ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2011
Using Medical Text Extraction, Reasoning and Mapping System (MTERMS) to process medication information in outpatient clinical notes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03911687 (Communicating Narrative Concerns Entered by RNs), which is not on this map. Cited by 50 papers, 2 of them syntheses that pooled it.
What it found
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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.
Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System
Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
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- Predicting Behavioral Determinants of Health from Clinical Text Using Transformer Models and BiLSTM.medRxiv : the preprint server for health sciences · 2025Article
- An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- CD-Tron: Leveraging large clinical language model for early detection of cognitive decline from electronic health records.Journal of biomedical informatics · 2025Article
- CD-Tron: Leveraging Large Clinical Language Model for Early Detection of Cognitive Decline from Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Building an allergy reconciliation module to eliminate allergy discrepancies in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Extracting Cognitive Impairment Assessment Information From Unstructured Notes in Electronic Health Records Using Natural Language Processing Tools: Validation with Clinical Assessment Data.Clinical epidemiology · 2025Article
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- Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records.medRxiv : the preprint server for health sciences · 2024Article
- Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- Leveraging natural language processing to augment structured social determinants of health data in the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Scalable Feature Engineering from Electronic Free Text Notes to Supplement Confounding Adjustment of Claims-Based Pharmacoepidemiologic Studies.Clinical pharmacology and therapeutics · 2023Article
- Post-Acute COVID-19 Respiratory Symptoms in Patients With Asthma: An Electronic Health Records-Based Study.The journal of allergy and clinical immunology. In practice · 2023Observational
- Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients.BMC medical informatics and decision making · 2023Article
- Identifying and Reconciling Patients' Allergy Information Within the Electronic Health Record.Studies in health technology and informatics · 2022Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Clinical information is often coded using different terminologies, and therefore is not interoperable. Our goal is to develop a general natural language processing (NLP) system, called Medical Text Extraction, Reasoning and Mapping System (MTERMS), which encodes clinical text using different terminologies and simultaneously establishes dynamic mappings between them. MTERMS applies a modular, pipeline approach flowing from a preprocessor, semantic tagger, terminology mapper, context analyzer, and parser to structure inputted clinical notes. Evaluators manually reviewed 30 free-text and 10 structured outpatient clinical notes compared to MTERMS output. MTERMS achieved an overall F-measure of 90.6 and 94.0 for free-text and structured notes respectively for medication and temporal information. The local medication terminology had 83.0% coverage compared to RxNorm's 98.0% coverage for free-text notes. 61.6% of mappings between the terminologies are exact match. Capture of duration was significantly improved (91.7% vs. 52.5%) from systems in the third i2b2 challenge.
Indexed as
Identifiers
22195230PMC3243163What 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.