SCH: INT: Large-Scale Probabilistic Phenotyping Applied to Patient Record Summarization

该项目为分析大规模电子健康记录(EHR)数据创造了新的方法和工具。疾病模型或表型来源于EHR中记录的大量患者特征。为了评估它们在临床应用中的价值和稳健性，表型被结合到临床医生在患者护理点的纵向患者记录汇总系统中。本项目的研究有助于两个相互关联的结果：(I)患者记录的概率图形模型，特别是患者表型的潜在Dirichlet分配(LDA)模型。研究了能够处理电子病历中的异类数据类型的模型及其面临的挑战，如稀疏性和人为冗余。要使这些模型在临床上有用，它们必须是人类可解释的，易于适应EHR驱动的应用程序，并且具有临床相关性。这是通过将先前的临床知识指定到模型中并自动从临床医生的反馈中学习来实现的；以及(Ii)在患者护理时为临床医生提供患者记录摘要。摘要生成器利用概率患者模型，并通过临床医生与部署的摘要生成器的交互来学习新的显著模型，本质上是学习不同患者表型的相关性。对于表现组模型和摘要的评估，特别注意评估它们在真实世界的临床环境中的价值，在护理的点。研究建立并转化为交付成果，是稳健的，并可与纽约市一家大型医院的EHR互操作。如果成功，可解释和可操作的患者模型的可用性将通过为临床医生提供更好的工具，对EHR驱动的研究活动和患者护理产生重大影响。最后，该项目向医学领域的学生介绍STEM活动，同时向STEM学生展示真实世界、令人兴奋的应用程序。有关更多信息，请参阅该项目网站：http://people.dbmi.columbia.edu/noemie/phenosum

This project creates novel methods and tools for the analysis of large-scale Electronic Health Record (EHR) data. Models of disease, or phenotypes, are derived from a large collection of patient characteristics, as recorded in the EHR. To assess their value and robustness in a clinical application, the phenotypes are incorporated into a longitudinal patient record summarization system for clinicians at the point of patient care.The research for this project contributes to two inter-related outcomes: (i) a probabilistic graphical model of a patient record, specifically a Latent Dirichlet Allocation (LDA) model of the patient phenotypes. Models that can handle the heterogeneous data types in the EHR, along with their challenges, such as sparseness and artificial redundancy are investigated. For the models to be useful in the clinical world, they must be interpretable by humans, easily adaptable for EHR-driven applications, and clinically relevant. This is achieved by specifying prior clinical knowledge into the models and learning from clinicians' feedback automatically; and (ii) a patient record summarizer for clinicians at the point of patient care. The summarizer leverages the probabilistic patient model and learns new models of salience through the clinicians' interactions with the deployed summarizer, in essence learning relevance of different patient phenotypes. For the evaluation of the phenome model and the summarizer, particular care is given to assessing their value in a real-world clinical setting, at the point of care.The research builds on and is translated into deliverables that are robust and are inter-operable with the EHR of a large hospital in New York City. If successful, the availability of interpretable and actionable patient models can impact drastically both EHR-driven research activities and patient care, through better tools for clinicians. Finally, the project introduces students in the field of medicine to STEM activities, while presenting real-world, exciting application to STEM students.For further information see the project website at: http://people.dbmi.columbia.edu/noemie/phenosum