ArticleJournal of the American Medical Informatics Association : JAMIA
Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents.
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 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- High alert drugs screening using gradient boosting classifier.Scientific reports · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- sig2db: a Workflow for Processing Natural Language from Prescription Instructions for Clinical Data Warehouses.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- An annotation and modeling schema for prescription regimens.Journal of biomedical semantics · 2019Article
- The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records.Frontiers in medicine · 2019Article
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study.Journal of medical Internet research · 2017Article
- Congestive heart failure information extraction framework for automated treatment performance measures assessment.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Comparison of UMLS terminologies to identify risk of heart disease using clinical notes.Journal of biomedical informatics · 2015Article
- A hybrid model for automatic identification of risk factors for heart disease.Journal of biomedical informatics · 2015Article
- Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes.Journal of biomedical informatics · 2015Article
- Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.Journal of biomedical informatics · 2015Review
- Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system.Journal of biomedical semantics · 2015Article
- Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents.Studies in health technology and informatics · 2015Article
- Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features.BMC medical informatics and decision making · 2013Article
- Clinical research informatics: a conceptual perspective.Journal of the American Medical Informatics Association : JAMIA · 2012Article
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Authors and funding
5 authors.
Funding
Abstract
unlabelledOBJECTIVE To describe a new medication information extraction system-Textractor-developed for the 'i2b2 medication extraction challenge'. The development, functionalities, and official evaluation of the system are detailed.
designTextractor is based on the Apache Unstructured Information Management Architecture (UMIA) framework, and uses methods that are a hybrid between machine learning and pattern matching. Two modules in the system are based on machine learning algorithms, while other modules use regular expressions, rules, and dictionaries, and one module embeds MetaMap Transfer. MEASUREMENTS: The official evaluation was based on a reference standard of 251 discharge summaries annotated by all teams participating in the challenge. The metrics used were recall, precision, and the F(1)-measure. They were calculated with exact and inexact matches, and were averaged at the level of systems and documents.
resultsThe reference metric for this challenge, the system-level overall F(1)-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F(1)-measure about 86%), and was good for dosage and frequency information, with F(1)-measures of about 82-85%. Results were not as good for durations, with F(1)-measures of 36-39%, and for reasons, with F(1)-measures of 24-27%.
conclusionThe official evaluation of Textractor for the i2b2 medication extraction challenge demonstrated satisfactory performance. This system was among the 10 best performing systems in this challenge.
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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.