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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article numbere0280330
JournalPLoS ONE
Volume18
Issue number1 January
DOIs
Publication statusPublished - Jan 2023

ASJC Scopus subject areas

  • General

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