Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
This work addresses the need for interpretable AI in clinical diagnostics for anemia, offering a tool to assist healthcare professionals, though it is incremental as it applies existing DRL methods to this specific medical domain.
The paper tackled the problem of automating differential anemia diagnosis by developing a deep reinforcement learning model that learns optimal diagnostic action sequences from electronic health records, achieving competitive performance with state-of-the-art methods while providing transparent, step-by-step diagnostic pathways.
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.