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Deep learning-based physiological risk stratification in night-shift hospital workers
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  • Published: 16 March 2026

Deep learning-based physiological risk stratification in night-shift hospital workers

  • InHo Lee1,2,
  • SangHee Hong3,
  • JuneHee Lee4,
  • HwaYoung Lee5,
  • SoonChan Kwon1,
  • YoungSun Min1,
  • EunChul Jang1 &
  • …
  • JeongBeom Lee2,6 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Health care
  • Health occupations
  • Medical research

Abstract

This cross-sectional study of 2,250 hospital workers attempted to identify potential physiological risk structures for night workers by utilizing the nonlinear manifold learning framework. Unlike conventional mean-centered linear analysis, this study applied the Potential of Heat-diffusion for Affinity-based Transition Embedding (PHATE) for topological visualization and Neural Additive Models (NAM) to predict interpretable risk. The ‘cholesterol homeostasis profile’ was defined as the main latent indicator. Analysis showed that the systemic metabolic load (PHATE-1) was higher after age correction, despite the night-shift group being younger than the non-night shift group (p < 0.001). This axis showed a positive correlation with body mass index (r = 0.71) and triglyceride (r = 0.56). The NAM model captured the risk slope with an average AUC of 0.864 (range 0.832–0.901) and confirmed a nonlinear risk transition when it exceeded the triglyceride 2 mmol/L threshold. The predicted risk of night workers in the highest risk cluster (cluster 1) was 0.71, and the metabolic load and cholesterol homeostasis profile risk were increased. Ultimately, the PHATE-NAM framework moves beyond traditional group averages. It provides a purely data-driven way to pinpoint individual physiological vulnerabilities, giving hospitals the exact evidence needed to design safer, more sustainable shift-work environments.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to the institution’s Electronic Medical Record (EMR) data protection policy regarding participant privacy. However, the raw data supporting the conclusions of this article are available from the corresponding author (JeongBeom Lee, leejb@sch.ac.kr) upon reasonable request and subject to Institutional Review Board (IRB) approval. To fully comply with the journal’s Code Availability policy and support research reproducibility, the official Python implementation of the analytical pipeline (including PHATE, LightGBM, NAM, and VaDE models) has been made publicly available. The code repository can be accessed at: https://github.com/Crescendo-OEM/DL-Physiological-Risk-Stratification. To ensure permanent accessibility, the version of the code used in this study is also archived at: https://doi.org/10.5281/zenodo.18872957.

Abbreviations

BMI:

body mass index

LDL/HDL:

Low-/high-density lipoprotein

G-GTP:

Gamma-glutamyl transpeptidase

ALT/AST:

Alanine/aspartate aminotransferase

ROC:

Receiver operating characteristic

AUC:

Area under the curve

References

  1. Pan, A., Schernhammer, E. S., Sun, Q. & Hu, F. B. Rotating night shift work and risk of type 2 diabetes: two prospective cohort studies in women. PLoS Med. 8, e1001141. https://doi.org/10.1371/journal.pmed.1001141 (2011).

    Google Scholar 

  2. Wang, F. et al. Meta-analysis on night shift work and risk of metabolic syndrome. Obes. Rev. 15, 709–720. https://doi.org/10.1111/obr.12194 (2014).

    Google Scholar 

  3. Morris, C. J., Purvis, T. E., Hu, K. & Scheer, F. A. J. L. Circadian misalignment increases cardiovascular disease risk factors in humans. Proc. Natl. Acad. Sci. USA 113, E1402–E1411 (2016). https://doi.org/10.1073/pnas.1516953113

  4. Leproult, R., Holmbäck, U. & Van Cauter, E. Circadian misalignment augments markers of insulin resistance and inflammation, independently of sleep loss. Diabetes 63, 1860–1869. https://doi.org/10.2337/db13-1546 (2014).

    Google Scholar 

  5. Kecklund, G. & Axelsson, J. Health consequences of shift work and insufficient sleep. BMJ 355, i5210. https://doi.org/10.1136/bmj.i5210 (2016).

    Google Scholar 

  6. International Agency for Research on Cancer. Night Shift Work (IARC Monographs on the Identification of Carcinogenic Hazards to Humans, Vol. 124). IARC, (2020). https://publications.iarc.fr/593

  7. International Labour Organization. Night Work Convention, 1990 (No. 171). ILO, (1990). https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C171

  8. Caruso, C. C. et al. NIOSH Training for Nurses on Shift Work and Long Work Hours (DHHS (NIOSH) Publication No. 2015 – 115, National Institute for Occupational Safety and Health, 2015). https://www.cdc.gov/niosh/docs/2015-115/

  9. Vetter, C. et al. Night shift work, genetic risk, and type 2 diabetes in the UK Biobank. Diabetes Care. 41, 762–769. https://doi.org/10.2337/dc17-1933 (2018).

