ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.
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Extracting information from the text of electronic medical records to improve case detection: a systematic reviewIdentifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.Developing a natural language processing application for measuring the quality of colonoscopy proceduresLumbar Imaging With Reporting Of Epidemiology (LIRE)--Protocol for a pragmatic cluster randomized trialIdentification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health RecordsCare episode retrieval: distributional semantic models for information retrieval in the clinical domainUsing large clinical corpora for query expansion in text-based cohort identificationPharmacovigilance Using Clinical NotesComparison of machine learning classifiers for influenza detection from emergency department free-text reports.An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithmsUsing multiple sources of data for surveillance of postoperative venous thromboembolism among surgical patients treated in Department of Veterans Affairs hospitals, 2005-2010.Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project.Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric HospitalPhenome-Wide Association Studies as a Tool to Advance Precision Medicine.Selecting information in electronic health records for knowledge acquisitionPatient-level temporal aggregation for text-based asthma status ascertainment.Influenza detection from emergency department reports using natural language processing and Bayesian network classifiersExtracting and integrating data from entire electronic health records for detecting colorectal cancer casesAutomated ancillary cancer history classification for mesothelioma patients from free-text clinical reportsA comparison of two approaches to text processing: facilitating chart reviews of radiology reports in electronic medical records.Extracting timing and status descriptors for colonoscopy testing from electronic medical records.Toward personalizing treatment for depression: predicting diagnosis and severity.Text mining for adverse drug events: the promise, challenges, and state of the art.Emergency Medical Text Classifier: New system improves processing and classification of triage notes.Androgen Deprivation Therapy and Future Alzheimer's Disease RiskNegation's not solved: generalizability versus optimizability in clinical natural language processing.Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical recordsAnaphoric relations in the clinical narrative: corpus creationMachine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010.ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpusUsing natural language processing to extract mammographic findings.TextHunter--A User Friendly Tool for Extracting Generic Concepts from Free Text in Clinical Research.Applying semantic-based probabilistic context-free grammar to medical language processing--a preliminary study on parsing medication sentencesUsing Medical Text Extraction, Reasoning and Mapping System (MTERMS) to process medication information in outpatient clinical notes.The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.Building an automated SOAP classifier for emergency department reportsFine-grained information extraction from German transthoracic echocardiography reportsAutomatic Classification of Ultrasound Screening Examinations of the Abdominal Aorta.Portability of an algorithm to identify rheumatoid arthritis in electronic health records.Towards automatic diabetes case detection and ABCS protocol compliance assessment.
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ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 10 May 2009
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
ConText: an algorithm for dete ...... status from clinical reports.
@en
ConText: an algorithm for dete ...... status from clinical reports.
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type
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ConText: an algorithm for dete ...... status from clinical reports.
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ConText: an algorithm for dete ...... status from clinical reports.
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ConText: an algorithm for dete ...... status from clinical reports.
@en
ConText: an algorithm for dete ...... status from clinical reports.
@nl
P2093
P2860
P1476
ConText: an algorithm for dete ...... status from clinical reports.
@en
P2093
Henk Harkema
John N Dowling
Tyler Thornblade
P2860
P304
P356
10.1016/J.JBI.2009.05.002
P577
2009-05-10T00:00:00Z