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This study aimed to develop machine learning (ML) models for risk stratification of infection\u2010related hospitalisations (IRHs) among patients with cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Adult patients diagnosed with and treated for lymphoma, multiple myeloma (MM), chronic lymphocytic leukaemia (CLL), colorectal, or lung cancer from 2013 to 2023 in the North Denmark Region were included. Data from national cancer registries and patient health records were used. Serious infections were defined as sepsis, positive blood culture, intensive care admission, or death during hospitalisation. Models were developed using multiple ML methodologies across three different data setups:\n                      <jats:italic>ML<\/jats:italic>\n                      \u2014using data up to hospital admission;\n                      <jats:italic>ML<\/jats:italic>\n                      <jats:sub>48<\/jats:sub>\n                      <jats:italic>\u2014<\/jats:italic>\n                      using data up to 48\u2009h after admission; and\n                      <jats:italic>ML<\/jats:italic>\n                      <jats:sub>48,reduced<\/jats:sub>\n                      \u2014a pruned version of\n                      <jats:italic>ML<\/jats:italic>\n                      <jats:sub>48<\/jats:sub>\n                      with fewer features. Data were split into training, validation and held\u2010out test sets.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 9874 patients 2661 IRHs occurred, including 691 (21.5%) serious infections. On the held\u2010out test set,\n                      <jats:italic>ML<\/jats:italic>\n                      achieved the best performance (ROC\u2010AUC: 0.79) versus\n                      <jats:italic>ML<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>48<\/jats:italic>\n                      <\/jats:sub>\n                      (0.76), with ML\n                      <jats:sub>48<\/jats:sub>\n                      having higher specificity at matched sensitivity (50.9% vs 44.9% for sensitivity ~85%).\n                      <jats:italic>ML<\/jats:italic>\n                      <jats:sub>48,reduced<\/jats:sub>\n                      reached a ROC\u2010AUC of 0.75 (specificity 46.5% at sensitivity ~85%). Subgroup analysis showed the best performance for lymphoma (ROC\u2010AUC: 0.86) and lowest for lung cancer (ROC\u2010AUC: 0.73). The model performed better in patients with ECOG score 0\u20131 compared to \u2265\u20092 (ROC\u2010AUC 0.88 vs. 0.77). SHAP analysis identified previous blood cultures, carbamide, and neutrophil counts as key predictive features across all models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ML models showed moderate performance in risk stratification of IRHs in patients with cancer. Such models could provide decision support in hospitalisation decisions and discharge.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72116","type":"journal-article","created":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T20:30:31Z","timestamp":1785097831000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for Prediction of High\u2010Risk Infections in Patients With Cancer"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3879-1950","authenticated-orcid":false,"given":"Alexander Djupnes","family":"Fuglkj\u00e6r","sequence":"first","affiliation":[{"name":"Department of Materials and Production Aalborg University  Aalborg Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0807-9352","authenticated-orcid":false,"given":"Mathias Holmsgaard","family":"Eskesen","sequence":"additional","affiliation":[{"name":"Department of Oncology, Clinical Research Centre Aalborg University Hospital  Aalborg Denmark"},{"name":"Department of Clinical Medicine Aalborg University Hospital  Aalborg Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikkel","family":"Werling","sequence":"additional","affiliation":[{"name":"Department of Haematology Copenhagen University Hospital  Copenhagen Denmark"},{"name":"Danish Cancer Institute Danish Cancer Society  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1119-4782","authenticated-orcid":false,"given":"Mikkel Runason","family":"Simonsen","sequence":"additional","affiliation":[{"name":"Department of Haematology, Clinical Cancer Research Centre Aalborg University Hospital  Aalborg Denmark"},{"name":"Department of Mathematical Sciences Aalborg University  Aalborg 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