Predicting Depression Among Chinese Patients with Narcolepsy Type 1: A Machine-Learning Approach
This study highlights the potential of ML models for predicting depression in NT1 patients. The SVM model shows promise in identifying patients at high risk of depression, offering a foundation for developing a data-driven, personalized decision-making tool. Further research should validate these findings in diverse populations and include additional psychological variables to enhance model accuracy.
Authors
- Fulong Xiao (ORCID: https://orcid.org/0000-0001-5933-4249)
- H Wang (ORCID: https://orcid.org/0000-0002-6484-7441)
- Zhaoyan Feng
- Bei Li (ORCID: https://orcid.org/0000-0003-3356-389X)
- Mengmeng Wang (ORCID: https://orcid.org/0000-0003-0587-4439)
- Fang Han
- Shuai Wu
Institutions
- Peking University (CN)
- Peking University People's Hospital (CN)
Publication Details
- Journal
- Nature and Science of Sleep
- Published
- 2024-09-01
- DOI
- https://doi.org/10.2147/nss.s468748
- Citations
- 3
- Primary Topic
- Sleep and Wakefulness Research
- Type
- article
- Field-Weighted Citation Impact
- 1.03
Funders
- China Association for Science and Technology
- National Natural Science Foundation of China
- Peking University