Classification of Clinical Notes from a Heart Failure Telehealth Network...33rd Medical Informatics Europe Conference (MIE2023), May 22-25, 2023, Gothenburg, Sweden
Heart failure is a common chronic disease which is associated with high re-hospitalization and mortality rates. Within the telemedicine-assisted transitional care disease management program HerzMobil, monitoring data such as daily measured vital parameters and various other heart failure related dat...
| Published in: | Studies in Health Technology & Informatics Vol. 302; pp. 803 - 808 |
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| Main Authors: | , , , , , , , , , , |
| Format: | proceedings research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163842291&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163842291 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 302 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 163842291 163842291 163842291 10.3233/SHTI230270 163842291 ppf: 803 ppct: 5 formats: tig: atl: Classification of Clinical Notes from a Heart Failure Telehealth Network...33rd Medical Informatics Europe Conference (MIE2023), May 22-25, 2023, Gothenburg, Sweden aug: au: WIESMÜLLER, Fabian LAUSCHENSKI, Aaron BAUMGARTNER, Martin HAYN, Dieter KREINER, Karl FETZ, Bettina BRUNELLI, Luca PÖLZL, Gerhard PFEIFER, Bernhard NEURURER, Sabrina SCHREIER, Günter affil: Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria sug: subj: Heart Failure Mortality Documentation Natural Language Processing Machine Learning Telehealth Transitional Care Disease Management Program Evaluation Human Congresses and Conferences Sweden Sweden Physicians Nurses Interrater Reliability Algorithms kappa Statistic Descriptive Statistics ab: Heart failure is a common chronic disease which is associated with high re-hospitalization and mortality rates. Within the telemedicine-assisted transitional care disease management program HerzMobil, monitoring data such as daily measured vital parameters and various other heart failure related data are collected in a structured way. Additionally, involved healthcare professionals communicate with one another via the system using free-text clinical notes. Since manual annotation of such notes is too time-consuming for routine care applications, an automated analysis process is needed. In the present study, we established a ground truth classification of 636 randomly selected clinical notes from HerzMobil based on annotations of 9 experts with different professional background (2 physicians, 4 nurses, and 3 engineers). We analyzed the influence of the professional background on the inter annotator reliability and compared the results with the accuracy of an automated classification algorithm. We found significant differences depending on the profession and on the category. These results indicate that different professional backgrounds should be considered when selecting annotators in such scenarios. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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