Abstract
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 a 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.
| Originalsprache | Englisch |
|---|---|
| Titel | Digital Personalized Health and Medicine |
| Redakteure/-innen | Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott |
| Herausgeber (Verlag) | IOS Press |
| Seiten | 761-765 |
| Seitenumfang | 5 |
| ISBN (Print) | 978-1-64368-082-8 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 30th Medical Informatics Europe conference (MIE 2020) CANCELLED due to the SARS-CoV-2 pandemy - Dauer: 28 Apr. 2020 → 1 Mai 2020 |
Konferenz
| Konferenz | 30th Medical Informatics Europe conference (MIE 2020) CANCELLED due to the SARS-CoV-2 pandemy |
|---|---|
| Zeitraum | 28/04/20 → 1/05/20 |
Research Field
- Exploration of Digital Health
Schlagwörter
- Adherence
- heart failure
- telemedicine
- text mining
- machine learning