ArticleJournal of the American Medical Informatics Association : JAMIA
Extracting timing and status descriptors for colonoscopy testing from electronic medical records.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case.JMIR medical informatics · 2022Article
- ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes.Journal of biomedical informatics · 2021Article
- ConceptWAS: a high-throughput method for early identification of COVID-19 presenting symptoms.medRxiv : the preprint server for health sciences · 2020Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study.Journal of medical Internet research · 2020Article
- Data mining-based model and risk prediction of colorectal cancer by using secondary health data: A systematic review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2020Article
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Electronic Health Record-Based Registries: Clinical Research Using Registries in Colon and Rectal Surgery.Clinics in colon and rectal surgery · 2019Review
- Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions.Applied clinical informatics · 2018Article
- Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.Evidence-based mental health · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Colorectal cancer (CRC) screening rates are low despite confirmed benefits. The authors investigated the use of natural language processing (NLP) to identify previous colonoscopy screening in electronic records from a random sample of 200 patients at least 50 years old. The authors developed algorithms to recognize temporal expressions and 'status indicators', such as 'patient refused', or 'test scheduled'. The new methods were added to the existing KnowledgeMap concept identifier system, and the resulting system was used to parse electronic medical records (EMR) to detect completed colonoscopies. Using as the 'gold standard' expert physicians' manual review of EMR notes, the system identified timing references with a recall of 0.91 and precision of 0.95, colonoscopy status indicators with a recall of 0.82 and precision of 0.95, and references to actually completed colonoscopies with recall of 0.93 and precision of 0.95. The system was superior to using colonoscopy billing codes alone. Health services researchers and clinicians may find NLP a useful adjunct to traditional methods to detect CRC screening status. Further investigations must validate extension of NLP approaches for other types of CRC screening applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.