Coronary artery disease risk assessment from unstructured electronic health records using text mining. - Wikidata - wikimedia - TriplyDB

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

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Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records Towards Interactive Medical Content Delivery Between Simulated Body Sensor Networks and Practical Data Center.Creation of a new longitudinal corpus of clinical narratives.Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome.Automatic prediction of coronary artery disease from clinical narratives.The use of machine learning for the identification of peripheral artery disease and future mortality risk

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Coronary artery disease risk assessment from unstructured electronic health records using text mining.

http://www.wikidata.org/entity/Q37175869

description

article científic

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article scientifique

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articolo scientifico

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artigo científico

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bilimsel makale

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scientific article published on 26 August 2015

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vedecký článok

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vetenskaplig artikel

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videnskabelig artikel

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vědecký článek

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name

Coronary artery disease risk a ...... lth records using text mining.

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Coronary artery disease risk a ...... lth records using text mining.

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Coronary artery disease risk a ...... lth records using text mining.

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Coronary artery disease risk a ...... lth records using text mining.

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prefLabel

Coronary artery disease risk a ...... lth records using text mining.

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Coronary artery disease risk a ...... lth records using text mining.

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J.JBI.2015.08.003

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Journal of Biomedical Informatics

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Coronary artery disease risk a ...... alth records using text mining

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Hong-Jie Dai

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Manish Kumar

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Nai-Wen Chang

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Pradeep Ray

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Siaw-Teng Liaw

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P2860

Prediction of Coronary Heart Disease Using Risk Factor Categories

Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

A context-aware approach for progression tracking of medical concepts in electronic medical records

General cardiovascular risk profile for use in primary care: the Framingham Heart Study

Global mortality, disability, and the contribution of risk factors: Global Burden of Disease Study

Mortality by cause for eight regions of the world: Global Burden of Disease Study

Combining knowledge and data driven insights for identifying risk factors using electronic health records

A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data.

Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration.

An electronic clinical decision support tool to assist primary care providers in cardiovascular disease risk management: development and mixed methods evaluation.

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10.1016/J.JBI.2015.08.003

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58 Suppl

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Jitendra Jonnagaddala

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2015-08-28T00:00:00Z

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electronic health record

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