{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:34:10Z","timestamp":1781249650383,"version":"3.54.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:00:00Z","timestamp":1773446400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Guangdong Natural Science Foundation General Project","award":["2024A1515012112"],"award-info":[{"award-number":["2024A1515012112"]}]},{"name":"Guangdong Medical Research Fund Project","award":["A2024044"],"award-info":[{"award-number":["A2024044"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Fund of China","doi-asserted-by":"crossref","award":["82302462"],"award-info":[{"award-number":["82302462"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guang Dong Basic and Applied Basic Research Foundation","award":["2022A1515111206"],"award-info":[{"award-number":["2022A1515111206"]}]},{"name":"National Key R&D Program of China-Intergovernmental Key Projects","award":["2023YFE0114300"],"award-info":[{"award-number":["2023YFE0114300"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02522-8","type":"journal-article","created":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T14:43:51Z","timestamp":1773499431000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction"],"prefix":"10.1038","volume":"9","author":[{"given":"Lijuan","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyi","family":"Mai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongnian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinrong","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyun","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Khalemsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijaya Arun","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James H.","family":"Paxton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dionyssios","family":"Tsilimingras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said","family":"Hachimi-Idrissi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan W.","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Savioli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niels K.","family":"Rathlev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karim","family":"Tazarourte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Slagman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Christ","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Qureshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani","family":"Hariri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shamai A.","family":"Grossman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phillip D.","family":"Levy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian J.","family":"O\u2019Neil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seth","family":"Gemme","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa","family":"Kurland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eddy","family":"Lang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinle","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiying","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelouahab","family":"Bellou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,14]]},"reference":[{"key":"2522_CR1","first-page":"168","volume":"36","author":"MO Tamara","year":"2024","unstructured":"Tamara, M. O. et al. Mortality after emergency department discharge: an analysis of 453599 cases. Emergencias 36, 168\u2013178 (2024).","journal-title":"Emergencias"},{"key":"2522_CR2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-03048-x","volume":"25","author":"S Yuan","year":"2025","unstructured":"Yuan, S., Yang, Z., Li, J., Wu, C. & Liu, S. AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis. BMC Med. Inform. Decis. Mak. 25, 203 (2025).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"2522_CR3","doi-asserted-by":"publisher","first-page":"e2516400","DOI":"10.1001\/jamanetworkopen.2025.16400","volume":"8","author":"MP Lin","year":"2025","unstructured":"Lin, M. P. et al. Potential diagnostic error for emergency conditions, mortality, and healthy days at home. JAMA Netw. Open 8, e2516400 (2025).","journal-title":"JAMA Netw. Open"},{"key":"2522_CR4","doi-asserted-by":"crossref","unstructured":"Honarmand, K. et al. Society of critical care medicine guidelines on recognizing and responding to clinical deterioration outside the ICU: 2023. Critic. Care Med. 52, 314\u2013330 (2024).","DOI":"10.1097\/CCM.0000000000006072"},{"key":"2522_CR5","doi-asserted-by":"publisher","first-page":"e258498","DOI":"10.1001\/jamanetworkopen.2025.8498","volume":"8","author":"DR Sax","year":"2025","unstructured":"Sax, D. R., Warton, E. M., Mark, D. G. & Reed, M. E. Emergency department triage accuracy and delays in care for high-risk conditions. JAMA Netw. Open 8, e258498 (2025).","journal-title":"JAMA Netw. Open"},{"key":"2522_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-41544-0","volume":"13","author":"B Son","year":"2023","unstructured":"Son, B. et al. Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models. Sci. Rep. 13, 15031 (2023).","journal-title":"Sci. Rep."},{"key":"2522_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-40661-0","volume":"13","author":"S Boulitsakis Logothetis","year":"2023","unstructured":"Boulitsakis Logothetis, S., Green, D., Holland, M. & Al Moubayed, N. Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making. Sci. Rep. 13, 13563 (2023).","journal-title":"Sci. Rep."},{"key":"2522_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s12902-023-01437-9","volume":"23","author":"CC Hsu","year":"2023","unstructured":"Hsu, C. C. et al. Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time. BMC Endocr. Disord. 23, 234 (2023).","journal-title":"BMC Endocr. Disord."},{"key":"2522_CR9","doi-asserted-by":"publisher","first-page":"e2013101","DOI":"10.1001\/jamanetworkopen.2020.13101","volume":"3","author":"BA Goldstein","year":"2020","unstructured":"Goldstein, B. A. & Bedoya, A. D. Guiding clinical decisions through predictive risk rules. JAMA Netw. Open 3, e2013101 (2020).","journal-title":"JAMA Netw. Open"},{"key":"2522_CR10","doi-asserted-by":"publisher","first-page":"e040837","DOI":"10.1136\/bmjopen-2020-040837","volume":"11","author":"MJ Hsieh","year":"2021","unstructured":"Hsieh, M. J. et al. Developing and validating a model for predicting 7-day mortality of patients admitted from the emergency department: an initial alarm score by a prospective prediction model study. BMJ Open 11, e040837 (2021).","journal-title":"BMJ Open"},{"key":"2522_CR11","doi-asserted-by":"crossref","unstructured":"Bedoya, A. D. et al. Minimal impact of implemented early warning score and best practice alert for patient deterioration. Critic. Care Med. 47, 49\u201355 (2019).","DOI":"10.1097\/CCM.0000000000003439"},{"key":"2522_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10462-024-10884-2","volume":"57","author":"M Salmi","year":"2024","unstructured":"Salmi, M., Atif, D., Oliva, D., Abraham, A. & Ventura, S. Handling imbalanced medical datasets: review of a decade of research. Artif. Intell. Rev. 57, 273 (2024).","journal-title":"Artif. Intell. Rev."},{"key":"2522_CR13","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1111\/add.16133","volume":"118","author":"AR Cartus","year":"2023","unstructured":"Cartus, A. R., Samuels, E. A., Cerd\u00e1, M. & Marshall, B. D. L. Outcome class imbalance and rare events: an underappreciated complication for overdose risk prediction modeling. Addiction 118, 1167\u20131176 (2023).","journal-title":"Addiction"},{"key":"2522_CR14","doi-asserted-by":"publisher","first-page":"e002342","DOI":"10.1136\/bmjoq-2023-002342","volume":"12","author":"H Ruppel","year":"2023","unstructured":"Ruppel, H., Dougherty, M., Bonafide, C. P. & Lasater, K. B. Alarm burden and the nursing care environment: a 213-hospital cross-sectional study. BMJ Open Qual. 12, e002342 (2023).","journal-title":"BMJ Open Qual."},{"key":"2522_CR15","doi-asserted-by":"publisher","first-page":"e002262","DOI":"10.1136\/bmjoq-2023-002262","volume":"12","author":"HR Anderson","year":"2023","unstructured":"Anderson, H. R. et al. Stats on the desats: alarm fatigue and the implications for patient safety. BMJ Open Qual. 12, e002262 (2023).","journal-title":"BMJ Open Qual."},{"key":"2522_CR16","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1093\/jamia\/ocac093","volume":"29","author":"R van den Goorbergh","year":"2022","unstructured":"van den Goorbergh, R., van Smeden, M., Timmerman, D. & Van Calster, B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J. Am. Med. Inform. Assoc. 29, 1525\u20131534 (2022).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2522_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0160-7","volume":"2","author":"WTM Au-Yeung","year":"2019","unstructured":"Au-Yeung, W. T. M., Sahani, A. K., Isselbacher, E. M. & Armoundas, A. A. Reduction of false alarms in the intensive care unit using an optimized machine learning based approach. NPJ Digital Med. 2, 86 (2019).","journal-title":"NPJ Digital Med."},{"key":"2522_CR18","doi-asserted-by":"crossref","unstructured":"Segal, G. et al. Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction. Front. Neurosci. 