{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:26:30Z","timestamp":1775845590282,"version":"3.50.1"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01229-z","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T08:29:29Z","timestamp":1752222569000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A comprehensive survey on machine learning for workplace injury analysis: risk prediction, return to work strategies, and demographic insights"],"prefix":"10.1186","volume":"12","author":[{"given":"Gonzalo A.","family":"Vivian","sequence":"first","affiliation":[]},{"given":"Richard A.","family":"Bauder","sequence":"additional","affiliation":[]},{"given":"Taghi M.","family":"Khoshgoftaar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"1229_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/ijerph192113962","volume":"19","author":"MZF Khairuddin","year":"2022","unstructured":"Khairuddin MZF, Lu Hui P, Hasikin K, Abd Razak NA, Lai KW, Mohd Saudi AS, Ibrahim SS. Occupational injury risk mitigation: machine learning approach and feature optimization for smart workplace surveillance. Int J Environ Res Public Health. 2022;19:21.","journal-title":"Int J Environ Res Public Health"},{"key":"1229_CR2","doi-asserted-by":"crossref","unstructured":"Sarkar S, Maiti J. Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis. Safety science 2020;131","DOI":"10.1016\/j.ssci.2020.104900"},{"issue":"03","key":"1229_CR3","doi-asserted-by":"publisher","DOI":"10.1142\/S0218539322500073","volume":"29","author":"A Ravikumar","year":"2022","unstructured":"Ravikumar A, Raju GY, Gupta S. A machine learning study on the role of behavioral and demographic factors in mining injuries. Int J Reliab , Qual Safety Eng. 2022;29(03): 225007.","journal-title":"Int J Reliab , Qual Safety Eng"},{"issue":"1","key":"1229_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s12998-016-0113-z","volume":"24","author":"C Cancelliere","year":"2016","unstructured":"Cancelliere C, et al. Factors affecting return to work after injury or illness: best evidence synthesis of systematic reviews. Chiropractic Manual Therapies. 2016;24(1):32. https:\/\/doi.org\/10.1186\/s12998-016-0113-z.","journal-title":"Chiropractic Manual Therapies"},{"issue":"19","key":"1229_CR5","doi-asserted-by":"publisher","first-page":"144","DOI":"10.3346\/jkms.2018.33.e144","volume":"33","author":"J Lee","year":"2018","unstructured":"Lee J, Kim H-R. Prediction of return-to-original-work after an industrial accident using machine learning and comparison of techniques. J Korean Med Sci. 2018;33(19):144. https:\/\/doi.org\/10.3346\/jkms.2018.33.e144.","journal-title":"J Korean Med Sci"},{"key":"1229_CR6","unstructured":"National Safety Council: Work injury costs 2023."},{"issue":"4","key":"1229_CR7","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/s10926-005-8038-8","volume":"15","author":"R-L Franche","year":"2005","unstructured":"Franche R-L, Cullen K, Clarke J, Irvin E, Sinclair S, Frank J. Workplace-based return-to-work interventions: a systematic review of the quantitative literature. J Occup Rehabil. 2005;15(4):607\u201331.","journal-title":"J Occup Rehabil"},{"key":"1229_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s10926-007-9116-x","volume":"18","author":"E Tompa","year":"2008","unstructured":"Tompa E, De Oliveira C, Dolinschi R, Irvin E. A systematic review of disability management interventions with economic evaluations. J Occup Rehabil. 2008;18:16\u201326.","journal-title":"J Occup Rehabil"},{"key":"1229_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10926-016-9690-x","volume":"28","author":"KL Cullen","year":"2018","unstructured":"Cullen KL, Irvin E, Collie A, Clay F, Gensby U, Jennings PA, Hogg-Johnson S, Kristman V, Laberge M, McKenzie D, et al. Effectiveness of workplace interventions in return-to-work for musculoskeletal, pain-related and mental health conditions: an update of the evidence and messages for practitioners. J Occup Rehabil. 