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Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management

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Journal of Occupational Rehabilitation Aims and scope Submit manuscript

Abstract

Purpose

This paper aims to illustrate an example of how to set up a work injury database: the Smart Work Injury Management (SWIM) system. It is a secure and centralized cloud platform containing a set of management tools for data storage, data analytics, and machine learning. It employs artificial intelligence to perform in-depth analysis via text-mining techniques in order to extract both dynamic and static data from work injury case files. When it is fully developed, this system can provide a more accurate prediction model for cost of work injuries. It can also predict return-to-work (RTW) trajectory and provide advice on medical care and RTW interventions to all RTW stakeholders. The project will comprise three stages. Stage one: to identify human factors in terms of both facilitators and barriers RTW through face-to-face interviews and focus group discussions with different RTW stakeholders in order to collect opinions related to facilitators, barriers, and essential interventions for RTW of injured workers; Stage two: to develop a machine learning model which employs artificial intelligence to perform in-depth analysis. The technologies used will include: 1. Text-mining techniques including English and Chinese work segmentation as well as N-Gram to extract both dynamic and static data from free-style text as well as sociodemographic information from work injury case files; 2. Principle component/independent component analysis to identify features of significant relationships with RTW outcomes or combine raw features into new features; 3. A machine learning model that combines Variational Autoencoder, Long and Short Term Memory, and Neural Turning Machines. Stage two will also include the development of an interactive dashboard and website to query the trained machine learning model. Stage three: to field test the SWIM system.

Conclusion

SWIM ia secure and centralized cloud platform containing a set of management tools for data storage, data analytics, and machine learning. When it is fully developed, SWIM can provide a more accurate prediction model for the cost of work injuries and advice on medical care and RTW interventions to all RTW stakeholders.

Ethics

The project has been approved by the Ethics Committee for Human Subjects at the Hong Kong Polytechnic University and is funded by the Innovation and Technology Commission (Grant # ITS/249/18FX).

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Funding

This project was funded by Innovation and Technology Commission (Grant Number ITS/249/18FX).

Author information

Authors and Affiliations

  1. Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Andy S. K. Cheng

  2. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Peter H. F. Ng & Zackary P. T. Sin

  3. Total Rehabilitaton Management (HK) Limited, Wanchai Road, Wanchai, Hong Kong

    Sun H. S. Lai

  4. Department of Orthopaedics & Traumatology, Alice Ho Miu Ling Nethersole Hospital/Tai Po Hospital, Tai Po, NT, Hong Kong

    S. W. Law

Authors
  1. Andy S. K. Cheng
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  2. Peter H. F. Ng
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  3. Zackary P. T. Sin
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  4. Sun H. S. Lai
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  5. S. W. Law
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Corresponding author

Correspondence to Andy S. K. Cheng.

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Conflict of interest

The authors have no conflict of interest to declare.

Ethical Approval

All procedures performed in this project were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the project.

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Cheng, A.S.K., Ng, P.H.F., Sin, Z.P.T. et al. Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management. J Occup Rehabil 30, 354–361 (2020). https://doi.org/10.1007/s10926-020-09886-y

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  • DOI: https://doi.org/10.1007/s10926-020-09886-y

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