Automated detection of cervical spondylotic myelopathy: harnessing the power of natural language processing
The timely identification of suspected CSM allows for prompt confirmation of diagnosis and treatment. The utilization of NLP algorithm demonstrated excellent discriminatory capabilities in identifying CSM based on positive symptoms in free-text admission notes complaint data. This study showcases the potential of a pre-diagnosis system in the field of spine.
Authors
- Zhi‐Yang Xie (ORCID: https://orcid.org/0000-0001-8716-7134)
- PeiYang Wang (ORCID: https://orcid.org/0000-0002-4298-8147)
- GuanRui Ren (ORCID: https://orcid.org/0000-0003-2438-8081)
- Lei Liu (ORCID: https://orcid.org/0000-0001-9211-5371)
- Zhiwei Wang (ORCID: https://orcid.org/0000-0001-6729-2237)
- Yuntao Wang (ORCID: https://orcid.org/0000-0003-1805-1247)
- Xiaotao Wu (ORCID: https://orcid.org/0000-0001-7937-626X)
Institutions
- Zhongda Hospital Southeast University (CN)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-03-19
- DOI
- https://doi.org/10.3389/fnins.2025.1421792
- Citations
- 1
- Primary Topic
- Cervical and Thoracic Myelopathy
- Type
- article
- Field-Weighted Citation Impact
- 2.01