17, 1184990 (2023).","DOI":"10.3389\/fnins.2023.1184990"},{"key":"2522_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12912-025-03084-y","volume":"24","author":"D Xu","year":"2025","unstructured":"Xu, D. et al. Exploring ICU nurses\u2019 response to alarm management and strategies for alleviating alarm fatigue: a meta-synthesis and systematic review. BMC Nurs. 24, 412 (2025).","journal-title":"BMC Nurs."},{"key":"2522_CR20","doi-asserted-by":"publisher","first-page":"106575","DOI":"10.1016\/j.jinf.2025.106575","volume":"91","author":"Y Takefuji","year":"2025","unstructured":"Takefuji, Y. Limitations of XGBoost-SHAP integration for interpretable machine learning in antimicrobial resistance prediction. J. Infect. 91, 106575 (2025).","journal-title":"J. Infect."},{"key":"2522_CR21","doi-asserted-by":"publisher","DOI":"10.1111\/cts.70056","volume":"17","author":"AV Ponce-Bobadilla","year":"2024","unstructured":"Ponce-Bobadilla, A. V., Schmitt, V., Maier, C. S., Mensing, S. & Stodtmann, S. Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development. Clin. Transl. Sci. 17, e70056 (2024).","journal-title":"Clin. Transl. Sci."},{"key":"2522_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s12873-024-01135-2","volume":"24","author":"BM Porto","year":"2024","unstructured":"Porto, B. M. Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review. BMC Emerg. Med. 24, 219 (2024).","journal-title":"BMC Emerg. Med."},{"key":"2522_CR23","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s44401-025-00015-6","volume":"2","author":"S Zhou","year":"2025","unstructured":"Zhou, S. et al. Explainable differential diagnosis with dual-inference large language models. NPJ Health Syst. 2, 12 (2025).","journal-title":"NPJ Health Syst."},{"key":"2522_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-52415-1","volume":"15","author":"CYK Williams","year":"2024","unstructured":"Williams, C. Y. K., Miao, B. Y., Kornblith, A. E. & Butte, A. J. Evaluating the use of large language models to provide clinical recommendations in the Emergency Department. Nat. Commun. 15, 8236 (2024).","journal-title":"Nat. Commun."},{"key":"2522_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-03010-x","volume":"25","author":"KM Kuo","year":"2025","unstructured":"Kuo, K. M. & Chang, C. S. A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions. BMC Med. Inform. Decis. Mak. 25, 187 (2025).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"2522_CR26","doi-asserted-by":"crossref","unstructured":"Tschoellitsch, T. et al. Using emergency department triage for machine learning-based admission and mortality prediction. Eur. J. Emergency Med. 30, 408\u2013416 (2023).","DOI":"10.1097\/MEJ.0000000000001068"},{"key":"2522_CR27","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/s11606-019-05512-7","volume":"35","author":"M Klug","year":"2020","unstructured":"Klug, M. et al. A gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a nine-point triage score. J. Gen. Intern. Med. 35, 220\u2013227 (2020).","journal-title":"J. Gen. Intern. Med."},{"key":"2522_CR28","doi-asserted-by":"publisher","first-page":"e13719","DOI":"10.2196\/13719","volume":"21","author":"C Ye","year":"2019","unstructured":"Ye, C. et al. A real-time early warning system for monitoring inpatient mortality risk: prospective study using electronic medical record data. J. Med. Internet Res. 21, e13719 (2019).","journal-title":"J. Med. Internet Res."},{"key":"2522_CR29","doi-asserted-by":"publisher","first-page":"e1920733","DOI":"10.1001\/jamanetworkopen.2019.20733","volume":"3","author":"N Brajer","year":"2020","unstructured":"Brajer, N. et al. Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission. JAMA Netw. Open 3, e1920733 (2020).","journal-title":"JAMA Netw. Open"},{"key":"2522_CR30","doi-asserted-by":"publisher","first-page":"e052663","DOI":"10.1136\/bmjopen-2021-052663","volume":"11","author":"A Naemi","year":"2021","unstructured":"Naemi, A. et al. Machine learning techniques for mortality prediction in emergency departments: a systematic review. BMJ Open 11, e052663 (2021).","journal-title":"BMJ Open"},{"key":"2522_CR31","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg, S. M. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2, 749\u2013760 (2018).","journal-title":"Nat. Biomed. Eng."},{"key":"2522_CR32","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/s42256-024-00976-7","volume":"7","author":"M Steyvers","year":"2025","unstructured":"Steyvers, M. et al. What large language models know and what people think they know. Nat. Mach. Intell. 7, 221\u2013231 (2025).","journal-title":"Nat. Mach. Intell."