{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T03:36:34Z","timestamp":1764646594702,"version":"3.46.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100018936","name":"Universit\u00e4tsklinikum Heidelberg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100018936","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-03290-3","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T06:54:48Z","timestamp":1764140088000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning models incorporating somatic and mental comorbidities for prolonged length-of-stay prediction in a maximum care university hospital"],"prefix":"10.1186","volume":"25","author":[{"given":"Sophia","family":"Stahl-Toyota","sequence":"first","affiliation":[]},{"given":"Ivo","family":"D\u00f6nnhoff","sequence":"additional","affiliation":[]},{"given":"Ede","family":"Nagy","sequence":"additional","affiliation":[]},{"given":"Achim","family":"Hochlehnert","sequence":"additional","affiliation":[]},{"given":"Inga","family":"Unger","sequence":"additional","affiliation":[]},{"given":"Julia","family":"Szendr\u00f6di","sequence":"additional","affiliation":[]},{"given":"Norbert","family":"Frey","sequence":"additional","affiliation":[]},{"given":"Patrick","family":"Michl","sequence":"additional","affiliation":[]},{"given":"Carsten","family":"M\u00fcller-Tidow","sequence":"additional","affiliation":[]},{"given":"Dirk","family":"J\u00e4ger","sequence":"additional","affiliation":[]},{"given":"Hans-Christoph","family":"Friederich","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Nikendei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"3290_CR1","doi-asserted-by":"publisher","first-page":"e0000017","DOI":"10.1371\/journal.pdig.0000017","volume":"1","author":"K Stone","year":"2022","unstructured":"Stone K, Zwiggelaar R, Jones P. Mac Parthal\u00e1in, N. A systematic review of the prediction of hospital length of stay: towards a unified framework. PLOS Digit Health. 2022;1:e0000017. https:\/\/doi.org\/10.1371\/journal.pdig.0000017.","journal-title":"PLOS Digit Health"},{"key":"3290_CR2","doi-asserted-by":"publisher","first-page":"1192969","DOI":"10.3389\/fmed.2023.1192969","volume":"10","author":"S Gokhale","year":"2023","unstructured":"Gokhale S, et al. Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis. Front Med. 2023;10:1192969. https:\/\/doi.org\/10.3389\/fmed.2023.1192969.","journal-title":"Front Med"},{"key":"3290_CR3","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1002\/jhm.13405","volume":"19","author":"AE Brown","year":"2024","unstructured":"Brown AE, Press VG, Meltzer DO. Association of health confidence with hospital length of stay and readmission. J Hosp Med. 2024;19:794\u2013801.","journal-title":"J Hosp Med"},{"key":"3290_CR4","doi-asserted-by":"publisher","first-page":"2695","DOI":"10.1177\/00031348241248690","volume":"90","author":"AA Webber","year":"2024","unstructured":"Webber AA, et al. Psychiatric diagnoses are associated with postoperative disparities in patients undergoing major colorectal operations. Am Surg. 2024;90:2695\u2013702.","journal-title":"Am Surg"},{"key":"3290_CR5","doi-asserted-by":"publisher","unstructured":"Rajkomar A, et al. Scalable and accurate deep learning with electronic health records. Npj Digit Med. 2018;1. https:\/\/doi.org\/10.1038\/s41746-018-0029-1.","DOI":"10.1038\/s41746-018-0029-1"},{"key":"3290_CR6","doi-asserted-by":"publisher","first-page":"e0287234","DOI":"10.1371\/journal.pone.0287234","volume":"18","author":"S Stahl-Toyota","year":"2023","unstructured":"Stahl-Toyota S, et al. Interaction of mental comorbidity and physical Multimorbidity predicts length-of-stay in medical inpatients. PLoS ONE. 2023;18:e0287234. https:\/\/doi.org\/10.1371\/journal.pone.0287234.","journal-title":"PLoS ONE"},{"key":"3290_CR7","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1111\/imj.14962","volume":"52","author":"S Bacchi","year":"2022","unstructured":"Bacchi S, et al. Machine learning in the prediction of medical inpatient length of stay. Intern Med J. 2022;52:176\u201385.","journal-title":"Intern Med J"},{"key":"3290_CR8","doi-asserted-by":"publisher","first-page":"2149318","DOI":"10.1080\/20016689.2022.2149318","volume":"11","author":"F Jaotombo","year":"2022","unstructured":"Jaotombo F, et al. Machine-learning prediction for hospital length of stay using a French medico-administrative database. J Mark Access Health Policy. 