Natural language processing for detecting medication-related notes in heart failure telehealth patients

Alphons Eggerth*, Karl Kreiner, Dieter Hayn, Bernhard Pfeifer, Gerhard Pölzl, Tim Egelseer-Bründl, Günter Schreier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Heart Failure is a severe chronic disease of the heart. Telehealth networks implement closed-loop healthcare paradigms for optimal treatment of the patients. For comprehensive documentation of medication treatment, health professionals create free text collaboration notes in addition to structured information. To make this valuable source of information available for adherence analyses, we developed classifiers for automated categorization of notes based on natural language processing, which allows filtering of relevant entries to spare data analysts from tedious manual screening. Furthermore, we identified potential improvements of the queries for structured treatment documentation. For 3,952 notes, the majority of the manually annotated category tags was medication-related. The highest F1-measure of our developed classifiers was 0.90. We conclude that our approach is a valuable tool to support adherence research based on datasets containing free-text entries.

Original languageEnglish
Title of host publicationDigital Personalized Health and Medicine - Proceedings of MIE 2020
EditorsLouise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
PublisherIOS Press
Pages761-765
Number of pages5
ISBN (Electronic)9781643680828
DOIs
Publication statusPublished - 16 Jun 2020
Event30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland
Duration: 28 Apr 20201 May 2020

Publication series

NameStudies in Health Technology and Informatics
Volume270
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference30th Medical Informatics Europe Conference, MIE 2020
CountrySwitzerland
CityGeneva
Period28/04/201/05/20

Keywords

  • Adherence
  • Heart failure
  • Machine learning
  • Telemedicine
  • Text mining

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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