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

Autor:	Muyama, Lillian, Lu, Estelle, Cheminet, Geoffrey, Pouchot, Jacques, Rance, Bastien, Tropeano, Anne-Isabelle, Neuraz, Antoine, Coulet, Adrien
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:2404.05913
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.02273 Zobrazit plný text záznamu View this record from Arxiv