Accurate and Scalable Classification of Colonoscopy Neoplasia Using Machine Learning and Natural Language Processing
The random forest-based hybrid natural language processing system for classification of colonoscopy results was both accurate and explainable. NLP combined with effective machine learning algorithms can provide a scalable strategy for colonoscopy quality monitoring.
Authors
- Michael B. Wallace (ORCID: https://orcid.org/0000-0002-6446-5785)
- John B. Kisiel (ORCID: https://orcid.org/0000-0002-3840-5324)
- Jason D. Greenwood (ORCID: https://orcid.org/0000-0002-9397-0107)
- Douglas W. Mahoney (ORCID: https://orcid.org/0000-0001-5144-0645)
- Derek W. Ebner (ORCID: https://orcid.org/0000-0003-0089-2507)
- Brendan T. Broderick
- Kelli N. Burger (ORCID: https://orcid.org/0009-0003-9522-2334)
- Sushil Kumar Garg (ORCID: https://orcid.org/0000-0002-8621-0214)
- Suryakanth Gurudu
Institutions
- Mayo Clinic (US)
- Jacksonville College (US)
- WinnMed (US)
- Health First (US)
- Mayo Clinic Health System (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Clinical and Translational Gastroenterology
- Published
- 2024-12-17
- DOI
- https://doi.org/10.14309/ctg.0000000000000959
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00