},{"key":"2522_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023).","journal-title":"Sci. Data"},{"key":"2522_CR34","doi-asserted-by":"publisher","first-page":"e35","DOI":"10.15586\/jptcp.v27i3.705","volume":"27","author":"A Gosselin","year":"2020","unstructured":"Gosselin, A. et al. Detection of serious adverse drug reactions using diagnostic codes in the International Statistical Classification of Diseases and Related Health Problems. J. Popul. Ther. Clin. Pharmacol. 27, e35\u2013e48 (2020).","journal-title":"J. Popul. Ther. Clin. Pharmacol."},{"key":"2522_CR35","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1001\/jamadermatol.2022.3872","volume":"158","author":"N Theodosakis","year":"2022","unstructured":"Theodosakis, N. et al. Validation of case identification for melasma using international statistical classification of diseases and related health problems, tenth revision codes. JAMA Dermatol. 158, 1453\u20131454 (2022).","journal-title":"JAMA Dermatol."},{"key":"2522_CR36","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1515\/cclm-2016-0793","volume":"54","author":"J Henny","year":"2016","unstructured":"Henny, J. et al. Recommendation for the review of biological reference intervals in medical laboratories. Clin. Chem. Lab. Med. 54, 1893\u20131900 (2016).","journal-title":"Clin. Chem. Lab. Med."},{"key":"2522_CR37","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s13246-023-01274-z","volume":"46","author":"N Prasanna Venkatesh","year":"2023","unstructured":"Prasanna Venkatesh, N., Pradeep Kumar, R., Chakravarthy Neelapu, B., Pal, K. & Sivaraman, J. CatBoost-based improved detection of P-wave changes in sinus rhythm and tachycardia conditions: a lead selection study. Phys. Eng. Sci. Med. 46, 925\u2013944 (2023).","journal-title":"Phys. Eng. Sci. Med."},{"key":"2522_CR38","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1089\/cmb.2023.0078","volume":"30","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Liu, J. xing, Wang, J., Shang, J. & Gao, Y. lian A graph representation approach based on light gradient boosting machine for predicting drug\u2013disease associations. J. Comput. Biol. 30, 937\u2013947 (2023).","journal-title":"J. Comput. Biol."},{"key":"2522_CR39","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TCBB.2021.3061300","volume":"19","author":"S Mahapatra","year":"2022","unstructured":"Mahapatra, S., Gupta, V. R., Sahu, S. S. & Panda, G. Deep neural network and extreme gradient boosting based hybrid classifier for improved prediction of protein-protein interaction. IEEE\/ACM Trans. Comput. Biol. Bioinform. 19, 155\u2013165 (2022).","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"2522_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2022.111514","volume":"341","author":"D Botha","year":"2022","unstructured":"Botha, D. & Steyn, M. The use of decision tree analysis for improving age estimation standards from the acetabulum. Forensic Sci. Int. 341, 111514 (2022).","journal-title":"Forensic Sci. Int."},{"key":"2522_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-023-01965-x","volume":"23","author":"ML Wallace","year":"2023","unstructured":"Wallace, M. L. et al. Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction. BMC Med. Res. Methodol. 23, 144 (2023).","journal-title":"BMC Med. Res. Methodol."},{"key":"2522_CR42","doi-asserted-by":"publisher","first-page":"16720","DOI":"10.1109\/TNNLS.2023.3297261","volume":"35","author":"J Wang","year":"2024","unstructured":"Wang, J. & Geng, X. Large margin weighted k-nearest neighbors label distribution learning for classification. IEEE Trans. Neural. Netw. Learn. Syst. 35, 16720\u201316732 (2024).","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."},{"key":"2522_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104484","volume":"151","author":"X Song","year":"2021","unstructured":"Song, X., Liu, X., Liu, F. & Wang, C. Comparison of machine learning and logistic regression models in predicting acute kidney injury: a systematic review and meta-analysis. Int. J. Med. Inform. 151, 104484 (2021).","journal-title":"Int. J. Med. Inform."},{"key":"2522_CR44","doi-asserted-by":"publisher","first-page":"2417","DOI":"10.1109\/TKDE.2017.2740926","volume":"29","author":"TT Wong","year":"2017","unstructured":"Wong, T. T. & Yang, N. Y. Dependency analysis of accuracy estimates in k-fold cross validation. IEEE Trans. Knowl. Data Eng. 29, 2417\u20132427 (2017).","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2522_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106584","volume":"214","author":"Y Nohara","year":"2022","unstructured":"Nohara, Y., Matsumoto, K., Soejima, H. & Nakashima, N. Explanation of machine learning models using shapley additive explanation and application for real data in hospital. Comput. Methods Prog. Biomed. 