NDLI: Deep learning approach to detection of colonoscopic information from unstructured reports

Content Provider	Springer Nature : BioMed Central
Author	Seong, Donghyeong Choi, Yoon Ho Shin, Soo-Yong Yi, Byoung-Kee
Abstract	Background Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. Methods This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. Results The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. Conclusions This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-023-02121-7.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-023-02121-7
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-02-07
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Natural language processing Deep learning Data processing Information extraction Colonoscopy
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach

Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers

Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text

Combining structured and unstructured data for predictive models: a deep learning approach

Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach

ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports

Natural language processing data services for healthcare providers

Deep learning approach to detection of colonoscopic information from unstructured reports

Similar Documents

Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach

Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers

Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text

Combining structured and unstructured data for predictive models: a deep learning approach

Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach

ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports

Natural language processing data services for healthcare providers

Deep learning approach to detection of colonoscopic information from unstructured reports