2022;11:2149318. https:\/\/doi.org\/10.1080\/20016689.2022.2149318.","journal-title":"J Mark Access Health Policy"},{"key":"3290_CR9","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1001\/jamainternmed.2019.5193","volume":"180","author":"JS Goodwin","year":"2020","unstructured":"Goodwin JS, Li S, Kuo Y-F. Association of the work schedules of hospitalists with patient outcomes of hospitalization. JAMA Intern Med. 2020;180:215\u201322.","journal-title":"JAMA Intern Med"},{"key":"3290_CR10","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s10754-024-09369-0","volume":"24","author":"D Golinelli","year":"2024","unstructured":"Golinelli D, Sanmarchi F, Toscano F, Bucci A, Nante N. Analyzing the 20-year declining trend of hospital length-of-stay in European countries with different healthcare systems and reimbursement models. Int J Health Econ Manag. 2024;24:375\u201392.","journal-title":"Int J Health Econ Manag"},{"key":"3290_CR11","doi-asserted-by":"publisher","unstructured":"Chrusciel J, et al. The prediction of hospital length of stay using unstructured data. BMC Med Inf Decis Mak. 2021;21. https:\/\/doi.org\/10.1186\/s12911-021-01722-4.","DOI":"10.1186\/s12911-021-01722-4"},{"key":"3290_CR12","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1080\/01621459.1993.10476408","volume":"88","author":"PJ Rousseeuw","year":"1993","unstructured":"Rousseeuw PJ, Croux C. Alternatives to the median absolute deviation. J Am Stat Assoc. 1993;88:1273\u201383.","journal-title":"J Am Stat Assoc"},{"key":"3290_CR13","doi-asserted-by":"publisher","unstructured":"Yang J, Rahardja S, Fr\u00e4nti P. Association for Computing Machinery, Outlier detection: how to threshold outlier scores? in Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing. 2019;1\u20136; https:\/\/doi.org\/10.1145\/3371425.3371427.","DOI":"10.1145\/3371425.3371427"},{"key":"3290_CR14","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.jesp.2013.03.013","volume":"49","author":"C Leys","year":"2013","unstructured":"Leys C, Ley C, Klein O, Bernard P, Licata L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol. 2013;49:764\u20136.","journal-title":"J Exp Soc Psychol"},{"key":"3290_CR15","doi-asserted-by":"publisher","first-page":"e0289795","DOI":"10.1371\/journal.pone.0289795","volume":"18","author":"F Jaotombo","year":"2023","unstructured":"Jaotombo F, Adorni L, Ghattas B, Boyer L. Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data. PLoS ONE. 2023;18:e0289795. https:\/\/doi.org\/10.1371\/journal.pone.0289795.","journal-title":"PLoS ONE"},{"key":"3290_CR16","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1053\/j.jfas.2024.05.005","volume":"63","author":"T Chirongoma","year":"2024","unstructured":"Chirongoma T, et al. Predicting prolonged length of hospital stay and identifying risk factors following total ankle arthroplasty: A supervised machine learning methodology. J Foot Ankle Surg. 2024;63:557\u201361.","journal-title":"J Foot Ankle Surg"},{"key":"3290_CR17","doi-asserted-by":"publisher","first-page":"ooae074","DOI":"10.1093\/jamiaopen\/ooae074","volume":"7","author":"R Bopche","year":"2024","unstructured":"Bopche R, et al. In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records. JAMIA Open. 2024;7:ooae074. https:\/\/doi.org\/10.1093\/jamiaopen\/ooae074.","journal-title":"JAMIA Open"},{"key":"3290_CR18","doi-asserted-by":"publisher","first-page":"131785","DOI":"10.1016\/j.ijcard.2024.131785","volume":"400","author":"G Shalaby","year":"2024","unstructured":"Shalaby G, et al. Predictors of prolonged hospital stay and in-hospital mortality in female patients with acute myocardial infarction with specific reference to diabetes. Int J Cardiol. 