2018;28:1\u201315.","journal-title":"J Occup Rehabil"},{"issue":"3","key":"1229_CR10","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1002\/ajim.22414","volume":"58","author":"J Berecki-Gisolf","year":"2015","unstructured":"Berecki-Gisolf J, Smith PM, Collie A, McClure RJ. Gender differences in occupational injury incidence. Am J Ind Med. 2015;58(3):299\u2013307. https:\/\/doi.org\/10.1002\/ajim.22414. (Epub 2015 Jan 14).","journal-title":"Am J Ind Med"},{"issue":"7","key":"1229_CR11","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1002\/ajim.23364","volume":"65","author":"A Biswas","year":"2022","unstructured":"Biswas A, Harbin S, Irvin E, Johnston H, Begum M, Tiong M, Apedaile D, Koehoorn M, Smith P. Differences between men and women in their risk of work injury and disability: a systematic review. Am J Ind Med. 2022;65(7):576\u201388. https:\/\/doi.org\/10.1002\/ajim.23364. (Epub 2022 May 16).","journal-title":"Am J Ind Med"},{"key":"1229_CR12","unstructured":"Occupational Safety and Health Administration: Recommended Practices for Safety and Health Programs. U.S. Department of Labor (n.d.)"},{"key":"1229_CR13","unstructured":"Bureau of Labor Statistics: Incidence rates of nonfatal occupational injuries and illnesses by industry and case types, 2021. U.S. Department of Labor. https:\/\/www.bls.gov\/iif\/nonfatal-injuries-and-illnesses-tables\/table-1-injury-and-illness-rates-by-industry-2021-national.htm 2023"},{"issue":"2","key":"1229_CR14","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1377\/hlthaff.2016.1185","volume":"36","author":"SA Seabury","year":"2017","unstructured":"Seabury SA, Terp S, Boden LI. Racial and ethnic differences in the frequency of workplace injuries and prevalence of work-related disability. Health Aff. 2017;36(2):266\u201373.","journal-title":"Health Aff"},{"key":"1229_CR15","unstructured":"Bureau of Labor Statistics: Employer-reported workplace injuries and illnesses (annual). Technical report, U.S. Department of Labor. https:\/\/www.bls.gov\/news.release\/osh.htm 2023"},{"issue":"1","key":"1229_CR16","first-page":"1","volume":"12","author":"A Chaudhry","year":"2024","unstructured":"Chaudhry A, Choudhury S. Clinical applications of artificial intelligence in occupational health: a systematic literature review. Occupat Med Health Aff. 2024;12(1):1\u201315.","journal-title":"Occupat Med Health Aff"},{"key":"1229_CR17","volume":"158","author":"M Maheronnaghsh","year":"2023","unstructured":"Maheronnaghsh M, Karimi A, Ghahramani R. Machine learning in occupational safety and health a systematic review. Saf Sci. 2023;158: 105998.","journal-title":"Saf Sci"},{"key":"1229_CR18","volume":"146","author":"R Cavalcanti","year":"2023","unstructured":"Cavalcanti R, Silva M, Santos P. Construction accident prevention: a systematic review of machine learning approaches. Autom Constr. 2023;146: 104785.","journal-title":"Autom Constr"},{"issue":"1","key":"1229_CR19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10926-023-10127-1","volume":"34","author":"R Escorpizo","year":"2024","unstructured":"Escorpizo R, Smith J, Brown D. A scoping review on the use of machine learning in return-to-work studies: strengths and weaknesses. J Occup Rehabil. 2024;34(1):45\u201362.","journal-title":"J Occup Rehabil"},{"key":"1229_CR20","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1007\/s10926-019-09832-7","volume":"29","author":"C Eib","year":"2019","unstructured":"Eib C, Bernhard-Oettel C, Magnusson Hanson LL, Leineweber C. Sustainable return to work: A systematic review focusing on personal and social factors. J Occup Rehabil. 2019;29:679\u2013700.","journal-title":"J Occup Rehabil"},{"key":"1229_CR21","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s10926-007-9071-6","volume":"17","author":"IZ Schultz","year":"2007","unstructured":"Schultz IZ, Stowell AW, Feuerstein M, Gatchel RJ. Models of return to work for musculoskeletal disorders: advances in preventing disability. J Occup Rehabil. 