用深度强化学习自动规划贫血诊断步骤，透明可解释。
Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
- 基于电子病历和强化学习，自动推导最优诊断顺序。
- 在真实数据上表现优于或媲美现有方法，诊断准确率高。
- 适合临床辅助诊断系统开发，提升诊疗透明度。
临床诊断指南列出了达成诊断所需的关键问题。受此启发，我们旨在构建一个从电子健康记录中学习的模型，以确定准确诊断的最佳行动序列。聚焦于贫血及其亚型，我们采用深度强化学习（DRL）算法，并在基于专家定义诊断路径的合成数据集和真实世界数据集上评估其性能。我们考察了这些算法在多种场景下的表现。实验结果表明，DRL算法在诊断准确性上与当前最优方法相当，同时具备逐步生成诊断路径的优势，提供透明的决策过程，有助于指导和解释诊断推理。
原文摘要 · Abstract (English)
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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