2024;400:131785. https:\/\/doi.org\/10.1016\/j.ijcard.2024.131785.","journal-title":"Int J Cardiol"},{"key":"3290_CR19","doi-asserted-by":"publisher","unstructured":"Berg AR, et al. Factors associated with unplanned readmissions and prolonged length of stay in patients undergoing primary fusion for congenital scoliosis. Int J Spine Surg. 2024;8614. https:\/\/doi.org\/10.14444\/8614.","DOI":"10.14444\/8614"},{"key":"3290_CR20","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1186\/s12913-024-11238-y","volume":"24","author":"R Jain","year":"2024","unstructured":"Jain R, Singh M, Rao AR, Garg R. Predicting hospital length of stay using machine learning on a large open health dataset. BMC Health Serv Res. 2024;24:860. https:\/\/doi.org\/10.1186\/s12913-024-11238-y.","journal-title":"BMC Health Serv Res"},{"key":"3290_CR21","doi-asserted-by":"publisher","first-page":"e210","DOI":"10.1016\/j.wneu.2023.11.081","volume":"182","author":"M Karabacak","year":"2023","unstructured":"Karabacak M, Jagtiani P, Shrivastrava RK, Margetis K. Personalized prognosis with machine learning models for predicting in-hospital outcomes following intracranial meningioma resections. World Neurosurg. 2023;182:e210\u201330. https:\/\/doi.org\/10.1016\/j.wneu.2023.11.081.","journal-title":"World Neurosurg"},{"key":"3290_CR22","doi-asserted-by":"publisher","first-page":"1165178","DOI":"10.3389\/fendo.2023.1165178","volume":"14","author":"K Wang","year":"2023","unstructured":"Wang K, et al. A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study. Front Endocrinol. 2023;14:1165178. https:\/\/doi.org\/10.3389\/fendo.2023.1165178.","journal-title":"Front Endocrinol"},{"key":"3290_CR23","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1186\/s12911-020-01297-6","volume":"20","author":"D Zhang","year":"2020","unstructured":"Zhang D, Yin C, Zeng J, Yuan X, Zhang P. Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inf Decis Mak. 2020;20:280. https:\/\/doi.org\/10.1186\/s12911-020-01297-6.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3290_CR24","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1007\/s00127-024-02645-x","volume":"59","author":"G Santos","year":"2024","unstructured":"Santos G, Ferreira AR, Gon\u00e7alves-Pinho M, Freitas A, Fernandes L. The impact of comorbid psychiatric disorders on chronic obstructive pulmonary disease (COPD) hospitalizations: a nationwide retrospective study. Soc Psychiatry Psychiatr Epidemiol. 2024;59:2093\u2013103.","journal-title":"Soc Psychiatry Psychiatr Epidemiol"},{"key":"3290_CR25","doi-asserted-by":"publisher","first-page":"606038","DOI":"10.3389\/fsurg.2021.606038","volume":"8","author":"C Han","year":"2021","unstructured":"Han C, et al. To predict the length of hospital stay after total knee arthroplasty in an orthopedic center in china: the use of machine learning algorithms. Front Surg. 2021;8:606038. https:\/\/doi.org\/10.3389\/fsurg.2021.606038.","journal-title":"Front Surg"},{"key":"3290_CR26","doi-asserted-by":"publisher","first-page":"564","DOI":"10.3390\/jcm7120564","volume":"7","author":"M W\u0119giel","year":"2018","unstructured":"W\u0119giel M, et al. Hospitalization length after myocardial infarction: Risk-Assessment-Based time of hospital discharge vs. Real life practice. J Clin Med. 2018;7:564. https:\/\/doi.org\/10.3390\/jcm7120564.","journal-title":"J Clin Med"},{"key":"3290_CR27","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1186\/s40001-024-01988-0","volume":"29","author":"P Yasin","year":"2024","unstructured":"Yasin P, et al. Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI). Eur J Med Res. 2024;29:383. https:\/\/doi.org\/10.1186\/s40001-024-01988-0.","journal-title":"Eur J Med Res"},{"key":"3290_CR28","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1097\/MLR.0000000000001962","volume":"62","author":"T Gilbert","year":"2023","unstructured":"Gilbert T, et al. Combining the hospital frailty risk score with the Charlson and elixhauser Multimorbidity indices to identify older patients at risk of poor outcomes in acute care. Med Care. 2023;62:117\u201324.","journal-title":"Med Care"},{"key":"3290_CR29","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s12911-023-02140-4","volume":"23","author":"R Chen","year":"2023","unstructured":"Chen R, et al. A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the XGBoost algorithm. BMC Med Inf Decis Mak. 2023;23:49. https:\/\/doi.org\/10.1186\/s12911-023-02140-4.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3290_CR30","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s12911-024-02417-2","volume":"24","author":"C-H Lai","year":"2024","unstructured":"Lai C-H, Mok PK-L, Chau W-W, Law S-W. Application of machine learning models on predicting the length of hospital stay in fragility fracture patients. BMC Med Inf Decis Mak. 2024;24:26. https:\/\/doi.org\/10.1186\/s12911-024-02417-2.