Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach

Hsiao Ting Tseng; Hsiao Chi Li; Chia Lun Lo; Tai Hsiang Shen; Shu Chiung Lin

doi:10.1109/ICMLC48188.2019.8949191

Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach

Hsiao Ting Tseng, Hsiao Chi Li, Chia Lun Lo, Tai Hsiang Shen, Shu Chiung Lin

Department of Information Management

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

1 Scopus citations

Abstract

The WHO identified depressive disorder as one of the three major diseases in the 21st century and studies have shown that patients with depression are more likely than nondepression to have dementia in the future. However, although there are many related studies that point out that depressive disorder is one of the important factor of dementia, however, these findings are not consistent. In addition, there has been no study of evidence-based construction of dementia prediction model of depressive disorder patients for clinical practice. Therefore, this study will use supervised learning techniques to construct a follow-up dementia prediction model for depressive disorder patients to assist depressive disorder patients and their medical staffs to predict his/her possible risk of suffering from dementia, and then develop early intervention and prevention measures.

Original language	English
Title of host publication	Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728128160
DOIs	https://doi.org/10.1109/ICMLC48188.2019.8949191
State	Published - Jul 2019
Event	18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan Duration: 7 Jul 2019 → 10 Jul 2019

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	2019-July
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Country/Territory	Japan
City	Kobe
Period	7/07/19 → 10/07/19

Keywords

Dementia
Depressive Disorder
Disease Severity
Machine Learning

UN SDGs

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

Access to Document

10.1109/ICMLC48188.2019.8949191

Cite this

Tseng, H. T., Li, H. C., Lo, C. L., Shen, T. H., & Lin, S. C. (2019). Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019 Article 8949191 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICMLC48188.2019.8949191

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title = "Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach",

abstract = "The WHO identified depressive disorder as one of the three major diseases in the 21st century and studies have shown that patients with depression are more likely than nondepression to have dementia in the future. However, although there are many related studies that point out that depressive disorder is one of the important factor of dementia, however, these findings are not consistent. In addition, there has been no study of evidence-based construction of dementia prediction model of depressive disorder patients for clinical practice. Therefore, this study will use supervised learning techniques to construct a follow-up dementia prediction model for depressive disorder patients to assist depressive disorder patients and their medical staffs to predict his/her possible risk of suffering from dementia, and then develop early intervention and prevention measures.",

keywords = "Dementia, Depressive Disorder, Disease Severity, Machine Learning",

author = "Tseng, {Hsiao Ting} and Li, {Hsiao Chi} and Lo, {Chia Lun} and Shen, {Tai Hsiang} and Lin, {Shu Chiung}",

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Tseng, HT, Li, HC, Lo, CL, Shen, TH & Lin, SC 2019, Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach. in Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019., 8949191, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2019-July, IEEE Computer Society, 18th International Conference on Machine Learning and Cybernetics, ICMLC 2019, Kobe, Japan, 7/07/19. https://doi.org/10.1109/ICMLC48188.2019.8949191

Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach. / Tseng, Hsiao Ting; Li, Hsiao Chi; Lo, Chia Lun et al.
Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019. IEEE Computer Society, 2019. 8949191 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July).

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

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T1 - Predicting Dementia Risk to Depressive Disorder Patients

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AB - The WHO identified depressive disorder as one of the three major diseases in the 21st century and studies have shown that patients with depression are more likely than nondepression to have dementia in the future. However, although there are many related studies that point out that depressive disorder is one of the important factor of dementia, however, these findings are not consistent. In addition, there has been no study of evidence-based construction of dementia prediction model of depressive disorder patients for clinical practice. Therefore, this study will use supervised learning techniques to construct a follow-up dementia prediction model for depressive disorder patients to assist depressive disorder patients and their medical staffs to predict his/her possible risk of suffering from dementia, and then develop early intervention and prevention measures.

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KW - Disease Severity

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Tseng HT, Li HC, Lo CL, Shen TH, Lin SC. Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019. IEEE Computer Society. 2019. 8949191. (Proceedings - International Conference on Machine Learning and Cybernetics). doi: 10.1109/ICMLC48188.2019.8949191

Predicting Dementia Risk to Depressive Disorder Patients: A classification Approach

Abstract

Publication series

Conference

Keywords

UN SDGs

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