Patient Medical History Representation, Extraction, and Inference from EHR Data

EHR 数据中的患者病史表示、提取和推断

• 批准号: 9115724
• 负责人: Cui Tao
• 金额: $ 33.52万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9115724
• 关键词: Address; Adopted; Aftercare; Archives; Automated Annotation; Big Data; Chronic Disease; Clinical; Clinical Data; Clinical Research; Colorectal Cancer; Communication; Complex; Computer software; Data; Data Collection; Data Reporting; Data Set; Data Sources; Databases; Decision Support Systems; Detection; Diabetes Mellitus; Disease; Disease Progression; Electronic Health Record; Evaluation; Event; Goals; Gold; Harvest; Human; Institutes; Maps; Measures; Medical History; Medical Records; Modeling; Natural Language Processing; Nature; Ontology; Patient Care; Patients; Performance; Recording of previous events; Registries; Reporting; Resolution; Semantics; Structure; System; Testing; Time; Translational Research; Work; application programming interface; base; clinical practice; cohort; colon cancer patients; data modeling; data structure; information model; innovation; novel strategies; open source; personalized medicine; tool; trend analysis

DESCRIPTION (provided by applicant): The significance of developing tools for automatically harvesting temporal constraints of clinical events from Electronic Health Records (EHR) cannot be overestimated. Efficient analysis of the temporal aspects in EHR data could boost an array of clinical and translational research such as disease progression studies, decision support systems, and personalized medicine. One big challenge we are facing is to automatically untangle and linearize the temporal constraints of clinical events embedded in highly diverse large-scale EHR data. Barriers to temporal data modeling, normalization, extraction, and reasoning have precluded the efficient use of EHR data sources for event history evaluation and trending analysis: (1) The current federally-supported EHR data normalization tools do not focus on the time aspect of unstructured data yet; (2) Existing time models focus only on structured data with absolute time, lack of supporting reasoning systems, or only offer application-specific partial solutions which cannot be adopted by the complex EHR data; (3) Current temporal information extraction approaches are either difficult to be adopted to EHR data, not scalable, or only offers application-specific partial solution. This proposed project fills in the current gaps among ontologies, Natural Language Processing (NLP), and EHR-based clinical research for temporal data representation, normalization, extractions, and reasoning. We propose to develop novel approaches for automatic temporal data representation, normalization and reasoning for large, diverse, and heterogeneous EHR data and prepare the integrated data for further analysis. We will build new reasoning and extraction capacities on our TIMER (Temporal Information Modeling, Extracting, and Reasoning) framework to provide an end-to-end, open-source, standard-conforming software package. TIMER will be built on strong prior work by our team. We will develop new features in our CNTRO (Clinical Narrative Temporal Relation Ontology) for semantically defining the time domain and representing temporal data in complex EHR data. On top of the new developed CNTRO semantics, we will implement temporal relation reasoning capacities to automatically normalize temporal expressions, compute and infer temporal relations, and resolve ambiguities. We will leverage existing NLP tools and work on top of these tools to develop new extraction approaches to fill in the current gaps between NLP approaches and ontology-based reasoning approaches. We will adapt the SHARPn EHR data normalization pipeline and cTAKES for extracting and normalizing clinical event mentions from clinical narratives. We will explore an innovative approach for temporal relation extraction and event coreference, and make it work with the TIMER framework. We will evaluate the system using Diabetes Mellitus (DM) and colorectal cancer (CRC) patient cohorts from two insititutions. Each component will be tested separately first followed by an evaluation of the whole framework. Results such as precision, recall, and f-measure will be reported.

描述（由申请人提供）：开发用于从电子健康记录（EHR）中自动获取临床事件时间约束的工具的重要性不能被高估。对电子病历数据中时间方面的有效分析可以促进一系列临床和转化研究，如疾病进展研究、决策支持系统和个性化医疗。