-world electronic health records, offering transparent decision pathways.

Contribution

It introduces a DRL-based approach for anemia diagnosis that learns from real-world data and provides interpretable diagnostic pathways, advancing clinical decision support tools.

Findings

DRL algorithms perform competitively with state-of-the-art methods.

The approach offers transparent, step-by-step diagnostic pathways.

Effective on both synthetic and real-world datasets.

Abstract

Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that learns from electronic health records to determine the optimal sequence of actions for accurate diagnosis. Focusing on anemia and its sub-types, we employ deep reinforcement learning (DRL) algorithms and evaluate their performance on both a synthetic dataset, which is based on expert-defined diagnostic pathways, and a real-world dataset. We investigate the performance of these algorithms across various scenarios. Our experimental results demonstrate that DRL algorithms perform competitively with state-of-the-art methods while offering the significant advantage of progressively generating pathways to the suggested diagnosis, providing a transparent decision-making process that can guide and explain diagnostic reasoning.