Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review-Reference-Cited by-同舟云学术

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Published:2023-08-02 Issue: Volume: Page:
ISSN:
Container-title:2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE)
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Author:

Elwirehardja Gregorius Natanael¹,Isnan Mahmud²,Perbangsa Anzaludin Samsinga³,Muchtar Kahlil⁴,Pardamean Bens⁵

Affiliation:

1. Bina Nusantara University,School of Computer Science,Computer Science Department,Jakarta,Indonesia,11480

2. Bina Nusantara University,Bioinformatics and Data Science Research Center,Jakarta,Indonesia,11480

3. Bina Nusantara University,School of Information Systems,Information System Department,Jakarta,Indonesia,11480

4. Universitas Syiah Kuala,Department of Electrical and Computer Engineering,Banda Aceh,Indonesia,23111

5. Bina Nusantara University,BINUS Graduate Program -Master of Computer Science,Computer Science Department,Jakarta,Indonesia,11480

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10249207/10249013/10249859.pdf?arnumber=10249859

Reference34 articles.

1. Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis

2. Development of an Auxiliary Platform (Mentali) for the Primary Screening of Anxiety and Depression in Young Adults

3. Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

4. Unimodal vs. Multimodal Prediction of Antenatal Depression from Smartphone-based Survey Data in a Longitudinal Study

5. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

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