Annotating Music Therapy, Chiropractic and Aquatic Exercise Using Electronic Health Record

Huixue Zhou, Greg M Silverman, Zhongran Niu, Jenzi Silverman, Rui Zhang, Roni L Evans, Robin Austin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. The overall goal of this study is to represent information pertinent to music therapy, chiropractic, and aquatic exercise in an EHR system. A total of 300 clinical notes were randomly selected and manually annotated. Annotations were made for status, symptom, and frequency of each approach. This set of annotations was used as a gold standard to evaluate performance of NLP systems used in this study (specifically BioMedICUS, MetaMap and cTAKES) for extracting CIH concepts. Three NLP systems achieved an average lenient match Fl-score of 0.50 in all three CIH approaches. BioMedICUS achieved the best performance in music therapy with an F1-score of 0.73. This study is a pilot to investigate CIH representation in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages610-611
Number of pages2
ISBN (Electronic)9781665468459
DOIs
StatePublished - 2022
Event10th IEEE International Conference on Healthcare Informatics, ICHI 2022 - Rochester, United States
Duration: Jun 11 2022Jun 14 2022

Publication series

NameProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022

Conference

Conference10th IEEE International Conference on Healthcare Informatics, ICHI 2022
Country/TerritoryUnited States
CityRochester
Period6/11/226/14/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • complementary and integrative health
  • electronic health record
  • knowledge representation

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