Automatic Bipolar Disorder Assessment Using Machine Learning With Smartphone-Based Digital Phenotyping-Reference-Cited by-同舟云学术

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Automatic Bipolar Disorder Assessment Using Machine Learning With Smartphone-Based Digital Phenotyping

Published:2023 Issue: Volume:11 Page:121845-121858
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wu Chung-Hsien¹^ORCID,Hsu Jia-Hao¹^ORCID,Liou Cheng-Ray¹,Su Hung-YI¹,Lin Esther Ching-Lan²,Chen Po-See³^ORCID

Affiliation:

1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

2. Department of Nursing, National Cheng Kung University, Tainan, Taiwan

3. Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan

Funder

National Science and Technology Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10299670.pdf?arnumber=10299670

Reference64 articles.

1. Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

2. Speaker-dependent audio-visual emotion recognition;haq;Proc AVSP,2009

3. Predicting Depression and Emotions in the Cross-roads of Cultures, Para-linguistics, and Non-linguistics

4. Speech Emotion Recognition Using CNN

5. Using technology to improve longitudinal studies: self-reporting with ChronoRecord in bipolar disorder

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1. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy;Computers in Human Behavior;2024-12

2. PREDICTION OF PATIENT’S WILLINGNESS FOR TREATMENT OF MENTAL ILLNESS USING MACHINE LEARNING APPROACHES;Applied Computer Science;2024-06-30

3. Dynamic bidirectional associations between GPS mobility and ecological momentary assessment of mood symptoms in mood disorders (Preprint);2023-12-19

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