{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T23:15:02Z","timestamp":1785366902659,"version":"3.55.0"},"reference-count":38,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early detection of postsurgical complications is critical for improving surgical outcomes, yet current monitoring methods are invasive, time-consuming, and may delay intervention. Advances in machine learning (ML) and artificial intelligence (AI) enable using real-time data, such as accelerometer-derived physical activity, as potential early warning signs. This exploratory study evaluated whether activity trends can predict postsurgical complications in oncology patients using ML models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Usual care data from a surgical oncology ward (October 2020\u2013December 2024) were analyzed. Three classifiers were evaluated\u2014Random Forest (RF), eXtreme Gradient Boosting (XGB), and Logistic Regression (LR)\u2014within a nested cross-validation framework. Two modeling strategies were compared: (1) training\/testing without undersampling and (2) training with undersampling at varying factors to balance complication versus noncomplication days. Models were assessed for next-day complication prediction using area under the ROC curve (AUC), precision, recall, and F1-score with bootstrap confidence intervals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Data were collected from 965 patients, of whom 189 were included. The best performance for RF was observed at an undersampling factor of 1 (AUC = 0.66, 95% confidence interval (CI) 0.64\u20130.67; recall = 0.63, 95% CI 0.27\u20130.91; precision = 0.05, 95% CI 0.03\u20130.07). LR achieved its highest AUC without undersampling (0.68, 95% CI 0.67\u20130.69), while XGB performed consistently lower (AUC \u2248 0.63\u20130.64).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This exploratory study showed that postoperative activity trends alone were insufficient to predict complications after major oncological surgery. Combining accelerometer, physiological, and laboratory data may improve predictive accuracy and overall clinical value in perioperative care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251408520","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T17:10:42Z","timestamp":1766423442000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Physical activity trends as predictors of postoperative complications in oncology patients: A machine learning approach"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1196-276X","authenticated-orcid":false,"given":"Carlos","family":"de Miguel Llorente","sequence":"first","affiliation":[{"name":"University Medical Center Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5306-6797","authenticated-orcid":false,"given":"Sjoerd","family":"de Vries","sequence":"additional","affiliation":[{"name":"University Medical Center Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8494-349X","authenticated-orcid":false,"given":"Petra","family":"Bor","sequence":"additional","affiliation":[{"name":"University Medical Center Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Veerhoek","sequence":"additional","affiliation":[{"name":"University Medical Center Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan Willem","family":"Van de Berg","sequence":"additional","affiliation":[{"name":"University Medical Center Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Meijer","sequence":"additional","affiliation":[{"name":"University Medical Center Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0970-2896","authenticated-orcid":false,"given":"Cindy","family":"Veenhof","sequence":"additional","affiliation":[{"name":"University Medical Center Utrecht"},{"name":"HU University of Applied Sciences Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4715-685X","authenticated-orcid":false,"given":"Karin","family":"Valkenet","sequence":"additional","affiliation":[{"name":"University Medical Center Utrecht"},{"name":"HU University of Applied Sciences Utrecht"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.sla.0000133083.54934.ae"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12955-014-0194-0"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12893-023-02171-8"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12871-018-0516-6"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-020-01510-1"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/jso.26910"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-012-2498-9"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/jocn.13648"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2015.12.007"},{"key":"e_1_3_3_11_2","article-title":"Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology","volume":"369","author":"Gerry S","year":"2020","unstructured":"Gerry S, Bonnici T, Birks J, et al. 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