@inproceedings{58e923e9611248a1adedcd61c5fed166,
title = "Natural language processing for detecting medication-related notes in heart failure telehealth patients",
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.",
keywords = "Adherence, Heart failure, Machine learning, Telemedicine, Text mining",
author = "Alphons Eggerth and Karl Kreiner and Dieter Hayn and Bernhard Pfeifer and Gerhard P{\"o}lzl and Tim Egelseer-Br{\"u}ndl and G{\"u}nter Schreier",
year = "2020",
month = jun,
day = "16",
doi = "10.3233/SHTI200263",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "761--765",
editor = "Pape-Haugaard, {Louise B.} and Christian Lovis and Madsen, {Inge Cort} and Patrick Weber and Nielsen, {Per Hostrup} and Philip Scott",
booktitle = "Digital Personalized Health and Medicine - Proceedings of MIE 2020",
address = "Netherlands",
note = "30th Medical Informatics Europe Conference, MIE 2020 ; Conference date: 28-04-2020 Through 01-05-2020",
}