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Comparison of Machine Learning Methods in the Study of Cancer Survivors’ Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort

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Abstract

Purpose

Machine learning (ML) methods showed a higher accuracy in identifying individuals without cancer who were unable to return to work (RTW) compared to the classical methods (e.g. logistic regression models). We therefore aim to discuss the value of these methods in relation to RTW for cancer survivors.

Methods

Breast cancer (BC) survivors who were working at diagnosis within the CONSTANCES cohort were included in the study. RTW was assessed five years after the BC diagnosis (early retirement was considered as non-RTW). Age and occupation at diagnosis, and physical occupational job exposures assessed using the Job Exposure Matrix, JEM-CONSTANCES, were evaluated as predictors of RTW five years after BC diagnosis. The following four ML methods were used: (i) k-nearest neighbors; (ii) random forest; (iii) neural network; and (iv) elastic net.

Results

The training sample included 683 BC survivors (RTW: 85.7%), and the test sample 171 (RTW: 85.4%). The elastic net method had the best results despite low sensitivity (accuracy = 76.6%; sensitivity = 31.7%; specificity = 90.8%), and the random forest model was the most accurate (= 79.5%) but also the least sensitive (= 14.3%).

Conclusion

This study takes a first step towards opening up new possibilities for identifying the occupational determinants of cancer survivors’ RTW. Further work, including a larger sample size, and more predictor variables, is now needed.

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References

  1. Gragnano A, Negrini A, Miglioretti M, Corbière M. Common psychosocial factors Predicting Return to Work after Common Mental Disorders, Cardiovascular Diseases, and cancers: a review of Reviews supporting a Cross-Disease Approach. J Occup Rehabil. 2018;28:215–31.

    Article  PubMed  Google Scholar 

  2. Tamminga SJ, de Wind A, Greidanus MA, Coenen P, Friberg E, Oldenburg H et al. Prognostic factors for return to work in breast cancer survivors. Cochrane Database of Systematic Reviews [Internet]. John Wiley & Sons, Ltd; 2022 [cited 2022 Jul 3]; Available from: https://www.cochranelibrary.com/cdsr/doi/https://doi.org/10.1002/14651858.CD015124/full

  3. Wang L, Hong BY, Kennedy SA, Chang Y, Hong CJ, Craigie S, et al. Predictors of unemployment after breast Cancer surgery: a systematic review and Meta-analysis of Observational Studies. J Clin Oncol. 2018;36:1868–79.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Porro B, Michel A, Zinzindohoué C, Bertrand P, Monrigal E, Trentini F et al. Quality of life, fatigue and changes therein as predictors of return to work during breast cancer treatment. Scandinavian Journal of Caring Sciences [Internet]. 2019 [cited 2019 Mar 3]; Available from: https://doi.org/10.1111/scs.12646

  5. Dumas A, Vaz Luis I, Bovagnet T, El Mouhebb M, Di Meglio A, Pinto S et al. Impact of Breast Cancer Treatment on Employment: Results of a Multicenter Prospective Cohort Study (CANTO).Journal of Clinical Oncology. 2020;JCO.19.01726.

  6. Granell M, Senín A, Barata A, Cibeira M-T, Gironella M, López-Pardo J et al. Predictors of return to work after autologous stem cell transplantation in patients with multiple myeloma.Bone Marrow Transplant. 2021

  7. Gross DP, Steenstra IA, Harrell FE, Bellinger C, Zaïane O. Machine learning for Work Disability Prevention: introduction to the Special Series. J Occup Rehabil. 2020;30:303–7.

    Article  PubMed  Google Scholar 

  8. Song X, Mitnitski A, Cox J, Rockwood K. Comparison of machine learning techniques with classical statistical models in Predicting Health Outcomes. MEDINFO 2004. IOS Press; 2004. pp. 736–40.

  9. Houston A, Cosma G, Turner P, Bennett A. Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation strategies. Sci Rep. 2021;11:24281.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Na K-S, Kim E. A machine learning-based predictive model of Return to Work after Sick leave. J Occup Environ Med. 2019;61:e191–9.

