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Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: A cross-sectional observational study
Zhou Y, Han W, Yao X, Xue J, Li Z, Li Y.. International journal of nursing studies, 2023-07-07
Topic: Risk, prevention and care in population studies
Background Depression, anxiety, and apathy are highly prevalent in older people with preclinical dementia and mild cognitive impairment. These symptoms have also proven valuable in predicting the progression from mild cognitive impairment to dementia, enabling a timely diagnosis and treatment. However, objective and reliable indicators to detect and distinguish depression, anxiety, and apathy are relatively scarce. Objective This study aimed to develop a machine learning model to detect and distinguish depression, anxiety, and apathy based on speech and facial expressions. Design An observational, cross-sectional study design. Setting(s) The memory outpatient department of a tertiary hospital. Participants 319 older adults diagnosed with mild cognitive impairment. Methods Depression, anxiety, and apathy were evaluated by the Public Health Questionnaire, General Anxiety Disorder, and Apathy Evaluation Scale, respectively. Speech and facial expressions of older adults with mild cognitive impairment were digitally captured using audio and video recording software. Open-source data analysis toolkits were utilized to extract speech, facial, and text features. The multiclass classification was used to develop classification models, and shapely additive explanations were used to explain the contribution of each feature within the model. Results The random forest method was used to develop a multiclass emotion classification model, which performed well in classifying emotions with a weighted-average F1 score of 96.6 %. The model also demonstrated high accuracy, precision, and recall, with 87.4 %, 86.6 %, and 87.6 %, respectively. Conclusions The machine learning model developed in this study demonstrated strong classification performance in detecting and differentiating depression, anxiety, and apathy. This innovative approach combines text, audio, and video to provide objective methods for precise classification and remote monitoring of these symptoms in nursing practice. Registration This study was registered at the Chinese Clinical Trial Registry (registration number: ChiCTR1900023892; registration date: June 19th, 2019).
Key findings
- Results The random forest method was used to develop a multiclass emotion classification model, which performed well in classifying emotions with a weighted-average F1 score of 96.6 %.
- The model also demonstrated high accuracy, precision, and recall, with 87.4 %, 86.6 %, and 87.6 %, respectively.
- Conclusions The machine learning model developed in this study demonstrated strong classification performance in detecting and differentiating depression, anxiety, and apathy.
Why it matters
A machine learning model using speech and facial expressions detected and distinguished depression, anxiety and apathy in 319 older adults with mild cognitive impairment, with a weighted-average F1 score of 96.6%. This is about detecting these symptoms, which can help predict progression to dementia, but it does not test any treatment, cure or prevention.
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Study details
- Study designcross-sectional
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for sample size, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (International journal of nursing studies) |
| Journal's citation record | 2 / 4 | International Journal of Nursing Studies: journal h-index 210 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (pubmed indexes it as a clinical or observational study) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 60 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.0 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 14.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 3 / 4 | Cited 60 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Yingxin Li: h-index 26 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Shanghai Jiao Tong University: institution h-index 975 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37531702) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 52 / 100 | — |
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APA 7th
Zhou, Y., Han, W., Yao, X., Xue, J., Li, Z., & Li, Y. (2023). Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: A cross-sectional observational study. International journal of nursing studies. https://doi.org/10.1016/j.ijnurstu.2023.104562
Vancouver
Zhou Y, Han W, Yao X, Xue J, Li Z, Li Y. Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: A cross-sectional observational study. International journal of nursing studies. 2023. doi:10.1016/j.ijnurstu.2023.104562. PMID: 37531702.
BibTeX
@article{zhou2023-37531702,
author = {Zhou, Y. and Han, W. and Yao, X. and Xue, J. and Li, Z. and Li, Y.},
title = {{Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: A cross-sectional observational study}},
journal = {International journal of nursing studies},
year = {2023},
doi = {10.1016/j.ijnurstu.2023.104562},
pmid = {37531702},
url = {https://doi.org/10.1016/j.ijnurstu.2023.104562}
}
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