{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T05:59:24Z","timestamp":1763445564882},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining\/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72\u00a0h after hospital admission.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The process mining\/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72\u00a0h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The proposed process mining\/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our proposed process mining\/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately.\n<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01934-2","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T09:15:43Z","timestamp":1658740543000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID\u201019 patients"],"prefix":"10.1186","volume":"22","author":[{"given":"M.","family":"Pishgar","sequence":"first","affiliation":[]},{"given":"S.","family":"Harford","sequence":"additional","affiliation":[]},{"given":"J.","family":"Theis","sequence":"additional","affiliation":[]},{"given":"W.","family":"Galanter","sequence":"additional","affiliation":[]},{"given":"J. M.","family":"Rodr\u00edguez-Fern\u00e1ndez","sequence":"additional","affiliation":[]},{"given":"L. H","family":"Chaisson","sequence":"additional","affiliation":[]},{"given":"Y.","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"A.","family":"Trotter","sequence":"additional","affiliation":[]},{"given":"K. M.","family":"Kochendorfer","sequence":"additional","affiliation":[]},{"given":"A.","family":"Boppana","sequence":"additional","affiliation":[]},{"given":"H.","family":"Darabi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"issue":"1","key":"1934_CR1","doi-asserted-by":"publisher","first-page":"26094","DOI":"10.1038\/srep26094","volume":"6","author":"R Miotto","year":"2016","unstructured":"Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. 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Permission from University of Illinois at Chicago Privacy Board and Internal Review Board were required to access the data used in this study. All the experiment protocols involving human data were in accordance with the University of Illinois at Chicago Privacy Board and Internal Review Board guidelines. Our research was provided a waiver of informed consent, parental permission and assent from the University of Illinois at Chicago IRB granted under 45 CFR 46.116(f).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"194"}}