Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105364
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Rehabilitation Sciences | - |
dc.contributor | Department of Computing | - |
dc.creator | Ng, PHF | - |
dc.creator | Chen, PQ | - |
dc.creator | Sin, ZPT | - |
dc.creator | Lai, SHS | - |
dc.creator | Cheng, ASK | - |
dc.date.accessioned | 2024-04-12T06:51:58Z | - |
dc.date.available | 2024-04-12T06:51:58Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105364 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Ng PHF, Chen PQ, Sin ZPT, Lai SHS, Cheng ASK. Smart Work Injury Management (SWIM) System: A Machine Learning Approach for the Prediction of Sick Leave and Rehabilitation Plan. Bioengineering. 2023; 10(2):172 is available at https://doi.org/10.3390/bioengineering10020172. | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Electronic health record | en_US |
dc.subject | Interactive dashboard | en_US |
dc.subject | Rehabilitation case management | en_US |
dc.subject | Rehabilitation plan | en_US |
dc.subject | Variational autoencoder | en_US |
dc.subject | Work injury | en_US |
dc.title | Smart Work Injury Management (SWIM) system : a machine learning approach for the prediction of sick leave and rehabilitation plan | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.3390/bioengineering10020172 | - |
dcterms.abstract | As occupational rehabilitation services are part of the public medical and health services in Hong Kong, work-injured workers are treated along with other patients and are not considered a high priority for occupational rehabilitation services. The idea of a work trial arrangement in the private market occurred to meet the need for a more coordinated occupational rehabilitation practice. However, there is no clear service standard in private occupational rehabilitation services nor concrete suggestions on how to offer rehabilitation plans to injured workers. Electronic Health Records (EHRs) data can provide a foundation for developing a model to improve this situation. This project aims at using a machine-learning-based approach to enhance the traditional prediction of disability duration and rehabilitation plans for work-related injury and illness. To help patients and therapists to understand the machine learning result, we also developed an interactive dashboard to visualize machine learning results. The outcome is promising. Using the variational autoencoder, our system performed better in predicting disability duration. We have around 30% improvement compared with the human prediction error. We also proposed further development to construct a better system to manage the work injury case. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Bioengineering, Feb. 2023, v. 10, no. 2, 172 | - |
dcterms.isPartOf | Bioengineering | - |
dcterms.issued | 2023-02 | - |
dc.identifier.scopus | 2-s2.0-85149065143 | - |
dc.identifier.eissn | 2306-5354 | - |
dc.identifier.artn | 172 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Innovation and Technology Commission | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
bioengineering-10-00172-v2.pdf | 5.77 MB | Adobe PDF | View/Open |
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