Acute coronary syndrome (ACS) is an acute heart disease that often evolves rapidly. In ACS patients presenting with no-ST-segment elevation (NSTE-ACS), the timing of symptom onset pre-hospital may inform the disease stage and prognosis. We pilot-tested two off-the-shelf natural language processing (NLP) pipelines, namely parsedatetime and regular expression (regex), to extract date and time (DateTime) information of patient-reported chest pain symptoms from electronic health records (EHR) clinical notes. We included three types of clinical notes (N=71): History and Physical (n=49), Emergency Department Screening (n=3), and Triage Notes (n=19). All notes were manually annotated for the true DateTime of symptom onset. Parsedatetime returned matching DateTime outputs in 36 notes (50.7%), while regex returned zero matched outputs. Parsedatetime performed better than regex, although it was still suboptimal. Both pipelines require constant refinement and custom improvements. Methods for a large-scale, automated DateTime extraction from EHR clinical notes further investigation.

The authors have declared no competing interest.

This study was supported by the University of Rochester CTSA award number UL1 TR002001 from the National Center for Advancing Translational Sciences of the National Institutes of Health through the URMC CTSI Pilot Studies Award (PI: S. Suba). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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The Research Subjects Review Board (RSRB) of the University of Rochester gave ethical approval for this work (STUDY00008105).

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Codes and outputs produced in the present work are contained in the manuscript. Clinical notes cannot be publicly shared as they are represent potentially identifying and sensitive patient data.

https://github.com/suksuba/datetime-expression