AIMC Topic: Electronic Health Records

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Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis.

Journal of biomedical semantics May 10, 2016
BACKGROUND: In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the manag...
Natural Language Processing Government Agencies Stroke Phenotype Veterans Humans Data Mining Electronic Health Records Carotid Stenosis Algorithms Risk Factors
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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

PloS one May 2, 2016
OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop a...
Machine Learning Algorithms Electronic Health Records Humans Primary Health Care Antirheumatic Agents Arthritis, Rheumatoid
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Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic.

Artificial intelligence in medicine Apr 28, 2016
BACKGROUND: Many electronic infection detection systems employ dichotomous classification methods, classifying patient data as pathological or normal with respect to one or several types of infection. An electronic monitoring and surveillance system ...
Adult Diagnosis, Computer-Assisted Fuzzy Logic Intensive Care Units Electronic Health Records Humans Automation Clinical Laboratory Information Systems Cross Infection Data Mining
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A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports.

PloS one Apr 28, 2016
Large volumes of data are continuously generated from clinical notes and diagnostic studies catalogued in electronic health records (EHRs). Echocardiography is one of the most commonly ordered diagnostic tests in cardiology. This study sought to expl...
Natural Language Processing Information Storage and Retrieval Aged Male Echocardiography Female Reproducibility of Results Humans Electronic Health Records
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Electronic medical record phenotyping using the anchor and learn framework.

Journal of the American Medical Informatics Association : JAMIA Apr 23, 2016
BACKGROUND: Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient's electronic medical record...
Decision Making, Computer-Assisted Emergency Service, Hospital Databases as Topic Machine Learning Phenotype Natural Language Processing Area Under Curve Humans Electronic Health Records
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Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine Apr 13, 2016
OBJECTIVE: Delayed diagnosis of Kawasaki disease (KD) may lead to serious cardiac complications. We sought to create and test the performance of a natural language processing (NLP) tool, the KD-NLP, in the identification of emergency department (ED) ...
Humans Electronic Health Records Natural Language Processing Emergency Service, Hospital Data Mining Child Mucocutaneous Lymph Node Syndrome Sensitivity and Specificity
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Developing a web-based SKOS editor.

Journal of biomedical semantics Apr 4, 2016
BACKGROUND: The Simple Knowledge Organization System (SKOS) was introduced to the wider research community by a 2005 World Wide Web Consortium (W3C) working draft, and further developed and refined in a 2009 W3C recommendation. Since then, SKOS has b...
Electronic Health Records Natural Language Processing Software Internet Vocabulary, Controlled
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Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.

Artificial intelligence in medicine Mar 31, 2016
OBJECTIVE: Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploit...
Natural Language Processing Humans Referral and Consultation Electronic Health Records ROC Curve Colorectal Neoplasms
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An automated knowledge-based textual summarization system for longitudinal, multivariate clinical data.

Journal of biomedical informatics Mar 30, 2016
OBJECTIVES: Design and implement an intelligent free-text summarization system: The system's input includes large numbers of longitudinal, multivariate, numeric and symbolic clinical raw data, collected over varying periods of time, and in different ...
Patient Discharge Knowledge Bases Automation Electronic Health Records Natural Language Processing Machine Learning Software Humans
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Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning.

Journal of the American Medical Informatics Association : JAMIA Mar 28, 2016
OBJECTIVE: To help cancer registrars efficiently and accurately identify reportable cancer cases.
International Classification of Diseases Pathology, Clinical Mandatory Reporting Natural Language Processing Data Mining Humans Machine Learning Neoplasms United States Electronic Health Records
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