{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:03:39Z","timestamp":1790251419061,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP25K02851"],"award-info":[{"award-number":["JP25K02851"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Geriatrics Gerontology Int"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To develop and internally validate a machine learning\u2013based risk scorecard for 1\u2010year pneumonia hospitalization among community\u2010dwelling Japanese adults aged \u2265\u200975\u2009years using routinely collected frailty screening and claims data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study of 1\u2009098\u2009404 community\u2010dwelling adults aged \u2265\u200975\u2009years who completed the Questionnaire for Medical Checkup of Old\u2010Old (QMCOO) between April 2020 and March 2024, using the Late\u2010Stage Medical Care System claims database. Data were split into training (70%) and test (30%) sets. A Super Learner ensemble of 20 base learners was developed to predict 1\u2010year pneumonia hospitalization (ICD\u201010: J12\u2013J18, J69). An independent 17\u2010feature point\u2010based scorecard (0\u201321 points) was derived from the training set with Platt calibration. Performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration slope, and calibration\u2010in\u2010the\u2010large.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 1\u2009098\u2009404 participants (mean age 80.6 [SD 5.0] years; 40.3% male), 4525 (0.41%) experienced pneumonia hospitalization. On the test set, the Super Learner achieved an AUC of 0.823 (95% CI, 0.812\u20130.834) and the scorecard 0.786 (0.774\u20130.798), which showed good calibration (slope 1.017; calibration\u2010in\u2010the\u2010large \u22120.001). Stratification into low (0\u20136 points; 55.6%, 0.12% event rate), moderate (7\u201310; 35.4%, 0.49%), and high (\u2265\u200911; 9.0%, 1.91%) groups yielded a 15.9\u2010fold risk gradient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A QMCOO\u2010based risk scorecard showed good discrimination and calibration for predicting 1\u2010year pneumonia hospitalization in this population. If externally validated, this tool may help identify high\u2010risk individuals during routine health checkups and support targeted preventive assessment in primary care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ggi.70850","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:03:54Z","timestamp":1790247834000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning\u2013Derived Risk Scorecard for Pneumonia Hospitalization in Japanese Old\u2010Old Adults"],"prefix":"10.1111","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-1953","authenticated-orcid":false,"given":"Akio","family":"Shimizu","sequence":"first","affiliation":[{"name":"Department of Rehabilitation Medicine Mie University Hospital  Tsu 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