    Google Scholar 

  10. Puttonen, S., Härmä, M. & Hublin, C. Shift work and cardiovascular disease: pathways from circadian stress to morbidity. Scand. J. Work Environ. Health. 36, 96–108. https://doi.org/10.5271/sjweh.2894 (2010).

    Google Scholar 

  11. Sooriyaarachchi, P., Jayawardena, R., Pavey, T. & King, N. A. Shift work and the risk for metabolic syndrome among healthcare workers: a systematic review and meta-analysis. Obes. Rev. 23, e13489. https://doi.org/10.1111/obr.13489 (2022).

    Google Scholar 

  12. Biglari, H. et al. Relationship between occupational stress and cardiovascular diseases risk factors in drivers. Int. J. Occup. Med. Environ. Health. 29, 895–901. https://doi.org/10.13075/ijomeh.1896.00125 (2016).

    Google Scholar 

  13. Khaleghi, S. et al. Association between blood pressure and oral temperature rate with sleepiness changes among clinical night workers. Iran. J. Public. Health. 49, 2232–2234. https://doi.org/10.18502/ijph.v49i11.4747 (2020).

    Google Scholar 

  14. Saberinia, A. et al. Investigation of relationship between occupational stress and cardiovascular risk factors among nurses. Iran. J. Public. Health. 49, 1954–1958. https://doi.org/10.18502/ijph.v49i10.4699 (2020).

    Google Scholar 

  15. Foster, R. G. Sleep, circadian rhythms and health. Interface Focus. 10, 20190098. https://doi.org/10.1098/rsfs.2019.0098 (2020).

    Google Scholar 

  16. Shen, Y. et al. Personalized physician-assisted sleep advice for shift workers: algorithm development and validation study. JMIR Form. Res. 9, e65000. https://doi.org/10.2196/65000 (2025).

    Google Scholar 

  17. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492. https://doi.org/10.1038/s41587-019-0336-3 (2019).

    Google Scholar 

  18. Agarwal, R. et al. Neural additive models: interpretable machine learning with neural nets. Adv. Neural Inf. Process. Syst. 34, 4699–4711. https://doi.org/10.48550/arXiv.2004.13912 (2021).

    Google Scholar 

  19. Jiang, S. et al. Enhancing vitiligo stage diagnosis through a reliable multimodal model with uncertainty calibration. Appl. Intell. 55, 1002. https://doi.org/10.1007/s10489-025-06839-x (2025).

    Google Scholar 

  20. Wang, Y., Kung, L., Wang, W. Y. C. & Cegielski, C. G. An integrated big data analytics-enabled transformation model: application to healthcare. Inf. Manag. 55, 64–79. https://doi.org/10.1016/j.im.2017.04.001 (2018).

    Google Scholar 

  21. Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N Engl. J. Med. 380, 1347–1358. https://doi.org/10.1056/NEJMra1814259 (2019).

    Google Scholar 

  22. Sadeghi, Z. et al. A review of explainable artificial intelligence in healthcare. Comput. Electr. Eng. 118, 109370. https://doi.org/10.1016/j.compeleceng.2024.109370 (2024).

    Google Scholar 

  23. Morris, C. J. et al. Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans. Proc. Natl. Acad. Sci. USA 112, E2225–E2234 (2015). https://doi.org/10.1073/pnas.1418955112

  24. Chellappa, S. L., Vujovic, N., Williams, J. S. & Scheer, F. A. J. L. Impact of circadian disruption on cardiovascular function and disease. Trends Endocrinol. Metab. 30, 767–779. https://doi.org/10.1016/j.tem.2019.07.008 (2019).

    Google Scholar 

  25. Mavaddat, N. et al. Prediction of breast cancer risk based on profiling with common genetic variants. J. Natl. Cancer Inst. 107, djv036. https://doi.org/10.1093/jnci/djv036 (2015).

    Google Scholar 

  26. Rose, G. Sick individuals and sick populations. Int. J. Epidemiol. 14, 32–38. https://doi.org/10.1093/ije/14.1.32 (1985).

    Google Scholar 

  27. Knutsson, A. Methodological issues in shift work research. Occup. Med. (Lond). 54, 193–198. https://doi.org/10.1093/occmed/kqh039 (2004).

    Google Scholar 

  28. McEwen, B. S. Protective and damaging effects of stress mediators. N Engl. J. Med. 338, 171–179. https://doi.org/10.1056/NEJM199801153380307 (1998).

