{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T08:13:37Z","timestamp":1784276017387,"version":"3.55.0"},"reference-count":54,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100005089","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["7244285"],"award-info":[{"award-number":["7244285"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Capital Medical University Research and Cultivation Foundation","award":["PYZ23028"],"award-info":[{"award-number":["PYZ23028"]}]},{"name":"The National Key Research and Development Project of China","award":["2017YFC1309204"],"award-info":[{"award-number":["2017YFC1309204"]}]},{"name":"Capital Medical University Basic Clinical Collaborative Research Project on Digital Intelligence Nursing","award":["SZHL23Q01"],"award-info":[{"award-number":["SZHL23Q01"]}]},{"name":"Capital Medical University Basic Clinical Collaborative Research Project on Digital Intelligence Nursing","award":["SZHL23Q08"],"award-info":[{"award-number":["SZHL23Q08"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clin Appl Thromb Hemost"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The purpose of this study is to develop and validate an improved CA-VTE risk prediction model based on semi-supervised learning (SSL) algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study used a combined retrospective and prospective cohort design. First, data from 2100 cancer patients in a tertiary hospital in Beijing were retrospectively collected, including a \u201clabeled cohort\u201d with CA-VTE outcomes (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u20091036) and an \u201cunlabeled cohort\u201d without outcomes (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u20091064). Then, another dataset were prospectively collected as an external validation set (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u2009321). Eight supervised machine learning (ML) algorithms were used to develop CA-VTE risk prediction models and one SSL algorithm was used to improve generalizability of the models (pre- and post-imputation ML models). Model performance were evaluated using the Area Under the Curve (AUC) and Brier score in the prospective cohort, and compare them with the Khorana score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The eight post-imputation ML models (AUC: 0.816-0.868; Brier score: 0.118-0.160) performed better on the external validation set than the pre-imputation models (AUC: 0.798-0.841; Brier score: 0.133-0.171). In contrast, the AUC of the Khorana score remained unchanged (AUC: 0.693), while its Brier score increased (Brier score: 0.172 vs 0.178).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Based on a retrospective and prospective cohort study design, this study developed eight ML models that outperformed the Khorana score. Using SSL algorithm improved the external validation performance of the models and enhanced prediction accuracy. This study can provide an important reference for the early identification of high-risk factors and stratified preventive care for CA-VTE.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10760296261416914","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T07:48:03Z","timestamp":1769500083000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction Models"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2469-0563","authenticated-orcid":false,"given":"Shuai","family":"Jin","sequence":"first","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Wang","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Qin","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baosheng","family":"Liang","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Nursing, Hebei University, Baoding, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyin","family":"Gao","sequence":"additional","affiliation":[{"name":"Second Hospital of Shanxi Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benqiang","family":"Rao","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanping","family":"Shi","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihui","family":"Liu","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Lu","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-022-00336-y"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16020458"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2024.0046"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2020007338"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaccao.2023.10.007"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.3949\/ccjm.91a.23017"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2007-10-116327"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2010-02-270116"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11739-012-0784-y"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0033-1349608"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2016-0246"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2016-0414"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/s2352-3026(18)30063-2"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41416-018-0027-8"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00520-019-05150-z"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13045-022-01259-7"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10147-022-02257-y"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2024.104867"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.thromres.2024.01.003"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.thromres.2018.01.036"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.4081\/btvb.2024.123"},{"key":"e_1_3_3_23_2","first-page":"436","article-title":"Learning to predict post-hospitalization VTE risk from EHR data","volume":"2012","author":"Kawaler E","year":"2012","unstructured":"Kawaler E, Cobian A, Peissig P, et al. 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