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

doi:10.1016/B978-0-323-95686-4.00024-1

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

Centre for Intelligent Healthcare

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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 language	English
Title of host publication	Internet of Things and Machine Learning for Type I and Type II Diabetes
Subtitle of host publication	Use cases
Editors	Sujata Dash, Subhendu Kumar Pani, Willy Susilo, Bernard Man Yung Cheung, Gary Tse
Publisher	Elsevier
Chapter	24
Pages	337-347
Number of pages	11
Edition	1
ISBN (Print)	978-0-323-95686-4
DOIs	https://doi.org/10.1016/B978-0-323-95686-4.00024-1
Publication status	Published - 19 Jul 2024

Keywords

Depression
Diabetes mellitis
Diabetes-related comorbidities
Internet of Things

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/B978-0-323-95686-4.00024-1

Cite this

Liu, H., Zhang, W., Goh, C.-H., Dai, F., Sadiq, S., & Tse, G. (2024). Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients. In S. Dash, S. K. Pani, W. Susilo, B. M. Y. Cheung, & G. Tse (Eds.), Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases (1 ed., pp. 337-347). Elsevier. https://doi.org/10.1016/B978-0-323-95686-4.00024-1

Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients. / Liu, Haipeng; Zhang, Wenlin; Goh, Choon-Hian et al.
Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases. ed. / Sujata Dash; Subhendu Kumar Pani; Willy Susilo; Bernard Man Yung Cheung; Gary Tse. 1. ed. Elsevier, 2024. p. 337-347.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Liu, H, Zhang, W, Goh, C-H, Dai, F, Sadiq, S & Tse, G 2024, Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients. in S Dash, SK Pani, W Susilo, BMY Cheung & G Tse (eds), Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases. 1 edn, Elsevier, pp. 337-347. https://doi.org/10.1016/B978-0-323-95686-4.00024-1

Liu H, Zhang W, Goh CH, Dai F, Sadiq S, Tse G. Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients. In Dash S, Pani SK, Susilo W, Cheung BMY, Tse G, editors, Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases. 1 ed. Elsevier. 2024. p. 337-347 doi: 10.1016/B978-0-323-95686-4.00024-1

Liu, Haipeng ; Zhang, Wenlin ; Goh, Choon-Hian et al. / Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients. Internet of Things and Machine Learning for Type I and Type II Diabetes: Use cases. editor / Sujata Dash ; Subhendu Kumar Pani ; Willy Susilo ; Bernard Man Yung Cheung ; Gary Tse. 1. ed. Elsevier, 2024. pp. 337-347

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AB - 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.

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

Abstract

Keywords

UN SDGs

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