Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. - Wikidata - wikimedia - TriplyDB

Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.

Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.

http://www.wikidata.org/entity/Q33252003

about

SHRINE: enabling nationally scalable multi-site disease studies Extracting information from the text of electronic medical records to improve case detection: a systematic review Coreference resolution: a review of general methodologies and applications in the clinical domain Mining electronic health records: towards better research applications and clinical care Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records Computing health quality measures using Informatics for Integrating Biology and the Bedside Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text Limbic system white matter microstructure and long-term treatment outcome in major depressive disorder: a diffusion tensor imaging study using legacy data.Computer-assisted update of a consumer health vocabulary through mining of social network data Feasibility of studying brain morphology in major depressive disorder with structural magnetic resonance imaging and clinical data from the electronic medical record: a pilot study.A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports.Characterization of patients who suffer asthma exacerbations using data extracted from electronic medical records.Tracking medical students' clinical experiences using natural language processing What can natural language processing do for clinical decision support?A clinical use case to evaluate the i2b2 Hive: predicting asthma exacerbations.A highly specific algorithm for identifying asthma cases and controls for genome-wide association studies Tailoring vocabularies for NLP in sub-domains: a method to detect unused word sense Asthma-susceptibility variants identified using probands in case-control and family-based analyses.Comprehensive genetic assessment of a functional TLR9 promoter polymorphism: no replicable association with asthma or asthma-related phenotypes.Representation of information about family relatives as structured data in electronic health records Development and validation of a computer-based algorithm to identify foreign-born patients with HIV infection from the electronic medical record.On experiences of i2b2 (Informatics for integrating biology and the bedside) database with Japanese clinical patients' data.Entity recognition from clinical texts via recurrent neural network.Contribution of high plasma triglycerides and low high-density lipoprotein cholesterol to residual risk of coronary heart disease after establishment of low-density lipoprotein cholesterol control Using machine learning for concept extraction on clinical documents from multiple data sources.MedXN: an open source medication extraction and normalization tool for clinical text Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases A survey of informatics platforms that enable distributed comparative effectiveness research using multi-institutional heterogenous clinical data.MedEx: a medication information extraction system for clinical narratives.Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC)Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2).Chapter 13: Mining electronic health records in the genomics era.Elevated rates of intracerebral hemorrhage in individuals from a US clinical care HIV cohort.Association of immunologic and virologic factors with myocardial infarction rates in a US healthcare system.Discovering peripheral arterial disease cases from radiology notes using natural language processing.

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P2860

Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.

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BMC Medical Informatics and Decision Making

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Extracting principal diagnosis ...... al language processing system.

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Margarita Sordo

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Qing T Zeng

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Ross Lazarus

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Scott Weiss

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Sergey Goryachev

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Shawn N Murphy

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P2860

Measuring diagnoses: ICD code accuracy

Automation of a problem list using natural language processing.

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

A comparison of the Charlson comorbidities derived from medical language processing and administrative data.

Extracting structured information from free text pathology reports.

Failure analysis of MetaMap Transfer (MMTx).

Evaluation of Medical Problem Extraction from Electronic Clinical Documents Using MetaMap Transfer (MMTx).

Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon.

On experiences of i2b2 (Informatics for integrating biology and the bedside) database with Japanese clinical patients' data.

Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors.

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10.1186/1472-6947-6-30

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6

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16872495

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1553439

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