CohortNet：赋能可解释医疗分析中的队列发现
CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics
AI总结：
针对现有医疗队列研究方法依赖人工或聚类缺乏医学相关性的问题，提出CohortNet模型，通过细粒度表征、启发式探索等实现可解释队列自动发现，性能优于现有方法并提供多维度见解。
摘要：
队列研究在医疗分析领域具有重要意义。然而，现有方法通常涉及人工、劳动密集型且由专家驱动的模式定义，或是依赖缺乏医学相关性的简单聚类技术。利用可解释模式实现队列研究自动化，对推动医疗分析发展具有巨大潜力，但这一需求在以往研究中仍未得到满足。本文提出一种队列自动发现模型CohortNet，用于可解释医疗分析，重点在于有效识别、表征和利用以具有医学意义模式为特征的队列。CohortNet首先通过分别处理每个特征学习细粒度患者表征，同时考虑每个时间步的单个特征趋势和特征交互。随后，它将每个特征划分为不同状态，并采用启发式队列探索策略，有效发现具有具体模式的大量队列。对于每个识别出的队列，模型通过关联患者检索学习带有可信证据的全面队列表征。最终，针对新患者，CohortNet可利用具有显著重要性的相关队列，提供对患者病情更全面的理解。在三个真实世界数据集上的大量实验表明，该模型始终优于现有最先进方法，并能以自上而下的方式从多维度提供可解释见解。
英文摘要：
Cohort studies are of significant importance in the field of healthcare analysis. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical relevance. Automating cohort studies with interpretable patterns has great potential to facilitate healthcare analysis but remains an unmet need in prior research efforts. In this paper, we propose a cohort auto-discovery model, CohortNet, for interpretable healthcare analysis, focusing on the effective identification, representation, and exploitation of cohorts characterized by medically meaningful patterns. CohortNet initially learns fine-grained patient representations by separately processing each feature, considering both individual feature trends and feature interactions at each time step. Subsequently, it classifies each feature into distinct states and employs a heuristic cohort exploration strategy to effectively discover substantial cohorts with concrete patterns. For each identified cohort, it learns comprehensive cohort representations with credible evidence through associated patient retrieval. Ultimately, given a new patient, CohortNet can leverage relevant cohorts with distinguished importance, which can provide a more holistic understanding of the patient's conditions. Extensive experiments on three real-world datasets demonstrate that it consistently outperforms state-of-the-art approaches and offers interpretable insights from diverse perspectives in a top-down fashion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
