{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:12:20Z","timestamp":1772251940774,"version":"3.50.1"},"reference-count":10,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>In order to address a long standing challenge for internal medicine physicians we developed artificial intelligence (AI) models to identify patients at risk of increased mortality. After querying 2,425 records of patients transferred from non-intensive care units to intensive care units from the Veteran Affairs Corporate Data Warehouse (CDW), we created two datasets. The former used 22 independent variables that included \u201cLength of Hospital Stay\u201d and \u201cDays to Intensive Care Transfer,\u201d and the latter lacked these two variables. Since these two variables are unknown at the time of admission, the second set is more clinically relevant. We trained 16 machine learning models using both datasets. The best-performing models were fine-tuned and evaluated. The LightGBM model achieved the best results for both datasets. The model trained with 22 variables achieved a Receiver Operating Characteristics Curve-Area Under the Curve (ROC-AUC) of 0.89 and an accuracy of 0.72, with a sensitivity of 0.97 and a specificity of 0.68. The model trained with 20 variables achieved a ROC-AUC of 0.86 and an accuracy of 0.71, with a sensitivity of 0.94 and a specificity of 0.67. The top features for the former model included \u201cTotal length of Stay,\u201d \u201cAdmit to ICU Transfer Days,\u201d and \u201cLymphocyte Next Lab Value.\u201d For the latter model, the top features included \u201cLymphocyte First Lab Value,\u201d \u201cHemoglobin First Lab Value,\u201d and \u201cHemoglobin Next Lab Value.\u201d Our clinically relevant predictive mortality model can assist providers in optimizing resource utilization when managing large caseloads, particularly during shift changes.<\/jats:p>","DOI":"10.3389\/frai.2023.1191320","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T06:09:06Z","timestamp":1690956546000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting ward transfer mortality with machine learning"],"prefix":"10.3389","volume":"6","author":[{"given":"Jose L.","family":"Lezama","sequence":"first","affiliation":[]},{"given":"Gil","family":"Alterovitz","sequence":"additional","affiliation":[]},{"given":"Colleen E.","family":"Jakey","sequence":"additional","affiliation":[]},{"given":"Ana L.","family":"Kraus","sequence":"additional","affiliation":[]},{"given":"Michael J.","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Andrew A.","family":"Borkowski","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1038\/s41591-022-01894-0","article-title":"Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis","volume":"28","author":"Adams","year":"2022","journal-title":"Nat. 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Sci."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1097\/CCM.0000000000003342","article-title":"Epidemiology and costs of sepsis in the united states-an analysis based on timing of diagnosis and severity level","volume":"46","author":"Paoli","year":"2018","journal-title":"Crit. Care Med."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-03078-2","article-title":"A community approach to mortality prediction in sepsis via gene expression analysis","volume":"9","author":"Sweeney","year":"2018","journal-title":"Nat. Commun."},{"key":"B10","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1093\/jamia\/ocab236","article-title":"Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review","volume":"29","author":"Yan","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1191320\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T06:09:09Z","timestamp":1690956549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1191320\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,2]]},"references-count":10,"alternative-id":["10.3389\/frai.2023.1191320"],"URL":"https:\/\/doi.org\/10.3389\/frai.2023.1191320","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.01.06.23284285","asserted-by":"object"}]},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,2]]},"article-number":"1191320"}}