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3290_CR31","doi-asserted-by":"publisher","first-page":"105634","DOI":"10.1016\/j.ijmedinf.2024.105634","volume":"192","author":"A Mittal","year":"2024","unstructured":"Mittal A, et al. Predicting prolonged length of stay following revision total knee arthroplasty: A National database analysis using machine learning models. Int J Med Informat. 2024;192:105634. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105634.","journal-title":"Int J Med Informat"},{"key":"3290_CR32","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1016\/S0895-7177(01)00109-1","volume":"34","author":"T Pham-Gia","year":"2001","unstructured":"Pham-Gia T, Hung TL. The mean and median absolute deviations. Math Comput Model. 2001;34:921\u201336.","journal-title":"Math Comput Model"},{"key":"3290_CR33","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s12913-018-2892-5","volume":"18","author":"J Wolff","year":"2018","unstructured":"Wolff J, Heister T, Normann C, Kaier K. Hospital costs associated with psychiatric comorbidities: a retrospective study. BMC Health Serv Res. 2018;18:67. https:\/\/doi.org\/10.1186\/s12913-018-2892-5.","journal-title":"BMC Health Serv Res"},{"key":"3290_CR34","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1097\/CCM.0000000000006362","volume":"52","author":"KM Potter","year":"2024","unstructured":"Potter KM, Prendergast NT, Boyd JG. From traditional typing to intelligent insights: A narrative review of directions toward targeted therapies in delirium. Crit Care Med. 2024;52:1285\u201394. https:\/\/doi.org\/10.1097\/CCM.0000000000006362.","journal-title":"Crit Care Med"},{"key":"3290_CR35","first-page":"131","volume":"32","author":"A Molt\u00f3","year":"2014","unstructured":"Molt\u00f3 A, Dougados M. Comorbidity indices. Clin Exp Rheumatol. 2014;32:131\u20134.","journal-title":"Clin Exp Rheumatol"},{"key":"3290_CR36","doi-asserted-by":"publisher","first-page":"e14325","DOI":"10.2196\/14325","volume":"7","author":"P Wu","year":"2019","unstructured":"Wu P, et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med Inf. 2019;7:e14325. https:\/\/doi.org\/10.2196\/14325.","journal-title":"JMIR Med Inf"},{"key":"3290_CR37","unstructured":"DIMDI. ICD-10-WHO 2019 Regelwerk (Band 2) PDF - Referenzfassung. Deutsches Institut f\u00fcr Medizinische Dokumentation und Information; 2019. https:\/\/www.bfarm.de\/DE\/Kodiersysteme\/Services\/Downloads\/_node.html [last accessed 26 September 2025]."},{"key":"3290_CR38","doi-asserted-by":"publisher","unstructured":"Sasse L, et al. Overview of leakage scenarios in supervised machine learning. J Big Data. 2025;12. https:\/\/doi.org\/10.1186\/s40537-025-01193-8.","DOI":"10.1186\/s40537-025-01193-8"},{"key":"3290_CR39","doi-asserted-by":"publisher","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: A Next-generation hyperparameter optimization framework. Proc 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Miningc. 2019;2623\u20132631. https:\/\/doi.org\/10.1145\/3292500.3330701.","DOI":"10.1145\/3292500.3330701"},{"key":"3290_CR40","doi-asserted-by":"publisher","first-page":"108321","DOI":"10.1016\/j.compbiomed.2024.108321","volume":"174","author":"LCE Huberts","year":"2024","unstructured":"Huberts LCE, et al. Predictive analytics for cardiovascular patient readmission and mortality: an explainable approach. Comput Biol Med. 2024;174:108321. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108321.","journal-title":"Comput Biol Med"},{"key":"3290_CR41","doi-asserted-by":"publisher","first-page":"17728","DOI":"10.1038\/s41598-024-67844-7","volume":"14","author":"H Luo","year":"2024","unstructured":"Luo H, et al. SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation. Sci Rep. 2024;14:17728. https:\/\/doi.org\/10.1038\/s41598-024-67844-7.","journal-title":"Sci Rep"},{"key":"3290_CR42","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1093\/neuros\/nyy343","volume":"85","author":"WE Muhlestein","year":"2018","unstructured":"Muhlestein WE, Akagi DS, Davies JM, Chambless LB. Predicting inpatient length of stay after brain tumor surgery: developing machine learning ensembles to improve predictive performance. Neurosurgery. 