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

SCH：INT：应用于患者记录汇总的大规模概率表型分析

• 批准号: 1344668
• 负责人: Noemie Elhadad
• 金额: $ 199.42万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-15 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1344668&HistoricalAwards=false
• 关键词: SCH; INT; Large; Scale; Probabilistic

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

该项目创建了用于分析大规模电子健康记录（EHR）数据的新方法和工具。疾病模型或表型来自于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，表型被纳入一个纵向病历汇总系统，供临床医生在患者护理点使用。该项目的研究有助于两个相互关联的结果：（i）病历的概率图形模型，特别是患者表型的潜在狄利克雷分配（LDA）模型。 模型，可以处理异构数据类型的EHR，沿着与他们的挑战，如稀疏和人工冗余进行了研究。对于在临床世界中有用的模型，它们必须是人类可解释的，易于适应EHR驱动的应用程序，并且与临床相关。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）在患者护理点处为临床医生提供的患者记录汇总器。摘要器利用概率患者模型，并通过临床医生与部署的摘要器的交互来学习新的显著性模型，本质上是学习不同患者表型的相关性。为了评估表型模型和摘要器，特别注意评估它们在现实世界的临床环境中的价值，在护理点。该研究建立在并转化为可交付成果，这些成果是强大的，并与纽约市一家大型医院的EHR互操作。如果成功的话，可解释和可操作的患者模型的可用性可以通过为临床医生提供更好的工具来彻底影响EHR驱动的研究活动和患者护理。最后，该项目向医学领域的学生介绍STEM活动，同时向STEM学生展示真实世界的令人兴奋的应用。欲了解更多信息，请访问项目网站：http://people.dbmi.columbia.edu/noemie/phenosum