Using annotation for computerized support for fast skimming of cardiology electronic health record notes

Mahshad Koohi H. Dehkordi, Andrew J. Einstein, Shuxin Zhou, Gai Elhanan, Yehoshua Perl, Vipina K. Keloth, James Geller, Hao Liu

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

Abstract

Under the circumstances prevalent in current healthcare, medical professionals such as physicians and nurses need to read large numbers of Electronic Health Record (EHR) notes. This demand fosters a situation in which providers typically do not read a whole note but quickly skim it, to capture its essential content. Consequently, by fast skimming, one may miss a critical medical fact. Annotation highlights important content in EHR notes, and enables healthcare professionals to perform fast skimming, thereby minimizing the risk of missing critical information, which is detrimental to patient care. We designed the Cardiology Interface Terminology (CIT) for the purpose of annotation of cardiology EHRs. We emphasize that by annotation we refer to highlighting important information in the EHR notes for enabling fast skimming rather than just recognizing names of diseases, drugs, etc. from Reference Terminologies as is usually done by existing Named Entity Recognition (NER) systems. The CIT design starts with the cardiology components of SNOMED CT. It is enhanced by mining phrases from cardiology EHRs, as potential CIT concepts, which are of higher granularity than SNOMED concepts. Machine learning (ML), the state of the art technique for mining concepts from EHRs, requires training data. However, there is no training data for designing CIT. In the first stage, we introduce an innovative semi-automatic method for mining concepts from EHRs, to replace costly manual mining. The only manual portion is the review of the automatically mined phrases, before their insertion as CIT concepts. The effectiveness of annotation of cardiology EHRs with CIT was evaluated utilizing proper metrics, and compared to annotation with SNOMED CT. In a future second stage, ML mining techniques will be used for enhancing CIT with extra concepts from EHRs, utilizing the concepts added in the first stage as training data. This work focuses on a novel semi-automated method to design the Cardiology Interface Terminology (CIT) for annotation of cardiology EHRs to support fast skimming of EHR notes. Similar interface terminologies for other medical specialties could be obtained from CIT using Transfer Learning.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
EditorsXingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4043-4050
Number of pages8
ISBN (Electronic)9798350337488
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey
Duration: Dec 5 2023Dec 8 2023

Publication series

NameProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/TerritoryTurkey
CityIstanbul
Period12/5/2312/8/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Automotive Engineering
  • Modeling and Simulation
  • Health Informatics

Keywords

  • EHR
  • annotation
  • cardiology
  • fast skimming

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