A natural language processing approach to detect inconsistencies in death investigation notes attributing suicide circumstances
To conclude, we present an NLP framework to detect the annotation inconsistencies, show the effectiveness of identifying and rectifying possible label errors, and eventually propose an improvement solution to improve the coding consistency of human annotators.
Authors
- Ziqiang Han (ORCID: https://orcid.org/0000-0003-0314-0570)
- Yunyu Xiao (ORCID: https://orcid.org/0000-0002-0479-1781)
- Ying Ding (ORCID: https://orcid.org/0000-0003-1352-1000)
- Cui Tao (ORCID: https://orcid.org/0000-0002-4267-1924)
- Yifan Peng (ORCID: https://orcid.org/0000-0001-9309-8331)
- Song Wang (ORCID: https://orcid.org/0000-0002-8224-0424)
- Joydeep Ghosh (ORCID: https://orcid.org/0000-0002-7366-3548)
- Yiliang Zhou
Institutions
- Shandong University (CN)
- Cornell University (US)
- Mayo Clinic in Florida (US)
- Weill Cornell Medicine (US)
- The University of Texas at Austin (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1038/s43856-024-00631-7
- Citations
- 2
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Science Foundation
- National Institutes of Health
- National Institute on Aging
- National Institute on Drug Abuse
- National Institute of Allergy and Infectious Diseases