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.
The significance of developing capabilities for automatically harvesting temporal constraints for clinical events from Electronic Health Records (EHR) cannot be overestimated. A substantial portion of the information in the EHR is historical in nature. Patient medical history can be long, especially in complex patients. The proposed work, by offering an end-to-end open-source framework for automatically extracting, normalizing, and reasoning clinically-important time-relevant information from large-scale EHR data, can boost an array of clinical and translational research such as disease progression studies, decision support systems, and personalized medicine; as well as facilitate clinical practice for early disease detection, post-treatment care, and patient-clinician communication.
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