{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:36:59Z","timestamp":1767965819458,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682846","type":"print"},{"value":"9781643682853","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,25]]},"abstract":"<jats:p>Burnout in healthcare professionals (HCPs) is a multi-factorial problem. There are limited studies utilizing machine learning approaches to predict HCPs\u2019 burnout during the COVID-19 pandemic. A survey consisting of demographic characteristics and work system factors was administered to 450 HCPs during the pandemic (participation rate: 59.3%). The highest performing machine learning model had an area under the receiver operating curve of 0.81. The eight key features that best predicted burnout are excessive workload, inadequate staffing, administrative burden, professional relationships, organizational culture, values and expectations, intrinsic motivation, and work-life integration. These findings provide evidence for resource allocation and implementation of interventions to reduce HCPs\u2019 burnout and improve the quality of care.<\/jats:p>","DOI":"10.3233\/shti220396","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:12:38Z","timestamp":1653480758000},"source":"Crossref","is-referenced-by-count":8,"title":["Using Explainable Supervised Machine Learning to Predict Burnout in Healthcare Professionals"],"prefix":"10.3233","author":[{"given":"Karthik","family":"Adapa","sequence":"first","affiliation":[{"name":"Carolina Health Informatics Program, University of North Carolina (UNC), Chapel Hill, USA"},{"name":"Division of Healthcare Engineering, Department of Radiation Oncology, School of Medicine, UNC, Chapel Hill, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malvika","family":"Pillai","sequence":"additional","affiliation":[{"name":"Carolina Health Informatics Program, University of North Carolina (UNC), Chapel Hill, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meagan","family":"Foster","sequence":"additional","affiliation":[{"name":"Carolina Health Informatics Program, University of North Carolina (UNC), Chapel Hill, USA"},{"name":"Division of Healthcare Engineering, Department of Radiation Oncology, School of Medicine, UNC, Chapel Hill, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadia","family":"Charguia","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, School of Medicine, UNC, Chapel Hill, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukasz","family":"Mazur","sequence":"additional","affiliation":[{"name":"Carolina Health Informatics Program, University of North Carolina (UNC), Chapel Hill, USA"},{"name":"Division of Healthcare Engineering, Department of Radiation Oncology, School of Medicine, UNC, Chapel Hill, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220396","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:12:39Z","timestamp":1653480759000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"ISBN":["9781643682846","9781643682853"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220396","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}