214, 106584 (2022).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"2522_CR46","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s13040-025-00440-1","volume":"18","author":"Y Ma","year":"2025","unstructured":"Ma, Y. et al. Advancing preeclampsia prediction: a tailored machine learning pipeline integratingresampling and ensemble models for handling imbalanced medical data. BioData Min. 18, 25 (2025).","journal-title":"BioData Min."},{"key":"2522_CR47","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1016\/j.spinee.2021.02.024","volume":"21","author":"AJ Vickers","year":"2021","unstructured":"Vickers, A. J. & Holland, F. Decision curve analysis to evaluate the clinical benefit of prediction models. Spine J. 21, 1643\u20131648 (2021).","journal-title":"Spine J."},{"key":"2522_CR48","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.eururo.2018.08.038","volume":"74","author":"B Van Calster","year":"2018","unstructured":"Van Calster, B. et al. Reporting and interpreting decision curve analysis: a guide for investigators. Eur. Urol. 74, 796\u2013804 (2018).","journal-title":"Eur. Urol."},{"key":"2522_CR49","unstructured":"Sellergren, A. et al. MedGemma Technical Report. Google Research and Google DeepMind. https:\/\/arxiv.org\/abs\/2507.05201 (2025)."},{"key":"2522_CR50","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1038\/s41591-025-03727-2","volume":"31","author":"S Sandmann","year":"2025","unstructured":"Sandmann, S. et al. Benchmark evaluation of DeepSeek large language models in clinical decision-making. Nat. Med. 31, 2546\u20132549 (2025).","journal-title":"Nat. Med."},{"key":"2522_CR51","doi-asserted-by":"publisher","first-page":"5641","DOI":"10.1007\/s00266-025-05038-w","volume":"49","author":"PP Ray","year":"2025","unstructured":"Ray, P. P. Toward transparent AI-enabled patient selection in cosmetic surgery by integrating reasoning and medical LLMs. Aesthetic Plastic Surg. 49, 5641\u20135642 (2025).","journal-title":"Aesthetic Plastic Surg."},{"key":"2522_CR52","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s41666-025-00194-9","volume":"9","author":"Y Lee","year":"2025","unstructured":"Lee, Y., Chang, C. H. & Yang, C. C. Enhancing patient-physician communication: simulating african american vernacular english in medical diagnostics with large language models. J. Healthc. Inform. Res. 9, 119\u2013153 (2025).","journal-title":"J. Healthc. Inform. Res."},{"key":"2522_CR53","doi-asserted-by":"crossref","unstructured":"Huang, Y. et al. Evaluation of large language models for providing educational information in orthokeratology care. Contact Lens Anterior Eye. 48, 102384 (2025).","DOI":"10.1016\/j.clae.2025.102384"},{"key":"2522_CR54","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1038\/s41592-024-02235-4","volume":"21","author":"W Hou","year":"2024","unstructured":"Hou, W. & Ji, Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat. Methods 21, 1462\u20131465 (2024).","journal-title":"Nat. Methods"},{"key":"2522_CR55","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-021-01400-z","volume":"21","author":"M Aslam","year":"2021","unstructured":"Aslam, M. Chi-square test under indeterminacy: an application using pulse count data. BMC Med. Res. Methodol. 21, 201 (2021).","journal-title":"BMC Med. Res. Methodol."},{"key":"2522_CR56","doi-asserted-by":"publisher","first-page":"77","DOI":"10.3758\/s13428-021-01775-3","volume":"55","author":"G Francis","year":"2023","unstructured":"Francis, G. & Jakicic, V. Equivalent statistics for a one-sample t-test. Behav. Res. Methods 55, 77\u201384 (2023).","journal-title":"Behav. Res. Methods"},{"key":"2522_CR57","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-29308-2","volume":"13","author":"JSC Clark","year":"2023","unstructured":"Clark, J. S. C. et al. Empirical investigations into Kruskal-Wallis power studies utilizing Bernstein fits, simulations and medical study datasets. Sci. Rep. 13, 2352 (2023).","journal-title":"Sci. Rep."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02522-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02522-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02522-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:02:35Z","timestamp":1777633355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02522-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2522"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02522-8","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,14]]},"assertion":[{"value":"8 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"346"}}