2018;85:384\u201393.","journal-title":"Neurosurgery"},{"key":"3290_CR43","doi-asserted-by":"publisher","unstructured":"Lundberg SM, Lee S-I. A Unified Approach to Interpreting Model Predictions in NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems. 4768\u20134777; https:\/\/doi.org\/10.5555\/3295222.3295230 (2017).","DOI":"10.5555\/3295222.3295230"},{"key":"3290_CR44","doi-asserted-by":"publisher","unstructured":"McKinney W. Data structures for statistical computing in python in Proceedings of the 9th Python in Science Conference. (eds Stefan van der Walt & Jarrod Millman). 2010;56\u201361. https:\/\/doi.org\/10.25080\/Majora-92bf1922-00a.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"3290_CR45","doi-asserted-by":"publisher","unstructured":"pandas. pandas-dev\/pandas: Pandas. The pandas development team, Zenodo, 2020. https:\/\/doi.org\/10.5281\/zenodo.3509134. [last accessed 26 September 2025].","DOI":"10.5281\/zenodo.3509134"},{"key":"3290_CR46","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"3290_CR47","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, et al. Array programming with numpy. Nature. 2020;585:357\u201362.","journal-title":"Nature"},{"key":"3290_CR48","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD, Matplotlib. A 2D graphics environment. Comput Sci Eng. 2007;9:90\u20135.","journal-title":"Comput Sci Eng"},{"key":"3290_CR49","unstructured":"Gazoni E, Clark C. Openpyxl - A Python library to read\/write Excel 2010 xlsx\/xlsm files, (2024) https:\/\/openpyxl.readthedocs.io\/en\/stable\/ [last accessed 26 Sep 2025]."},{"key":"3290_CR50","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1186\/s13054-019-2568-5","volume":"23","author":"BA-O Gershkovich","year":"2019","unstructured":"Gershkovich BA-O, et al. Outcomes of hospitalized hematologic oncology patients receiving rapid response system activation for acute deterioration. Crit Care. 2019;23:286. https:\/\/doi.org\/10.1186\/s13054-019-2568-5.","journal-title":"Crit Care"},{"key":"3290_CR51","doi-asserted-by":"publisher","unstructured":"Aubert CE et al. Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization. Mayo Clin. Proc. Innov. Qual. Outcomes 4;40\u201349. https:\/\/doi.org\/10.1016\/j.mayocpiqo.2019.09.002 (2020).","DOI":"10.1016\/j.mayocpiqo.2019.09.002"},{"key":"3290_CR52","unstructured":"UKB. UK biobank hospital inpatient data version 4.0. UK Biobank; 2023. [last accessed 26 Sep 2025]."},{"key":"3290_CR53","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1210\/clinem\/dgae607","volume":"110","author":"K Tenreiro","year":"2025","unstructured":"Tenreiro K, Hatipoglu B. Mind matters: mental health and diabetes management. J Clin Endocrinol Metab. 2025;110:131\u20136.","journal-title":"J Clin Endocrinol Metab"},{"key":"3290_CR54","doi-asserted-by":"publisher","unstructured":"Ski CF, et al. Psychological interventions for depression and anxiety in patients with coronary heart disease, heart failure or atrial fibrillation. Cochrane Database Syst Rev. 2024;4. https:\/\/doi.org\/10.1002\/14651858.CD013508.pub3.","DOI":"10.1002\/14651858.CD013508.pub3"},{"key":"3290_CR55","doi-asserted-by":"publisher","first-page":"5992","DOI":"10.1017\/S0033291722003154","volume":"53","author":"T Bermudez","year":"2023","unstructured":"Bermudez T, et al. The role of daily adjustment disorder, depression and anxiety symptoms for the physical activity of cardiac patients. Psychol Med. 2023;53:5992\u20136001.","journal-title":"Psychol Med"},{"key":"3290_CR56","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s11886-023-01870-1","volume":"25","author":"M Princip","year":"2023","unstructured":"Princip M, Ledermann K. K\u00e4nel, R. Posttraumatic stress disorder as a consequence of acute cardiovascular disease. Curr Cardiol Rep. 2023;25:455\u201365. von.","journal-title":"Curr Cardiol Rep"},{"key":"3290_CR57","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/medicina61020279","volume":"61","author":"T Anghel","year":"2025","unstructured":"Anghel T, et al. Review of psychological interventions in oncology: current trends and future directions. Medicina. 