{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T23:46:37Z","timestamp":1777851997371,"version":"3.51.4"},"reference-count":53,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant No. 72371116"],"award-info":[{"award-number":["Grant No. 72371116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK116986"],"award-info":[{"award-number":["R01DK116986"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF Smart and Connected Health award","award":["2014554"],"award-info":[{"award-number":["2014554"]}]},{"name":"Major Research Plan of the National Natural Science Foundation of China","award":["91746204"],"award-info":[{"award-number":["91746204"]}]},{"name":"Science and Technology Development in Guangdong Province","award":["2017B030308008"],"award-info":[{"award-number":["2017B030308008"]}]},{"name":"Guangdong Engineering Technology Research Center for Big Data Precision Healthcare","award":["603141789047"],"award-info":[{"award-number":["603141789047"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023A04J0360"],"award-info":[{"award-number":["2023A04J0360"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Complex diseases, like diabetic kidney disease (DKD), often exhibit heterogeneity, challenging accurate risk prediction with machine learning. Traditional global models ignore patient differences, and subgroup learning lacks interpretability and predictive efficiency. This study introduces the Interpretable Subgroup Learning-based Modeling (iSLIM) framework to address these issues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>iSLIM integrates expert knowledge with a tree-based recursive partitioning approach to identify DKD subgroups within an EHR dataset of 11,559 patients. It then constructs separate models for each subgroup, enhancing predictive accuracy while preserving interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Five clinically relevant subgroups are identified, achieving an average sensitivity of 0.8074, outperforming a single global model by 0.1104. Post hoc analyses provide pathological and biological evidence supporting subgroup validity and potential DKD risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The iSLIM surpasses traditional global model in predictive performance and subgroup-specific risk factor interpretation, enhancing the understanding of DKD\u2019s heterogeneous mechanisms and potentially increasing the adoption of machine learning models in clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/14604582241291379","type":"journal-article","created":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T08:34:33Z","timestamp":1729326873000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable subgroup learning-based modeling framework: Study of diabetic kidney disease prediction"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9709-3546","authenticated-orcid":false,"given":"Bo","family":"Liu","sequence":"first","affiliation":[{"name":"Big Data Decision Institute, Jinan University, Guangzhou, China"},{"name":"School of Management, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangzhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Big Data Decision Institute, Jinan University, Guangzhou, China"},{"name":"School of Medicine, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Liu","sequence":"additional","affiliation":[{"name":"Big Data Decision Institute, Jinan University, Guangzhou, China"},{"name":"School of Management, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhou","family":"Hu","sequence":"additional","affiliation":[{"name":"Big Data Decision Institute, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric W. 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