Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients

  • Haipeng Liu
  • , Wenlin Zhang
  • , Choon Hian Goh
  • , Fangyu Dai
  • , Soban Sadiq
  • , Gary Tse

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    6 Citations (Scopus)

    Abstract

    Diabetes mellitus (DM) patients are at high risk of developing multiple complications where depression is a common one. This chapter provides an up-to-date review on the diagnosis, treatment, and management of diabetes-depression comorbidity. The treatment and management of diabetes-depression comorbidity involve a combination of pharmacological, psychotherapeutic, and lifestyle interventions, which is still challenging. Recent advancements of artificial intelligence, wearable sensors, and Internet of Things (IoT) commonly contributed to the potential of early diagnosis and patient-specific treatment, as well as efficient management of diabetes-depression comorbidity. IoT-based big-data-driven clinical decision support systems may aid in addressing the limitations in current clinical practice and comprehensively improve the prognosis and living quality of DM patients with comorbid depression.

    Original languageEnglish
    Title of host publicationInternet of Things and Machine Learning for Type I and Type II Diabetes
    Subtitle of host publicationUse Cases
    Pages337-347
    Number of pages11
    ISBN (Electronic)9780323956864
    DOIs
    Publication statusPublished - 1 Jan 2024

    Keywords

    • Depression
    • Diabetes mellitis
    • Diabetes-related comorbidities
    • Internet of Things

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