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

• URL: http://arxiv.org/abs/2412.02273v1
• Date: Tue, 03 Dec 2024 08:45:50 GMT
• Title: Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
• Authors: Lillian Muyama, Estelle Lu, Geoffrey Cheminet, Jacques Pouchot, Bastien Rance, Anne-Isabelle Tropeano, Antoine Neuraz, Adrien Coulet,
• Abstract summary: Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. We aim to develop a model that learns from electronic health records to determine the optimal sequence of actions for accurate diagnosis.
• Score: 1.5272023683653024
• License:
• 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.

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