A Pilot Report on Extracting Symptom Onset Date and Time from Clinical Notes in Patients Presenting with Chest Pain

Abstract

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.

Keywords: clinical notes; electronic health records; natural language processing; temporality; time expression.

Figure 1.

Examples of H&P (top) and ED Screening notes (bottom). Both notes have preset sections/sub-sections (bolded). We extracted descriptions of patient-reported chest pain complaints (greyed) and used the sentences as input into the NLP pipelines. The desired outputs of DateTime are shown in a standard MM/DD/YYYY HH:MM format. The date, time, and age above are de-identified.