标题： 从医学数据中抢救被禁用的宝藏：利用替代结局和单条记录进行罕见事件建模 显示英文标题

标题： Salvaging Forbidden Treasure in Medical Data: Utilizing Surrogate Outcomes and Single Records for Rare Event Modeling

摘要： 电子健康记录（EHR）和医疗索赔的大规模存储库蕴含着研究罕见但至关重要的事件（如自杀未遂）的潜力。传统方法通常将自杀未遂建模为单一变量的结果，并由于缺乏历史信息而排除了“单记录”患者（即只有一次记录的患者）。然而，令人惊讶的是，在数据中，那些在唯一一次就诊时被诊断为自杀未遂的患者可能占所有尝试案例的很大比例，高达70%-80%。我们创新性地提出了一种混合且集成的学习框架，利用同时发生的结局作为替代指标，并从单记录数据中挖掘被禁止但宝贵的信息。我们的方法采用监督学习组件来学习连接主要结局（如自杀）和替代结局（如精神障碍）与历史信息之间的潜在变量。同时，它还使用无监督学习组件通过共享潜在变量来利用单记录数据。因此，我们的方法提供了一种整合信息的一般策略，这对建模罕见条件和事件至关重要。使用来自康涅狄格州医院住院数据，我们证明了单记录数据和同时诊断确实携带有价值的信息，并且利用这些信息可以显著提高自杀风险建模的效果。 显示英文摘要

摘要： The vast repositories of Electronic Health Records (EHR) and medical claims hold untapped potential for studying rare but critical events, such as suicide attempt. Conventional setups often model suicide attempt as a univariate outcome and also exclude any ``single-record'' patients with a single documented encounter due to a lack of historical information. However, patients who were diagnosed with suicide attempts at the only encounter could, to some surprise, represent a substantial proportion of all attempt cases in the data, as high as 70--80%. We innovate a hybrid and integrative learning framework to leverage concurrent outcomes as surrogates and harness the forbidden yet precious information from single-record data. Our approach employs a supervised learning component to learn the latent variables that connect primary (e.g., suicide) and surrogate outcomes (e.g., mental disorders) to historical information. It simultaneously employs an unsupervised learning component to utilize the single-record data, through the shared latent variables. As such, our approach offers a general strategy for information integration that is crucial to modeling rare conditions and events. With hospital inpatient data from Connecticut, we demonstrate that single-record data and concurrent diagnoses indeed carry valuable information, and utilizing them can substantially improve suicide risk modeling.