Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach
The two-step hybrid model ("LSTM + ML") effectively predicted depression over 2 years via demographic and health data, aiding early diagnosis and intervention.
Authors
- Jingwen Zhou (ORCID: https://orcid.org/0000-0002-5470-9424)
- Ning Xue (ORCID: https://orcid.org/0000-0003-4643-5053)
- Minghui Zhang (ORCID: https://orcid.org/0000-0002-6158-7605)
- Ling Zhang (ORCID: https://orcid.org/0000-0001-5304-5569)
- Z.W. Zhong (ORCID: https://orcid.org/0000-0002-0762-3580)
- Ruigang Wei
- Xue Che
- Lin Tan (ORCID: https://orcid.org/0000-0002-6690-8332)
Institutions
- Jiangxi University of Finance and Economics (CN)
Publication Details
- Journal
- BMC Psychology
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1186/s40359-025-02691-3
- Citations
- 4
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 11.75
Funders
- Jiangxi University of Finance and Economics
- Jilin Office of Philosophy and Social Science