Predicting Objective Performance Using Perceived Cognitive Workload Data in Healthcare Professionals: A Machine Learning Study...18th World Congress of Medical and Health Informatics, MedInfo 2021 - One World, One Health – Global Partnership for Digital Innovation, 2-4 October, 2021.
Cognitive Workload (CWL) is a fundamental concept in predicting healthcare professionals' (HCPs) objective performance. The study aims to compare the accuracy of the classical model (utilizes all six dimensions of the National Aeronautics and Space Administration Task Load Index (NASA-TLX)) and nove...
| Publicado en: | Studies in Health Technology & Informatics Vol. 290; pp. 809 - 814 |
|---|---|
| Autores principales: | , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157572066&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157572066 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 290 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157572066 157572066 157572066 10.3233/SHTI220191 157572066 ppf: 809 ppct: 5 formats: tig: atl: Predicting Objective Performance Using Perceived Cognitive Workload Data in Healthcare Professionals: A Machine Learning Study...18th World Congress of Medical and Health Informatics, MedInfo 2021 - One World, One Health – Global Partnership for Digital Innovation, 2-4 October, 2021. aug: au: Adapa, Karthik Pillai, Malvika Das, Shiva Mosaly, Prithima Mazur, Lukasz affil: Department of Radiation Oncology, School of Medicine, UNC-Chapel Hill, NC, USA sug: subj: Health Personnel Workload Cognition Task Performance and Analysis Machine Learning Methods Human Congresses and Conferences Ergonomics Data Analytics Self Report Descriptive Statistics Models, Statistical kappa Statistic ab: Cognitive Workload (CWL) is a fundamental concept in predicting healthcare professionals' (HCPs) objective performance. The study aims to compare the accuracy of the classical model (utilizes all six dimensions of the National Aeronautics and Space Administration Task Load Index (NASA-TLX)) and novel models (utilize four or five dimensions of NASA-TLX) in predicting HCPs' objective performance. We use a dataset from our previous human factors research studies and apply a broad selection of supervised machine learning classification techniques to develop data-driven computational models and predict objective performance. The study findings confirm that classical models are better predictors of objective performance than novel models. This has practical implications for research in health informatics, human factors and ergonomics, and human-computer interaction in healthcare. Findings, although promising, cannot be generalized as they are based on a small dataset. Future studies may investigate additional subjective and physiological measures of CWL to predict HCPs' objective performance. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|