2007;17:327\u201352.","journal-title":"J Occup Rehabil"},{"key":"1229_CR22","first-page":"5","volume":"134","author":"JE Dodoo","year":"2021","unstructured":"Dodoo JE, Al-Samarraie H. A systematic review of factors leading to occupational injuries and fatalities. Safety Sci. 2021;134:5.","journal-title":"Safety Sci"},{"key":"1229_CR23","volume":"146","author":"M Khairuddin","year":"2022","unstructured":"Khairuddin M, Rahman A, Ang K. Predicting occupational injury causal factors using text-based analytics: a systematic review. Safety Sci. 2022;146: 984099.","journal-title":"Safety Sci"},{"issue":"1","key":"1229_CR24","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"issue":"3","key":"1229_CR25","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273\u201397.","journal-title":"Mach Learn"},{"issue":"1","key":"1229_CR26","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/A:1022643204877","volume":"1","author":"J Quinlan","year":"1986","unstructured":"Quinlan J. Induction of decision trees. Mach Learn. 1986;1(1):81\u2013106.","journal-title":"Mach Learn"},{"issue":"2","key":"1229_CR27","doi-asserted-by":"publisher","first-page":"119","DOI":"10.35371\/kjoem.2003.15.2.119","volume":"15","author":"WM Jeong","year":"2003","unstructured":"Jeong WM, Park CY, Koo JW, Roh YM. Predictors of return to work in occupational injured workers. Korean J Occup Environ Med. 2003;15(2):119\u201331.","journal-title":"Korean J Occup Environ Med"},{"issue":"13","key":"1229_CR28","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1056\/NEJM198509263131306","volume":"313","author":"JH Wasson","year":"1985","unstructured":"Wasson JH, Sox HC, Neff RK, Goldman L. Clinical prediction rules applications and methodological standards. New England J Med. 1985;313(13):793.","journal-title":"New England J Med"},{"key":"1229_CR29","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s40557-015-0076-x","volume":"27","author":"W Lee","year":"2015","unstructured":"Lee W, Yoon JH, Roh J, Kim YK, Seok H, Lee JH, et al. Factors related to the physician and the employer influencing successful return to work in korea: results from the first panel study of workers\u2019 compensation insurance (pswci). Ann Occup Environ Med. 2015;27:27.","journal-title":"Ann Occup Environ Med"},{"issue":"5","key":"1229_CR30","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1097\/JOM.0000000000001567","volume":"61","author":"K-S Na","year":"2019","unstructured":"Na K-S, Kim E. A machine learning-based predictive model of return to work after sick leave. J Occup Environ Med. 2019;61(5):191\u20139. https:\/\/doi.org\/10.1097\/JOM.0000000000001567.","journal-title":"J Occup Environ Med"},{"key":"1229_CR31","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1002\/mpr.329","volume":"20","author":"MJ Azur","year":"2011","unstructured":"Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20:40\u20139.","journal-title":"Int J Methods Psychiatr Res"},{"key":"1229_CR32","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.jbi.2008.09.001","volume":"42","author":"L Taft","year":"2009","unstructured":"Taft L, Evans R, Shyu C, et al. Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. J Biomed Inform. 2009;42:356\u201364.","journal-title":"J Biomed Inform"},{"key":"1229_CR33","doi-asserted-by":"publisher","DOI":"10.1515\/9781400876136","volume-title":"Society and the Adolescent Self-Image","author":"M Rosenberg","year":"1965","unstructured":"Rosenberg M. Society and the Adolescent Self-Image. Princeton, NJ: Princeton University Press; 1965."},{"key":"1229_CR34","volume-title":"Problem Solving Therapy in the Clinical Practice","author":"M Eskin","year":"2013","unstructured":"Eskin M. Problem Solving Therapy in the Clinical Practice. Oxford: Elsevier; 2013."