Zalewski Aaron, Long William, Johnson Alistair E W, Mark Roger G, Lehman Li-Wei H
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA.
IEEE EMBS Int Conf Biomed Health Inform. 2017 Feb;2017:449-452. doi: 10.1109/BHI.2017.7897302. Epub 2017 Apr 13.
Modern intensive care units (ICUs) collect large volumes of data in monitoring critically ill patients. Clinicians in the ICUs face the challenge of interpreting large volumes of high-dimensional data to diagnose and treat patients. In this work, we explore the use of Hierarchical Dirichlet Processes (HDP) as a Bayesian nonparametric framework to infer patients' states of health by combining multiple sources of data. In particular, we employ HDP to combine clinical time series and text from the nursing progress notes in a probabilistic topic modeling framework for patient risk stratification. Given a patient cohort, we use HDP to infer latent "topics" shared across multimodal patient data from the entire cohort. Each topic is modeled as a multinomial distribution over a vocabulary of codewords, defined over heterogeneous data sources. We evaluate the clinical utility of the learned topic structure using the first 24-hour ICU data from over 17,000 adult patients in the MIMIC-II database to estimate patients' risks of in-hospital mortality. Our results demonstrate that our approach provides a viable framework for combining different data modalities to model patient's states of health, and can potentially be used to generate alerts to identify patients at high risk of hospital mortality.
现代重症监护病房（ICU）在监测重症患者时会收集大量数据。ICU的临床医生面临着解读大量高维数据以诊断和治疗患者的挑战。在这项工作中，我们探索使用分层狄利克雷过程（HDP）作为贝叶斯非参数框架，通过整合多源数据来推断患者的健康状况。具体而言，我们采用HDP在概率主题建模框架中结合临床时间序列和护理进展记录中的文本，用于患者风险分层。给定一个患者队列，我们使用HDP从整个队列的多模态患者数据中推断出共享的潜在“主题”。每个主题被建模为一个在码字词汇表上的多项分布，该词汇表定义在异构数据源上。我们使用MIMIC-II数据库中超过17000名成年患者的前24小时ICU数据来评估所学习到的主题结构的临床效用，以估计患者的院内死亡风险。我们的结果表明，我们的方法为整合不同数据模态以建模患者健康状况提供了一个可行的框架，并且有可能用于生成警报以识别有高院内死亡风险的患者。