2025;61:279. https:\/\/doi.org\/10.3390\/medicina61020279.","journal-title":"Medicina"},{"key":"3290_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S2045796019000866","volume":"29","author":"R Caruso","year":"2020","unstructured":"Caruso R, Breitbart W. Mental health care in oncology. Contemporary perspective on the psychosocial burden of cancer and evidence-based interventions. Epidemiol Psychiatr Sci. 2020;29:1\u20134. https:\/\/doi.org\/10.1017\/S2045796019000866.","journal-title":"Epidemiol Psychiatr Sci"},{"key":"3290_CR59","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s00134-021-06503-1","volume":"47","author":"JL Stollings","year":"2021","unstructured":"Stollings JL, et al. Delirium in critical illness: clinical manifestations, outcomes, and management. Intensive Care Med. 2021;47:1089\u2013103.","journal-title":"Intensive Care Med"},{"key":"3290_CR60","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1186\/s12913-024-10566-3","volume":"24","author":"G Arena","year":"2024","unstructured":"Arena G, Cumming C, Lizama N, Mace H, Preen DB. Hospital length of stay and readmission after elective surgery: a comparison of current and former smokers with non-smokers. BMC Health Serv Res. 2024;24:85. https:\/\/doi.org\/10.1186\/s12913-024-10566-3.","journal-title":"BMC Health Serv Res"},{"key":"3290_CR61","unstructured":"B\u00f6cken J. Spotlight Healthcare - Reorganizing germany\u2019s hospital landscape. Bertelsmann Stiftung; 2019. [last accessed 17 Oct 2025]."},{"key":"3290_CR62","doi-asserted-by":"publisher","first-page":"57","DOI":"10.15388\/Informatica.2011.314","volume":"22","author":"L Garg","year":"2011","unstructured":"Garg L, McClean S, Meenan BJ, Millard P. Phase-Type survival trees and mixed distribution survival trees for clustering patients\u2019 hospital length of stay. Informatica. 2011;22:57\u201372.","journal-title":"Informatica"},{"key":"3290_CR63","doi-asserted-by":"publisher","first-page":"e79","DOI":"10.1192\/j.eurpsy.2024.1780","volume":"67","author":"I D\u00f6nnhoff","year":"2024","unstructured":"D\u00f6nnhoff I, et al. Predictors for improvement in personality functioning during outpatient psychotherapy: A machine learning approach within a psychodynamic psychotherapy sample. Eur Psychiatry. 2024;67:e79.","journal-title":"Eur Psychiatry"},{"key":"3290_CR64","first-page":"87","volume":"23","author":"ME Chernew","year":"2017","unstructured":"Chernew ME. Addressing the chronification of disease. Am J Manag Care. 2017;23:87\u20138.","journal-title":"Am J Manag Care"},{"key":"3290_CR65","doi-asserted-by":"crossref","unstructured":"Mattei G, Curatola C, Moscara M. The interplay between Psychiatry, general Practitioners, and other specialists. Comorbidity between mental and physical disorders: Identification, management and treatment. eds. Andrea Fiorillo, Afzal Javed, & Norman Sartorius; Springer Nature Switzerland, 2025. pp. 369\u2013409.","DOI":"10.1007\/978-3-031-81802-8_17"},{"key":"3290_CR66","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1159\/000528451","volume":"92","author":"S Zipfel","year":"2023","unstructured":"Zipfel S, L\u00f6we B, Giel K, Friederich H-C, Henningsen P. Implementing the biopsychosocial model in clinical medicine: A tribute to Giovanni Fava. Psychother Psychosom. 2023;92:21\u20136.","journal-title":"Psychother Psychosom"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03290-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03290-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03290-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T03:33:52Z","timestamp":1764646432000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03290-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3290"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03290-3","relation":{},"ISSN":["1472-6947"],"issn-type":[{"type":"electronic","value":"1472-6947"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"10 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in accordance with the Declaration of Helsinki and the professional code of conduct of the Medical Association of Baden-W\u00fcrttemberg. The study was submitted to and approved by the Institutional Review Board \u2018Ethics Committee of the Medical Faculty of Heidelberg University\u2019 (German title: \u2018Ethikkommission der medizinischen Fakult\u00e4t Heidelberg\u2019) with the reference number No. S-690\/2021. As only clinical routine data were used, the Ethics Committee of the Medical Faculty of Heidelberg University waived the need for informed consent. Clinical trial number: not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"436"}}