},{"issue":"1","key":"1229_CR35","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s40537-024-00905-w","volume":"11","author":"H Wang","year":"2024","unstructured":"Wang H, Liang Q, Hancock JT, Khoshgoftaar TM. Feature selection strategies: a comparative analysis of shap-value and importance-based methods. J Big Data. 2024;11(1):44.","journal-title":"J Big Data"},{"issue":"1","key":"1229_CR36","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s40537-024-01041-1","volume":"12","author":"JT Hancock","year":"2025","unstructured":"Hancock JT, Khoshgoftaar TM, Liang Q. A problem-agnostic approach to feature selection and analysis using SHAP. J Big Data. 2025;12(1):12.","journal-title":"J Big Data"},{"key":"1229_CR37","doi-asserted-by":"publisher","DOI":"10.2991\/978-94-6463-136-4_43","author":"A Deshpande","year":"2023","unstructured":"Deshpande A, Kumar A. Workplace incident and injuries prevention using machine learning. Int Conf Appl Mach Intell Data Anal. 2023. https:\/\/doi.org\/10.2991\/978-94-6463-136-4_43.","journal-title":"Int Conf Appl Mach Intell Data Anal"},{"key":"1229_CR38","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"1229_CR39","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.procs.2017.08.335","volume":"113","author":"D Suleiman","year":"2017","unstructured":"Suleiman D, Al-Naymat G. Sms spam detection using h2o framework. Procedia Comput Sci. 2017;113:154\u201361.","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"1229_CR40","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s10926-018-9784-8","volume":"29","author":"MK Nicholas","year":"2019","unstructured":"Nicholas MK, et al. Predicting return to work in a heterogeneous sample of recently injured workers using the brief \u00f6MPSQ-sf. J Occup Rehabil. 2019;29(2):295\u2013302. https:\/\/doi.org\/10.1007\/s10926-018-9784-8.","journal-title":"J Occup Rehabil"},{"issue":"22","key":"1229_CR41","doi-asserted-by":"publisher","first-page":"1891","DOI":"10.1097\/BRS.0b013e3181f8f775","volume":"36","author":"SJ Linton","year":"2011","unstructured":"Linton SJ, Nicholas MK, MacDonald S. Development of a short form of the \u00f6REBRO musculoskeletal pain screening questionnaire. Spine. 2011;36(22):1891\u20135.","journal-title":"Spine"},{"issue":"4","key":"1229_CR42","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1097\/01.jom.0000121151.40413.bd","volume":"46","author":"RZ Goetzel","year":"2004","unstructured":"Goetzel RZ, Long SR, Ozminkowski RJ, Hawkins K, Wang S, Lynch W. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting u.s. employers. J Occup Environ Med. 2004;46(4):398.","journal-title":"J Occup Environ Med"},{"issue":"6","key":"1229_CR43","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1136\/oemed-2013-101571","volume":"71","author":"G Wynne-Jones","year":"2014","unstructured":"Wynne-Jones G, Cowen J, Lordan JL, Uthman O, Main CJ, Glozier N, et al. Absence from work and return to work in people with back pain: a systematic review and meta-analysis. Occup Environ Med. 2014;71(6):448\u201356.","journal-title":"Occup Environ Med"},{"issue":"5","key":"1229_CR44","doi-asserted-by":"publisher","first-page":"737","DOI":"10.2522\/ptj.20100224","volume":"91","author":"M Nicholas","year":"2011","unstructured":"Nicholas M, Linton S, Watson P, Main C. The early identification and management of psychological risk factors (yellow flags) in patients with low back pain: a reappraisal. Phys Ther. 2011;91(5):737\u201353.","journal-title":"Phys Ther"},{"key":"1229_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105201","volume":"178","author":"H Van Deynse","year":"2023","unstructured":"Van Deynse H, et al. Predicting return to work after traumatic brain injury using machine learning and administrative data. Int J Med Inf. 2023;178: 105201. https:\/\/doi.org\/10.1016\/j.ijmedinf.2023.105201.","journal-title":"Int J Med Inf"},{"issue":"12","key":"1229_CR46","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1016\/S1474-4422(17)30371-X","volume":"16","author":"AI Maas","year":"2017","unstructured":"Maas AI, Menon DK, Adelson PD, Andelic N, Bell MJ, Belli A, et al. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol. 2017;16(12):987\u20131048.","journal-title":"Lancet Neurol"},{"issue":"3","key":"1229_CR47","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1097\/HTR.0000000000000438","volume":"34","author":"KA Stromberg","year":"2019","unstructured":"Stromberg KA, Agyemang AA, Graham KM, Walker WC, Sima AP, Marwitz JH, et al. Using decision tree methodology to predict employment after moderate to severe traumatic brain injury. J Head Trauma Rehabil. 