    Article  PubMed  Google Scholar 

  11. 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:e144.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chen Y-C, Chen Y-L, Kuo D-P, Li Y-T, Chiang Y-H, Chang J-J, et al. Personalized prediction of postconcussive Working Memory decline: a feasibility study. J Pers Med. 2022;12:196.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Iosa M, Capodaglio E, Pelà S, Persechino B, Morone G, Antonucci G, et al. Artificial neural network analyzing Wearable device Gait Data for identifying patients with stroke unable to return to work. Front Neurol. 2021;12:650542.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Miotto R, Percha BL, Glicksberg BS, Lee H-C, Cruz L, Dudley JT, et al. Identifying Acute Low Back Pain Episodes in Primary Care Practice from Clinical Notes: Observational Study. JMIR Med Inform. 2020;8:e16878.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Deo RC. Machine learning in Medicine. Circulation. 2015;132:1920–30.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci. 2020;111:1452–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cartus AR, Naimi AI, Himes KP, Jarlenski M, Parisi SM, Bodnar LM. Can Ensemble Machine Learning improve the accuracy of severe maternal morbidity screening in a Perinatal Database? Epidemiology. 2022;33:95–104.

    Article  PubMed  Google Scholar 

  18. Connor CW. Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology. 2019;131:1346–59.

    Article  PubMed  Google Scholar 

  19. Christie SA, Conroy AS, Callcut RA, Hubbard AE, Cohen MJ. Dynamic multi-outcome prediction after injury: applying adaptive machine learning for precision medicine in trauma. PLoS ONE. 2019;14:e0213836.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Badreau M, Fadel M, Graszyck P, Descatha A. [Machine Learning: fundamentals for environmental and occupational health professionals]. Encycl Med Chir (Elservier Paris), Pathologie professionnelle,. 2023;acccepted.

  21. Fauquemberg L, Le guen V, Badreau M, Gilbert F, Descatha A. Sectors-Job retention/disability matrix on Reunion Island: from description and prediction by Machine Learning to prevention of job loss. Arch MalProf Environ. 2023;accepted.

  22. Porro B, Durand M-J, Petit A, Bertin M, Roquelaure Y. Return to work of breast cancer survivors: toward an integrative and transactional conceptual model. J Cancer Surviv. 2022;16:590–603.

    Article  PubMed  Google Scholar 

  23. Porro B, Campone M, Moreau P, Roquelaure Y. Supporting the Return to Work of Breast Cancer Survivors: From a Theoretical to a Clinical Perspective. International Journal of Environmental Research and Public Health. Multidisciplinary Digital Publishing Institute; 2022;19:5124.

  24. Goldberg M, Carton M, Descatha A, Leclerc A, Roquelaure Y, Santin G, et al. CONSTANCES: a general prospective population-based cohort for occupational and environmental epidemiology: cohort profile. Occup Environ Med. 2017;74:66–71.

    Article  PubMed  Google Scholar 

  25. Zins M, Goldberg M. The french CONSTANCES population-based cohort: design, inclusion and follow-up. Eur J Epidemiol. 2015;30:1317–28.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Descatha A, Fadel M, Sembajwe G, Peters S, Evanoff BA, Job-Exposure Matrix. A Useful Tool for Incorporating Workplace Exposure Data Into Population Health Research and Practice. Frontiers in Epidemiology [Internet]. 2022 [cited 2022 Aug 10];2. Available from: https://www.frontiersin.org/articles/https://doi.org/10.3389/fepid.2022.857316

  27. Evanoff BA, Yung M, Buckner-Petty S, Andersen JH, Roquelaure Y, Descatha A, et al. The CONSTANCES job exposure matrix based on self-reported exposure to physical risk factors: development and evaluation. Occup Environ Med. 2019;76:398–406.

    Article  PubMed  Google Scholar 

  28. Brünger M, Bernert S, Spyra K. Occupation as a Proxy for Job Exposures? Routine Data Analysis using the Example of Rehabilitation. Gesundheitswesen. 2020;82:41–51.

    Article  Google Scholar 

  29. van der Laan MJ, Polley EC, Hubbard AE. Super Learner. Statistical Applications in Genetics and Molecular Biology [Internet]. De Gruyter; 2007 [cited 2022 Aug 13];6. Available from: https://www.degruyter.com/document/doi/https://doi.org/10.2202/1544-6115.1309/html

  30. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Science & Business Media; 2009.

  31. van Hoffen MFA, Norder G, Twisk JWR, Roelen CAM. Development of prediction models for sickness absence due to Mental Disorders in the General Working Population. J Occup Rehabil. 2020;30:308–17.

