Noshin Kazi, Boland Mary Regina, Hou Bojian, He Weiqing, Lu Victoria, Manning Carol, Shen Li, Zhang Aidong
Department of Computer Science.
Data Science Program, Department of Mathematics, Saint Vincent College, Latrobe, PA 15650, USA.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:385-394. eCollection 2025.
Falls among the elderly and especially those with NeuroDegenerative Disorders (NDD) reduces life expectancy. The purpose of this study is to explore the role of Machine Learning on Electronic Health Records (EHR) data for time-to-event survival analysis prediction of injuries, and role of sensitive attributes, e.g., Race, Ethnicity, Sex, in these models. We used multiple survival analysis methods on a cohort of 29,045 patients 65 years and older treated at PennMedicine for either NDD, Mild Cognitive Impairment (MCI), or another disease. We compare the algorithms and explore the role of multiple modalities on improving prediction of injuries among NDD patients, specifically medications and laboratory tests. Overall, we found that medication features resulted in either increased Hazard Ratios (HR) or reduced HR depending on the NDD type. We found that being of Black race significantly increased the risk offall/injury in the models that included only medication and sensitive attribute features. The combined model that used both modalities (medications and laboratory information) removed this relationship between being of Black race and increases in fall/injury. Therefore, we found that combining modalities in these survival models in the prediction offall/injury risk among NDD and MCI individuals results in findings that are robust to different Racial and Ethnic groups with no biases apparent in our final combined modality results. Furthermore, combining modalities (both medications and laboratory values) improved the survival analysis performance across multiple survival analysis methods, when compared using the C-index.
老年人，尤其是患有神经退行性疾病（NDD）的老年人跌倒会缩短预期寿命。本研究的目的是探讨机器学习在电子健康记录（EHR）数据用于损伤事件生存分析预测中的作用，以及敏感属性（如种族、民族、性别）在这些模型中的作用。我们对宾夕法尼亚大学医学中心治疗的29045名65岁及以上患有NDD、轻度认知障碍（MCI）或其他疾病的患者队列使用了多种生存分析方法。我们比较了算法，并探讨了多种模式在改善NDD患者损伤预测中的作用，特别是药物和实验室检查。总体而言，我们发现药物特征根据NDD类型导致危险比（HR）增加或降低。我们发现，在仅包含药物和敏感属性特征的模型中，黑人种族显著增加了跌倒/受伤的风险。使用两种模式（药物和实验室信息）的组合模型消除了黑人种族与跌倒/受伤增加之间的这种关系。因此，我们发现，在NDD和MCI个体跌倒/受伤风险预测的这些生存模型中结合多种模式，会得出对不同种族和民族群体具有稳健性的结果，在我们最终的组合模式结果中没有明显的偏差。此外，当使用C指数进行比较时，结合多种模式（药物和实验室值）在多种生存分析方法中提高了生存分析性能。