2019;34(3):64.","journal-title":"J Head Trauma Rehabil"},{"key":"1229_CR48","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.2340\/jrm.v55.2432","volume":"55","author":"JC Yuan","year":"2023","unstructured":"Yuan JC, et al. Predicting return to work after cardiac rehabilitation using machine learning models. J Rehabil Med. 2023;55:2432. https:\/\/doi.org\/10.2340\/jrm.v55.2432.","journal-title":"J Rehabil Med"},{"key":"1229_CR49","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.117.004528","author":"H Warraich","year":"2018","unstructured":"Warraich H, Kaltenbach L, Fonarow G, Peterson E, Wang T. Adverse change in employment status after acute myocardial infarction: analysis from the translate-acs study. Circu Cardiovas Qual Outcomes. 2018. https:\/\/doi.org\/10.1161\/CIRCOUTCOMES.117.004528.","journal-title":"Circu Cardiovas Qual Outcomes"},{"key":"1229_CR50","doi-asserted-by":"crossref","unstructured":"Kennedy KR, Khoshgoftaar TM. Impact of class imbalance on unsupervised label generation for medicare fraud detection. In: 2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI), 2024;216\u2013221. IEEE","DOI":"10.1109\/IRI62200.2024.00053"},{"issue":"01","key":"1229_CR51","doi-asserted-by":"publisher","first-page":"2350039","DOI":"10.1142\/S0218539323500390","volume":"31","author":"JT Hancock III","year":"2024","unstructured":"Hancock JT III, Khoshgoftaar TM, Johnson JM. Using area under the precision recall curve to assess the effect of random undersampling in the classification of imbalanced medicare big data. Int J Reliab Qual Saf Eng. 2024;31(01):2350039.","journal-title":"Int J Reliab Qual Saf Eng"},{"issue":"1","key":"1229_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/HTR.0000000000000772","volume":"38","author":"SCR Fure","year":"2023","unstructured":"Fure SCR, et al. Workplace factors associated with return to work after mild-to-moderate traumatic brain injury. J Head Trauma Rehabil. 2023;38(1):1\u20139. https:\/\/doi.org\/10.1097\/HTR.0000000000000772.","journal-title":"J Head Trauma Rehabil"},{"key":"1229_CR53","unstructured":"The Norwegian Government: Letter of Intent regarding a more inclusive working life (the IA Agreement). Updated January 25, 2017 (2017). https:\/\/www.regjeringen.no\/globalassets\/departementene\/asd\/dokumenter\/2016\/ia_agreement_-2014_18.pdf"},{"issue":"6","key":"1229_CR54","doi-asserted-by":"publisher","first-page":"438","DOI":"10.5271\/sjweh.948","volume":"31","author":"T Kristensen","year":"2005","unstructured":"Kristensen T, Hannerz H, H\u00f8gh A, Borg V. The copenhagen psychosocial questionnaire\u2013a tool for the assessment and improvement of the psychosocial work environment. Scand J Work Environ Health. 2005;31(6):438\u201349. https:\/\/doi.org\/10.5271\/sjweh.948.","journal-title":"Scand J Work Environ Health"},{"issue":"8","key":"1229_CR55","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s00420-014-0936-7","volume":"87","author":"T Clausen","year":"2014","unstructured":"Clausen T, Burr H, Borg V. Do psychosocial job demands and job resources predict long-term sickness absence? an analysis of register-based outcomes using pooled data on 39,408 individuals in four occupational groups. Int Arch Occup Environ Health. 2014;87(8):909\u201317. https:\/\/doi.org\/10.1007\/s00420-014-0936-7.","journal-title":"Int Arch Occup Environ Health"},{"issue":"2","key":"1229_CR56","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1080\/09638288.2016.1247471","volume":"40","author":"C Roelen","year":"2018","unstructured":"Roelen C, Thorsen S, Heymans M, Twisk J, B\u00fcltmann U, Bj\u00f8rner J. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables. Disab Rehabil. 