Machine learning-driven simplification of the hypomania checklist-32 for adolescent: a feature selection approach
The machine learning-driven 8-item version of the HCL-32 demonstrates strong diagnostic utility for adolescent bipolar disorder, offering a more efficient screening tool without sacrificing clinical sensitivity. This shortened scale may improve assessment feasibility and accuracy in clinical settings, addressing the unique challenges of diagnosing bipolar disorder in adolescents.
Authors
- Siling Tang (ORCID: https://orcid.org/0009-0008-7010-096X)
- Xiaodong Xue (ORCID: https://orcid.org/0000-0003-4559-2495)
- Guanghui Shen (ORCID: https://orcid.org/0000-0002-4253-695X)
- Hao Chen (ORCID: https://orcid.org/0009-0001-6480-7976)
- Xinwu Ye
Institutions
- Wenzhou City People's Hospital (CN)
Publication Details
- Journal
- International Journal of Bipolar Disorders
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s40345-024-00365-4
- Citations
- 2
- Primary Topic
- Bipolar Disorder and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.77
Funders
- Science and Technology Plan Project of Wenzhou, China