PMC/PubMed Indexed Articles
Indexed In
- Open J Gate
- Genamics JournalSeek
- JournalTOCs
- Ulrich's Periodicals Directory
- RefSeek
- Hamdard University
- EBSCO A-Z
- OCLC- WorldCat
- Publons
- Geneva Foundation for Medical Education and Research
- Euro Pub
- Google Scholar
Useful Links
Share This Page
Journal Flyer
Open Access Journals
- Agri and Aquaculture
- Biochemistry
- Bioinformatics & Systems Biology
- Business & Management
- Chemistry
- Clinical Sciences
- Engineering
- Food & Nutrition
- General Science
- Genetics & Molecular Biology
- Immunology & Microbiology
- Medical Sciences
- Neuroscience & Psychology
- Nursing & Health Care
- Pharmaceutical Sciences
Shashank Reddy Vadyala
Department of Computational Analysis and Modelling, Louisiana Tech University, Ruston, LA, United States
Publications
-
Research Article
Natural Language Processing Accurately Categorizes Indications, Findings and Pathology Reports from Multicentre Colonoscopy
Author(s): Shashank Reddy Vadyala*
Colonoscopy is used for Colorectal Cancer (CRC) screening. Extracting details of the colonoscopy findings from free text in Electronic Health Records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies. We developed and evaluated the accuracy of a deep learning model framework to extract information for the clinical decision support system to interpret relevant free-text reports, including indications, pathology, and findings notes. The Bio-Bi-LSTM-CRF framework was developed using Bidirectional Long Short-term Memory (Bi-LSTM) and Conditional Random Fields (CRF) to extract several clinical features from these free-text reports including indications for the colonoscopy, findings during the colonoscopy, and pathology of resected material. We trained the Bio-Bi-LSTM-CRF and existing Bi-LSTM-CRF models on 80% of 4,000 manually ann.. View more»