2018;40(2):168\u201375. https:\/\/doi.org\/10.1080\/09638288.2016.1247471.","journal-title":"Disab Rehabil"},{"issue":"4","key":"1229_CR57","doi-asserted-by":"publisher","first-page":"806","DOI":"10.3390\/jcm10040806","volume":"10","author":"M Vlegel","year":"2021","unstructured":"Vlegel M, Polinder S, Toet H, Panneman M, Haagsma J. Prevalence of postconcussion-like symptoms in the general injury population and the association with health-related quality of life, health care use, and return to work. J Clin Med. 2021;10(4):806. https:\/\/doi.org\/10.3390\/jcm10040806.","journal-title":"J Clin Med"},{"issue":"1","key":"1229_CR58","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s40621-018-0166-7","volume":"5","author":"NK Baidwan","year":"2018","unstructured":"Baidwan NK, et al. A longitudinal study of work-related injuries: comparisons of health and work-related consequences between injured and uninjured aging united states adults. Inj Epidemiol. 2018;5(1):35. https:\/\/doi.org\/10.1186\/s40621-018-0166-7.","journal-title":"Inj Epidemiol"},{"issue":"4","key":"1229_CR59","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1002\/ajim.20569","volume":"51","author":"M Silverstein","year":"2008","unstructured":"Silverstein M. Meeting the challenges of an aging workforce. Am J Ind Med. 2008;51(4):269\u201380.","journal-title":"Am J Ind Med"},{"issue":"8","key":"1229_CR60","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1093\/oxfordjournals.aje.a010087","volume":"150","author":"L Gardner","year":"1999","unstructured":"Gardner L, Landsittel D, Nelson N. Risk factors for back injury in 31,076 retail merchandise store workers. Am J Epidemiol. 1999;150(8):825\u201333.","journal-title":"Am J Epidemiol"},{"issue":"5","key":"1229_CR61","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1002\/1097-0274(200011)38:5<498::AID-AJIM2>3.0.CO;2-I","volume":"38","author":"J Keogh","year":"2000","unstructured":"Keogh J, Nuwayhid I, Gordon J, Gucer P. The impact of occupational injury on injured worker and family: outcomes of upper extremity cumulative trauma disorders in maryland workers. Am J Ind Med. 2000;38(5):498\u2013506.","journal-title":"Am J Ind Med"},{"issue":"2","key":"1229_CR62","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"D Cox","year":"1972","unstructured":"Cox D. Regression models and life-tables. J Roy Stat Soc: Ser B (Methodol). 1972;34(2):187\u2013220.","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"4","key":"1229_CR63","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1214\/aos\/1176345976","volume":"10","author":"P Andersen","year":"1982","unstructured":"Andersen P, Gill R. Cox\u2019s regression model for counting processes: a large sample study. Ann Stat. 1982;10(4):1100\u201320.","journal-title":"Ann Stat"},{"key":"1229_CR64","unstructured":"Bureau of Labor Statistics: Nonfatal Occupational Injuries and Illnesses Requiring Days Away From Work. https:\/\/www.bls.gov\/news.release\/pdf\/osh2.pdf 2016"},{"key":"1229_CR65","volume-title":"The aging us workforce: A chart book of demographic shifts","author":"A Hayutin","year":"2013","unstructured":"Hayutin A, Beals M, Borges E. The aging us workforce: A chart book of demographic shifts. Stanford Center on Longevity, Stanford, CA: Technical report; 2013."},{"issue":"1","key":"1229_CR66","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1136\/emj.20.1.54","volume":"20","author":"C Mann","year":"2003","unstructured":"Mann C. Observational research methods research design ii cohort cross sectional and case-control studies. Emerg Med J. 2003;20(1):54\u201360.","journal-title":"Emerg Med J"},{"key":"1229_CR67","doi-asserted-by":"crossref","unstructured":"Guerin RJ, et al. 2020 Nonfatal occupational injuries to younger workers - united states, 2012\u20132018. MMWR. Morbidity and mortality weekly report 69(35):1204\u20131209 https:\/\/doi.org\/10.15585\/mmwr.mm6935a3","DOI":"10.15585\/mmwr.mm6935a3"},{"key":"1229_CR68","unstructured":"National Institute for Occupational Safety and Health: The employed labor force query system. Technical report, US Department of Health and Human Services, CDC, Morgantown, WV. https:\/\/wwwn.cdc.gov\/Wisards\/cps\/default.aspx 2020"},{"key":"1229_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.shaw.2018.12.003","author":"TN