{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:34:39Z","timestamp":1773002079978,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":54,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81800613"],"award-info":[{"award-number":["81800613"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071098"],"award-info":[{"award-number":["62071098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2023YFSY0027"],"award-info":[{"award-number":["2023YFSY0027"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2021YFG0307"],"award-info":[{"award-number":["2021YFG0307"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2022YFG0319"],"award-info":[{"award-number":["2022YFG0319"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Model drift is a major challenge for applications of clinical prediction models. We aimed to investigate the effect of two strategies to mitigate model drift based on a previously reported prediction model for acute kidney injury (AKI).\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    Deidentified electronic medical data of inpatients in Sichuan Provincial People\u2019s Hospital from January 1, 2019, to December 31, 2022, were collected. AKI was defined by the KDIGO criteria. The top 50 laboratory variables, alongside with sex, age, and the top 20 prescribed medicines were included as predictive variables. In model optimization, the convolution neural network module was replaced by a self\u2010attention module. Periodical refitting with accumulative data was also conducted before temporally external validations. The performance of the innovated model (ATRN) was compared with the previous model (ATCN) and other four models.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    A total of 150,373 admissions were identified. The annual incidences of AKI varied between 5.57% and 5.8%. The performance of the models which had used temporal features profoundly declined over time. The ATRN model with module more suitable to capture short\u2010term time dependencies outperformed the other five models both in C\u2010statistics and recall rates perspectives. Periodic refitting the prediction model with accumulative data also helped to effectively mitigate the model drift, especially in models with time series data.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    Enhancing the model\u2019s ability to capture short\u2010term time dependencies in time series data and periodic refitting with accumulative data were both capable of mitigating the model drift. The best improvement of model performance was observed in the combination of these two strategies.\n                  <\/jats:p>","DOI":"10.1155\/int\/2240862","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T05:41:13Z","timestamp":1741758073000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Strategies to Mitigate Model Drift of a Machine Learning Prediction Model for Acute Kidney Injury in General Hospitalization"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6632-7629","authenticated-orcid":false,"given":"Jie","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1970-9979","authenticated-orcid":false,"given":"Guisen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Mi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9187-6187","authenticated-orcid":false,"given":"Martin","family":"Gallagher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4393-6661","authenticated-orcid":false,"given":"Yunlin","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1681\/asn.2017070765"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.13359"},{"key":"e_1_2_13_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107660"},{"key":"e_1_2_13_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107673"},{"key":"e_1_2_13_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107679"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2018.02.12"},{"key":"e_1_2_13_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2020.3042646"},{"key":"e_1_2_13_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41580-021-00407-0"},{"key":"e_1_2_13_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1390-1"},{"key":"e_1_2_13_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2022.104729"},{"key":"e_1_2_13_11_2","first-page":"625","article-title":"Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality","volume":"2017","author":"Davis S. E.","year":"2017","journal-title":"AMIA Annual Symposium Proceedings"},{"key":"e_1_2_13_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2015.04.005"},{"key":"e_1_2_13_13_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.1751"},{"key":"e_1_2_13_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00395"},{"key":"e_1_2_13_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2020.3032060"},{"key":"e_1_2_13_16_2","doi-asserted-by":"crossref","unstructured":"ElsaharH.andGall\u00e9M. To Annotate or Not? Predicting Performance Drop Under Domain Shift Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing 2019 EMNLP-IJCNLP 2163\u20132173.","DOI":"10.18653\/v1\/D19-1222"},{"key":"e_1_2_13_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.72"},{"key":"e_1_2_13_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108632"},{"key":"e_1_2_13_19_2","unstructured":"DavisS. E. LaskoT. A. ChenG. andMathenyM. E. Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality 2017: American Medical Informatics Association 2017 625\u2013634."},{"key":"e_1_2_13_20_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocz127"},{"key":"e_1_2_13_21_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocx030"},{"key":"e_1_2_13_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.05.035"},{"key":"e_1_2_13_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00116900"},{"key":"e_1_2_13_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3122531"},{"key":"e_1_2_13_25_2","doi-asserted-by":"publisher","DOI":"10.1186\/cc11454"},{"key":"e_1_2_13_26_2","article-title":"Attention is All You Need","volume":"30","author":"Vaswani A.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_13_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2018.2858826"},{"key":"e_1_2_13_28_2","unstructured":"Van RossumG.andDrakeF. L. Python Reference Manual: Centrum Voor Wiskunde En Informatica Amsterdam 1995."},{"key":"e_1_2_13_29_2","unstructured":"R Core Team R: A Language and Environment for Statistical Computing 2013."},{"key":"e_1_2_13_30_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0199839"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/2240862","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/int\/2240862","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/2240862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T18:10:38Z","timestamp":1772993438000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/int\/2240862"}},"subtitle":[],"editor":[{"given":"Eugenio","family":"Vocaturo","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/int\/2240862"],"URL":"https:\/\/doi.org\/10.1155\/int\/2240862","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-08-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2240862"}}