Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults

Sri Susanty; Herdiantri Sufriyana; Emily Chia-Yu Su; Yeu-Hui Chuang

doi:10.1371/journal.pone.0280330

Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults

PLoS One. 2023 Jan 25;18(1):e0280330. doi: 10.1371/journal.pone.0280330. eCollection 2023.

Authors

Sri Susanty^{1

2}, Herdiantri Sufriyana^{3

4}, Emily Chia-Yu Su^{3

5

6}, Yeu-Hui Chuang^{1

7}

Affiliations

¹ School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan.
² Nursing Study Program, Faculty of Medicine, Universitas Halu Oleo, Kendari, Southeast Sulawesi, Indonesia.
³ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
⁴ Department of Medical Physiology, Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, Surabaya, Indonesia.
⁵ Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
⁶ Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan.
⁷ Center for Nursing and Healthcare Research in Clinical Practice Application, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.

Abstract

The 15-item Geriatric Depression Scale (GDS-15) is widely used to screen for depressive symptoms among older populations. This study aimed to develop and validate a questionnaire-free, machine-learning model as an alternative triage test for the GDS-15 among community-dwelling older adults. The best models were the random forest (RF) and deep-insight visible neural network by internal validation, but both performances were undifferentiated by external validation. The AUROC of the RF model was 0.619 (95% CI 0.610 to 0.627) for the external validation set with a non-local ethnic group. Our triage test can allow healthcare professionals to preliminarily screen for depressive symptoms in older adults without using a questionnaire. If the model shows positive results, then the GDS-15 can be used for follow-up measures. This preliminary screening will save a lot of time and energy for healthcare providers and older adults, especially those persons who are illiterate.

Copyright: © 2023 Susanty et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Depression* / diagnosis
Ethnicity
Humans
Independent Living*
Machine Learning

Grants and funding

This study was funded by: (1) the Postdoctoral Accompanies Research Project from the National Science and Technology Council (NSTC) in Taiwan (grant no.: NSTC111-2811-E-038-003-MY2) to Herdiantri Sufriyana; and (2) the Ministry of Science and Technology (MOST) in Taiwan (grant nos.: MOST110-2628-E-038-001 and MOST111-2628-E-038-001-MY2), and the Higher Education Sprout Project from the Ministry of Education (MOE) in Taiwan (grant no.: DP2-111-21121-01-A-05) to Emily Chia-Yu Su. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.