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The Innovation Medicine > 2025 Vol. 3 > No. 1 > 100106
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An electronic health record-linked machine learning tool for diabetes risk assessment in adults with prediabetes

  • 1.

    School of Nursing, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China

  • 2.

    Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China

  • 3.

    Department of Applied Mathematics, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China

  • 4.

    Research Centre for Textile of Future Fashion, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China

  • 5.

    Joint Research Centre for Primary Health Care, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China

  • 6.

    School of Optometry, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China

  • 7.

    Department of Family Medicine & Primary Health Care, New Territories West Cluster, Hospital Authority, Hong Kong SAR 999077, China

  • 8.

    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston 02115, USA

  • 9.

    School of Public Health, Sun Yat-Sen University, Guangzhou 510275, China

  • 10.

    Department of Rehabilitation Sciences, the Hong Kong Polytechnic University, Hong Kong SAR 999077, China

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  • Corresponding author: l.yang@polyu.edu.hk 
  • Received Date:  September 25, 2024
    Accepted Date:  December 09, 2024
    Published Online:  December 17, 2024
The Innovation Medicine  3(1),  https://doi.org/10.59717/j.xinn-med.2024.100106  |  Cite this article
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    • REFERENCES
  • [1] Zheng Y., Ley S. H. and Hu F. B. (2018). Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat. Rev. Endocrinol. 14:88−98. DOI:10.1038/nrendo.2017.151

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    [2] IDF. (2021). IDF Diabetes Atlas. https://www.ncbi.nlm.nih.gov/books/NBK581934/.

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    [6] Hong H. (2023). Diabetes Care for Adults in Primary Care Settings. https://www.healthbureau.gov.hk/phcc/rfs/src/pdfviewer/web/pdf/diabetescare/en/coredocuments/15_en_diabetes_care.pdf.

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    [7] Stone M. S., Martyn L. and Weaver C. M. (2016). Potassium intake, bioavailability, hypertension, and glucose control. Nutrients 8:444. DOI:10.3390/nu8070444

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    [8] Heianza Y., Hara S., Arase Y., et al. (2011). Low serum potassium levels and risk of type 2 diabetes: The Toranomon Hospital Health Management Center Study 1 (TOPICS 1). Diabetologia 54:762−766. DOI:10.1007/s00125-010-2029-9

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    [9] Sudlow C., Gallacher J., Allen N., et al. (2015). UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. Plos Med. 12:e1001779. DOI:10.1371/journal.pmed.1001779

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  • Cite this article:

    Lu J., Lu S., Zhao Y., et al. (2025). An electronic health record-linked machine learning tool for diabetes risk assessment in adults with prediabetes. The Innovation Medicine 3:100106. https://doi.org/10.59717/j.xinn-med.2024.100106
    Lu J., Lu S., Zhao Y., et al. (2025). An electronic health record-linked machine learning tool for diabetes risk assessment in adults with prediabetes. The Innovation Medicine 3:100106. https://doi.org/10.59717/j.xinn-med.2024.100106

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