    Google Scholar 

  29. Vyas, M. V. et al. Shift work and vascular events: systematic review and meta-analysis. BMJ 345, e4800 (2012). https://doi.org/10.1136/bmj.e4800

  30. Reutrakul, S. & Knutson, K. L. Consequences of circadian disruption on cardiometabolic health. Sleep. Med. Clin. 10, 455–468. https://doi.org/10.1016/j.jsmc.2015.07.005 (2015).

    Google Scholar 

  31. Czeisler, C. A. Perspective: casting light on sleep deficiency. Nature 497, 13. https://doi.org/10.1038/497S13a (2013).

    Google Scholar 

  32. Buxton, O. M. et al. Adverse metabolic consequences in humans of prolonged sleep restriction combined with circadian disruption. Sci. Transl Med. 4, 129ra43. https://doi.org/10.1126/scitranslmed.3003200 (2012).

    Google Scholar 

  33. Wong, I. S., McLeod, C. B. & Demers, P. A. Shift work trends and risk of work injury among Canadian workers. Scand. J. Work Environ. Health. 37, 54–61. https://doi.org/10.5271/sjweh.3124 (2011).

    Google Scholar 

  34. Huang, H., Liu, Z., Xie, J. & Xu, C. Association between night shift work and NAFLD: a prospective analysis of 281,280 UK Biobank participants. BMC Public. Health. 23, 1282. https://doi.org/10.1186/s12889-023-16204-7 (2023).

    Google Scholar 

  35. De Bacquer, D. et al. Rotating shift work and the metabolic syndrome: a prospective study. Int. J. Epidemiol. 38, 848–854. https://doi.org/10.1093/ije/dyn360 (2009).

    Google Scholar 

  36. Partch, C. L., Green, C. B. & Takahashi, J. S. Molecular architecture of the mammalian circadian clock. Trends Cell. Biol. 24, 90–99. https://doi.org/10.1016/j.tcb.2013.07.002 (2014).

    Google Scholar 

  37. Koruca, H. İ., Emek, M. S. & Gulmez, E. Development of a personalized staff-scheduling method with work-life balance perspective: case of a hospital. Ann. Oper. Res. https://doi.org/10.1007/s10479-023-05244-2 (2023).

    Google Scholar 

  38. Whittaker, D. S. et al. Circadian modulation by time-restricted feeding rescues brain pathology and improves memory in mouse models of Alzheimer’s disease. Cell. Metab. 35, 1704–1721e6. https://doi.org/10.1016/j.cmet.2023.07.014 (2023).

    Google Scholar 

  39. Adamovich, Y. et al. Oxygen and carbon dioxide rhythms are circadian clock controlled and differentially directed by behavioral signals. Cell. Metab. 29, 1092–1103e3. https://doi.org/10.1016/j.cmet.2019.01.007 (2019).

    Google Scholar 

  40. Tian, H., Zhao, X., Zhang, Y. & Xia, Z. Research progress of circadian rhythm in cardiovascular disease: a bibliometric study. Heliyon 10, e28738. https://doi.org/10.1016/j.heliyon.2024.e28738 (2024).

    Google Scholar 

  41. Gachon, F. et al. Potential bidirectional communication between the liver and the central circadian clock in MASLD. NPJ Metab. Health Dis. 3, 15. https://doi.org/10.1038/s44324-025-00058-1 (2025).

    Google Scholar 

  42. Shi, S. Q. et al. Circadian disruption leads to insulin resistance and obesity. Curr. Biol. 23, 372–381. https://doi.org/10.1016/j.cub.2013.01.048 (2013).

    Google Scholar 

  43. Chellappa, S. L., Morris, C. J. & Scheer, F. A. J. L. Daily circadian misalignment impairs human cognitive performance task-dependently. Sci. Rep. 8, 3041. https://doi.org/10.1038/s41598-018-20707-4 (2018).

    Google Scholar 

  44. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 285, 2486–2497. https://doi.org/10.1001/jama.285.19.2486 (2001).

    Google Scholar 

  45. Zhang, L., Sun, D. M., Li, C. B. & Tao, M. F. Influencing factors for sleep quality among shift-working nurses. Asian Nurs. Res. 10, 277–282. https://doi.org/10.1016/j.anr.2016.09.002 (2016).

    Google Scholar 

  46. Eastman, C. I. How to reduce circadian misalignment in rotating shift workers. ChronoPhysiol Ther. 6, 41–46. https://doi.org/10.2147/CPT.S111424 (2016).

