{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T09:23:21Z","timestamp":1789809801065,"version":"4.0.1"},"reference-count":32,"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"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["7242264"],"award-info":[{"award-number":["7242264"]}]},{"name":"the R&D Program of Beijing Municipal Education Commission","award":["KM202310025020"],"award-info":[{"award-number":["KM202310025020"]}]}],"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>Objective<\/jats:title>\n                    <jats:p>To refine prognostic stratification for intermediate-risk acute myeloid leukemia (IR-AML) by leveraging machine learning to integrate clinical and genomic features and relate them to survival outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a two-cohort study comprising a single-center development cohort from Beijing Tsinghua Changgung Hospital (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u200956) and an independent external cohort from The Cancer Genome Atlas (TCGA;\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u200979). Demographics and mutational profiles were analyzed alongside survival outcomes. We developed three tree-based models\u2014random forests, gradient-boosted decision trees (GBDT), and XGBoost\u2014on the Tsinghua Changgung cohort, using stratified five-fold cross-validation for internal validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In internal cross-validation, tree-based learners showed strong discrimination (best GBDT AUROC 0.98, 95% confidence interval (CI) 0.91\u20131.00). On the external TCGA cohort, GBDT achieved AUROC 0.73 (95% CI 0.62\u20130.83). Model-agnostic explanations (Shapley additive explanations) consistently highlighted white blood cell count, age, transplantation, and\n                      <jats:italic toggle=\"yes\">TET2<\/jats:italic>\n                      among top contributors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An interpretable machine learning framework built from accessible clinical and genomic variables provided quantitative risk discrimination for IR-AML across development and external test cohorts, supporting individualized risk assessment and informing refinement of prognostic stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251404525","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T14:52:12Z","timestamp":1765205532000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Construction of a prognostic hierarchical model of intermediate-risk acute myeloid leukemia based on machine learning"],"prefix":"10.1177","volume":"11","author":[{"given":"Bianhong","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Hematology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2216-4953","authenticated-orcid":false,"given":"Ziqi","family":"Li","sequence":"additional","affiliation":[{"name":"Capital Medical University"},{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixue","family":"Niu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3061-3652","authenticated-orcid":false,"given":"Yingliang","family":"Yang","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congmin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2963-5248","authenticated-orcid":false,"given":"Honglei","family":"Liu","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n2026"},{"key":"e_1_3_2_3_2","first-page":"38","article-title":"Acute myeloid leukemia: a review","volume":"103","author":"Pelcovits A","year":"2020","unstructured":"Pelcovits A, Niroula R. Acute myeloid leukemia: a review. 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Prognostic value of European LeukemiaNet 2022 criteria and genomic clusters using machine learning in older adults with acute myeloid leukemia. Haematologica 2024; 109: 1095\u20131106.","journal-title":"Haematologica"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.leukres.2024.107437"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2021-145122"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2022016867"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtct.2024.08.011"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2019-125780"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2024.114338"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2019-122804"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2023010477"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtct.2023.03.033"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41375-025-02634-2"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2021011103"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.humpath.2012.05.003"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2013.236"},{"key":"e_1_3_2_28_2","volume-title":"Revisiting deep learning models for tabular dataNeural information processing systems","author":"Gorishniy YV","unstructured":"Gorishniy YV, Rubachev I, Khrulkov V, et al. 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Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study in France. Lancet Dig Health 2024; 6: e323\u2013e333.","journal-title":"Lancet Dig Health"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0103-9"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076251404525","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076251404525","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076251404525","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:55:23Z","timestamp":1777398923000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076251404525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":32,"alternative-id":["10.1177\/20552076251404525"],"URL":"https:\/\/doi.org\/10.1177\/20552076251404525","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]},"article-number":"20552076251404525"}}