    Article  PubMed  Google Scholar 

  32. Gross DP, Zhang J, Steenstra I, Barnsley S, Haws C, Amell T, et al. Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers. J Occup Rehabil. 2013;23:597–609.

    Article  PubMed  Google Scholar 

  33. Gross DP, Steenstra IA, Shaw W, Yousefi P, Bellinger C, Zaïane O. Validity of the Work Assessment Triage Tool for Selecting Rehabilitation Interventions for Workers’ Compensation Claimants with Musculoskeletal Conditions. J Occup Rehabil. 2020;30:318–30.

    Article  PubMed  Google Scholar 

  34. Six Dijkstra MWMC, Siebrand E, Dorrestijn S, Salomons EL, Reneman MF, Oosterveld FGJ, et al. Ethical considerations of using machine learning for decision support in Occupational Health: an Example Involving Periodic Workers’ Health assessments. J Occup Rehabil. 2020;30:343–53.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This manuscript was prepared as part of the SIRIC ILIAD program supported by the French National Cancer Institute (INCa), the French Ministry of Health and the Institute of Health and Medical Research (Inserm); INCa-DGOS-Inserm_12558 contract.

This manuscript was prepared as part of the TEC-TOP project (Pays de la Loire Region, Angers Loire Metropole, Univ Angers and CHU Angers).

Author information

Author notes

  1. Bertrand Porro and Alexis Descatha are co-last author.

Authors and Affiliations

  1. Univ Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), UMR_S 1085, IRSET-ESTER, SFR ICAT, Angers, F-49000, France

    Marie Badreau, Marc Fadel, Yves Roquelaure, Clémence Rapicault, Fabien Gilbert, Bertrand Porro & Alexis Descatha

  2. Univ Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), UMR_S 1085, IRSET ESTER, SFR ICAT, Angers, F-49000, France

    Marc Fadel, Yves Roquelaure, Mélanie Bertin & Alexis Descatha

  3. Univ Rennes, EHESP, CNRS, Inserm, Arènes - UMR 6051, RSMS (Recherche sur les Services et Management en Santé) - U 1309, Rennes, F-35000, France

    Mélanie Bertin

  4. Department of Human and Social Sciences, Institut de Cancerologie de l’Ouest (ICO), Angers, 49055, France

    Bertrand Porro

  5. Centre antipoison et de toxicovigilance Grand Ouest, CHU Angers, CHU Angers, Angers, France

    Alexis Descatha

  6. Department of Occupational Medicine, Epidemiology and Prevention, Donald and Barbara Zucker School of Medicine, Hofstra, Northwell, USA

    Alexis Descatha

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  1. Marie Badreau
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Contributions

Marie Badreau (MSc): conception and design of the study, analysis, interpretation of data, drafting the article and the final approval of the version to be submitted; Marc Fadel (MSc, MD): conception and design of the study, analysis, interpretation of data, revising the article critically for important intellectual content and the final approval of the version to be submitted; Prof. Yves Roquelaure (PhD, MD): acquisition of data and the final approval of the version to be submitted; Mélanie Bertin (PhD): acquisition of data, interpretation of data, revising the article critically for important intellectual content and the final approval of the version to be submitted; Clémence Rapicault (MSc): revising the article critically for important intellectual content and the final approval of the version to be submitted; Fabien Gilbert (MSc): acquisition of data and the final approval of the version to be submitted; Bertrand Porro (PhD): interpretation of data, drafting the article, revising the article critically for important intellectual content and the final approval of the version to be submitted; Prof Alexis Descatha (PhD, MD): conception and design of the study, acquisition of data, revising the article critically for important intellectual content and the final approval of the version to be submitted.

Corresponding author

Correspondence to Bertrand Porro.

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Conflict of Interest

Alexis Descatha is paid as editor in chief by Elsevier (Archives des maladies professionnelles et de l?environnement). Other authors have no conflict of interest to declare.

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Table S1:Table S2:Table S3:

Some significant job codes description Physical exposures from JEM-CONSTANCES Descriptive analysis of the test sample

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Badreau, M., Fadel, M., Roquelaure, Y. et al. Comparison of Machine Learning Methods in the Study of Cancer Survivors’ Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort. J Occup Rehabil 33, 750–756 (2023). https://doi.org/10.1007/s10926-023-10112-8

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