Displaying results 1 - 3 of 3
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Using clinician mental models to guide annotation of medically unexplained symptoms and syndromes found in VA clinical documents
Content Type: Abstract
Medically unexplained syndromes (MUS) are conditions that are diagnosed on the basis of symptom constellations and are characterized by a lack of well-defined pathogenic pathways. The three most common MUS are chronic fatigue … read more… will support building a reference standard used for training and evaluation of a Natural Language Processing … will support building a reference standard used for training and evaluation of a Natural Language Processing … of clinician annotators. Annotated data can be used to train NLP tools for related surveillance tasks including … -
Automated Detection of GI Syndrome using Structured and Non-Structured Data from the VA EMR
Content Type: Abstract
Objective We performed a gold-standard manual chart review for gastro-intestinal syndrome to evaluate automated detection models based on both structured and non-structured data extracted from the VA … read more… models included “GI” ICD-9 code sets used in the “ESSENCE” and the “BioSense” syndromic surveillance systems. … on the index visit day of fever > 37.8ºC. RESULTS The ESSENCE and BioSense ICD-9 code sets detected 242 GI … -
Using NLP on VA Electronic Medical Records to Facilitate Epidemiologic Case Investigations
Content Type: Abstract
A major goal of biosurveillance is the timely detection of an infectious disease outbreak. Once a disease has been identified, another very important goal is to find all known cases of the disease to assist public health… read more… the first stage, we used structured data and BioSense and ESSENCE ICD 9 codes on records during the study period …