ReviewJournal of biomedical informatics2018
Clinical information extraction applications: A literature review.
Review in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 347 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
347 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- Clinical and operational insights from data-driven care pathway mapping: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Identification of patients with epilepsy using automated electronic health records phenotyping.Epilepsia · 2023Trial
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Expanding research on clinical agent data to a virtual space of location, time, and context: exploration of data structures and limitations using a pragmatic digital twin.International journal of computer assisted radiology and surgery · 2026Article
- Exploratory Implementation and Feasibility Report of CLASS (Clinical LLM Abstraction & Structuring System), A Large Language Model Pipeline for Extracting Unstructured Data From Clinical Notes.Journal of medical systems · 2026Article
- A framework for extraction of clinical information from radiological mammography reports using large language models and retrieval augmented generation.BMC medical informatics and decision making · 2026Article
- Less Is More: Radiologists Prefer Brief Narratives in LLM-Generated Clinical History Summaries.Journal of imaging informatics in medicine · 2026Article
- A Secure, Scalable Large Language Model-Based System (CIDER) for High-Throughput Clinical Data Extraction From Medical Reports: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- OphthoChat: A HIPAA-Compliant, Artificial Intelligence-Driven Natural-Language Chatbot for Ophthalmic Electronic Medical Record Querying and Analysis.Ophthalmology science · 2026Article
- Distilling Knowledge in Gastroenterology: An Artificial Intelligence System for Assisting Colonoscopy and Pathology Report Review.Mayo Clinic proceedings. Digital health · 2026Article
- A machine-assisted framework for systematic error analysis in clinical concept extraction.Nature communications · 2026Article
- Using Natural Language Processing to Identify Adverse Drug Events Characterized by Medication Replacement in Primary Care Electronic Medical Records: Algorithm and Validation Study.Journal of medical Internet research · 2026Article
- The word and the way: strategies for domain-specific BERT pre-training in German medical NLP.BMC medical informatics and decision making · 2026Article
- Comparison of Machine Learning Algorithms for Predicting Spine Surgery Duration.Medicina (Kaunas, Lithuania) · 2026Article
- Extracting Medical Information From Unstructured Clinical Text Using Large Language Models to Enhance Health Care Interoperability: Proof-of-Concept Study.Journal of medical Internet research · 2026Article
- Large language model ensemble for automated TNM staging from radiology reports.Bioinformatics (Oxford, England) · 2026Article
- Development and implementation of a data parsing protocol for companion animal cancer data.Veterinary pathology · 2026Article
287 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundWith the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.
objectivesIn this literature review, we present a review of recent published research on clinical information extraction (IE) applications.
methodsA literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library.
resultsA total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations.
conclusionsClinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.