    Google Scholar 

  47. Chellappa, S. L. et al. Daytime eating during simulated night work mitigates changes in cardiovascular risk factors. Nat. Commun. 16, 57846. https://doi.org/10.1038/s41467-025-57846-y (2025).

    Google Scholar 

  48. Molzof, H. E. et al. Nightshift work and nighttime eating are associated with higher insulin and leptin levels in hospital nurses. Front. Endocrinol. 13, 876752. https://doi.org/10.3389/fendo.2022.876752 (2022).

    Google Scholar 

  49. Ezerins, M. E. et al. Advancing safety analytics: a diagnostic framework for assessing system readiness within occupational safety and health. Saf. Sci. 146, 105569. https://doi.org/10.1016/j.ssci.2021.105569 (2022).

    Google Scholar 

  50. Baron, K. G. & Reid, K. J. Circadian misalignment and health. Int. Rev. Psychiatry. 26, 139–154. https://doi.org/10.3109/09540261.2014.911149 (2014).

    Google Scholar 

  51. Salehi, A. & Khedmati, M. Identifying at-risk patients for congenital heart disease using integrated predictive models and fuzzy clustering analysis: A cross-sectional study. Heliyon 10, e39609. https://doi.org/10.1016/j.heliyon.2024.e39609 (2024).

    Google Scholar 

  52. Epstein, M., Arakelian, E., Tucker, P. & Dahlgren, A. Managing sustainable working hours within participatory scheduling for nurses and assistant nurses: a qualitative interview study. J. Nurs. Manag. 2023 (8096034). https://doi.org/10.1155/2023/8096034 (2023).

  53. Lee, I. et al. Metabolic risk stratification of night shift workers in a large retail workplace through clustering and SHAP interpretation. Front. Public. Health. 13, 1704046. https://doi.org/10.3389/fpubh.2025.1704046 (2026).

    Google Scholar 

  54. Chemkomnerd, N. et al. A scenario-driven simulation approach to sustainable hospital resource management: aging society, pandemic preparedness and referral enhancement. BMC Health Serv. Res. 25, 982. https://doi.org/10.1186/s12913-025-13221-7 (2025).

    Google Scholar 

  55. European Agency for Safety and Health at Work (EU-OSHA). Current and emerging issues in the healthcare sector, including home and community care (Publications Office of the European Union, 2014). https://doi.org/10.2802/72773

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Acknowledgements

We extend our thanks to the subjects whose participation made this study possible.

Funding

This research was supported by the Soonchunhyang University Research Fund. The funder had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Occupational and Environmental Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea

    InHo Lee, SoonChan Kwon, YoungSun Min & EunChul Jang

  2. Department of Physiology, College of Medicine, Soonchunhyang University, Cheonan, 31151, Republic of Korea

    InHo Lee & JeongBeom Lee

  3. Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, Republic of Korea

    SangHee Hong

  4. Department of Occupational and Environmental Medicine, Soonchunhyang University Hospital, Seoul, Republic of Korea

    JuneHee Lee

  5. Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, 31151, Republic of Korea

    HwaYoung Lee

  6. Advanced Pharmacology and Physiology Integrated Sciences (AXIS), College of Medicine, Soonchunhyang University, Cheonan, 31151, Republic of Korea

    JeongBeom Lee

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Contributions

Conceptualization: EC.J., JB.L., and IH.L.; Methodology: IH.L., JB.L., SH.H., and EC.J.; Software: IH.L.; Validation: JB.L. and EC.J.; Formal analysis: IH.L. and JB.L.; Investigation: IH.L., SH.H., JH.L., and HY.L.; Resources: SC.K., YS.M., and EC.J.; Data Curation: IH.L. and SH.H.; Visualization: IH.L.; Supervision: JB.L. and EC.J.; Writing – original draft: IH.L.; Writing – review & editing: IH.L., JB.L., and EC.J.

Corresponding authors

Correspondence to EunChul Jang or JeongBeom Lee.

Ethics declarations

Competing interest

The authors declare no competing interests.

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Not applicable.

Ethical approval and consent to participate

All participants provided written informed consent, and the study protocol was reviewed and approved by the Soonchunhyang University Cheonan Hospital Institutional Review Board (IRB No. SCHCA-2025-10-010).

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Cite this article

Lee, I., Hong, S., Lee, J. et al. Deep learning-based physiological risk stratification in night-shift hospital workers. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43982-y

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  • Received: 21 November 2025

  • Accepted: 09 March 2026

  • Published: 16 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-43982-y

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Keywords

  • Night-shift
  • PHATE-NAM analysis
  • Accumulated night exposure
  • Circadian misalignment
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