Dynamic Healthcare Embeddings for Improving Patient Care-猫眼课题宝

DOI：

10.1109/asonam55673.2022.10068627

发表时间：

2022-11

期刊：

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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其他

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作者：

Hankyu Jang;Sulyun Lee;D. M. H. Hasan;P. Polgreen;Sriram V. Pemmaraju;Bijaya Adhikari Department of Computer Science;U. Iowa;Interdisciplinary Graduate Program in Informatics;Department of Preventive Medicine

随着医院逐步实现计算系统的自动化和集成，越来越多的细粒度医院运营数据变得可用。这些数据包括医院建筑图纸、患者与医疗保健专业人员之间的交互日志、处方数据、程序数据以及患者入院、出院和转院的数据。这为改善患者护理的医疗保健相关预测任务开辟了许多迷人的途径。然而，为了利用现成的机器学习软件来完成这些任务，人们需要从异构的动态数据流中学习所涉及的实体的结构化表示。在这里，我们提出了DECENT，一个自动编码的异构协同进化动态神经网络，用于从不同的数据流中学习患者，医生，房间和药物的异构动态嵌入。这些嵌入基于静态属性和动态交互来捕获医生、房间、患者和药物之间的相似性。DECENT能够在医疗保健预测中实现若干应用，例如预测患者的死亡风险和病例严重程度、不良事件（例如，转回重症监护室），以及未来的医疗保健相关感染。在预测建模中使用学习到的患者嵌入的结果表明，DECENT在死亡风险预测任务上的收益高达48.1%，在病例严重程度预测任务上的收益高达12.6%，在医疗重症监护病房转移任务上的收益高达6.4%，在艰难梭菌（C.diff）感染（CDI）预测任务上的收益高达3.8%。此外，对博学的医生、药物和房间嵌入的案例研究表明，我们的方法学习了有意义和可解释的嵌入。

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (C.diff) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings.

Dynamic Healthcare Embeddings for Improving Patient Care

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