A Vision on User-Centered Implementation and Evaluation of Explainable AI for Predicting Hospital-Onset Bacteremia...Medical Informatics Europe (MIE) 34th Conference, August 25–29, 2024, Athens, Greece.
In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algorithm arrives at a decision. In our research proje...
| Publicado en: | Studies in Health Technology & Informatics Vol. 316; pp. 766 - 771 |
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| Autores principales: | , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| 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=179286357&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179286357 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 316 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179286357 179286357 179286357 10.3233/SHTI240525 179286357 ppf: 766 ppct: 5 formats: tig: atl: A Vision on User-Centered Implementation and Evaluation of Explainable AI for Predicting Hospital-Onset Bacteremia...Medical Informatics Europe (MIE) 34th Conference, August 25–29, 2024, Athens, Greece. aug: au: HOOGESTRAAT, Anna Thalea WULFF, Antje affil: Big Data in Medicine, Department of Health Services Research, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany. sug: subj: Implementation Science Machine Learning Cross Infection Risk Factors Bacteremia Risk Factors Risk Assessment Human Congresses and Conferences Greece Greece Artificial Intelligence Decision Support Systems, Clinical Algorithms Questionnaires Interviews Academic Medical Centers Usability Study Multimethod Studies ab: In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algorithm arrives at a decision. In our research project, machine learning methods are used for individual risk prediction of hospital-onset bacteremia (HOB). This paper presents a vision on a step-wise process for implementation and evaluation of user-centered XAI for risk prediction of HOB. An initial requirement analysis revealed first insights on the users' needs of explainability to use and trust such risk prediction applications. The findings were then used to propose step-wise process towards a user-centered evaluation. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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