Hanvold","year":"2019","unstructured":"Hanvold TN, Kines P, Nyk\u00e4nen M, et al. Occupational safety and health among young workers in the nordic countries: a systematic literature review. Safety Health at Work. 2019. https:\/\/doi.org\/10.1016\/j.shaw.2018.12.003.","journal-title":"Safety Health at Work"},{"key":"1229_CR70","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1007\/s11121-019-01008-2","volume":"20","author":"RJ Guerin","year":"2019","unstructured":"Guerin RJ, Okun AH, Barile JP, Emshoff JG, Ediger MD, Baker DS. Preparing teens to stay safe and healthy on the job: a multilevel evaluation of the talking safety curriculum for middle schools and high schools. Prev Sci. 2019;20:510\u201320. https:\/\/doi.org\/10.1007\/s11121-019-01008-2.","journal-title":"Prev Sci"},{"key":"1229_CR71","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.2105\/AJPH.2007.122853","volume":"98","author":"KJ Rauscher","year":"2008","unstructured":"Rauscher KJ, Runyan CW, Schulman MD, Bowling JM. Us child labor violations in the retail and service industries: findings from a national survey of working adolescents. Am J Public Health. 2008;98:1693\u20139. https:\/\/doi.org\/10.2105\/AJPH.2007.122853.","journal-title":"Am J Public Health"},{"issue":"1","key":"1229_CR72","doi-asserted-by":"publisher","first-page":"2473011421","DOI":"10.1177\/2473011421S00241","volume":"7","author":"CP Hoch","year":"2022","unstructured":"Hoch CP, et al. A detailed analysis of workplace foot and ankle injuries. Foot & Ankle Orthopaedics. 2022;7(1):2473011421\u201300241. https:\/\/doi.org\/10.1177\/2473011421S00241.","journal-title":"Foot & Ankle Orthopaedics"},{"issue":"2","key":"1229_CR73","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1136\/injuryprev-2019-043607","volume":"27","author":"C Peterson","year":"2021","unstructured":"Peterson C, et al. Average lost work productivity due to non-fatal injuries by type in the USA. Inj Prev. 2021;27(2):111\u20137. https:\/\/doi.org\/10.1136\/injuryprev-2019-043607.","journal-title":"Inj Prev"},{"key":"1229_CR74","unstructured":"CDC National Center for Health Statistics: The Barell injury diagnosis matrix, classification by body region and nature of the injury. https:\/\/www.cdc.gov\/nchs\/injury\/ice\/barell_matrix.htm 2001"},{"key":"1229_CR75","volume-title":"Medical and work loss cost estimation methods for the wisqars cost of injury module","author":"B Lawrence","year":"2014","unstructured":"Lawrence B, Miller T. Medical and work loss cost estimation methods for the wisqars cost of injury module. Technical report: Pacific Institute for Research & Evaluation, Calverton, MD; 2014."},{"key":"1229_CR76","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1186\/s12913-015-0925-x","volume":"15","author":"X Song","year":"2015","unstructured":"Song X, Quek R, Gandra S, et al. Productivity loss and indirect costs associated with cardiovascular events and related clinical procedures. BMC Health Serv Res. 2015;15:245.","journal-title":"BMC Health Serv Res"},{"key":"1229_CR77","unstructured":"Lundberg SM, Lee S-I. A Unified Approach to Interpreting Model Predictions. https:\/\/github.com\/shap\/shap. Accessed: 2025-05-04 2017"},{"key":"1229_CR78","unstructured":"Bureau of Labor Statistics: Employer-reported workplace injuries and illnesses 2021. U.S. Department of Labor. https:\/\/www.bls.gov\/news.release\/pdf\/osh.pdf 2023"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01229-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01229-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01229-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T05:11:32Z","timestamp":1757221892000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01229-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,11]]},"references-count":78,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1229"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01229-z","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,11]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"167"}}