Researchers at Vanderbilt University Medical Center have used natural language processing technology in an electronic medical records system to identify patients with multiple sclerosis and collect data on traits of their disease course.
The work is significant, researchers say, because much remains unknown about the course of the disease, which varies widely among patients. Most research studies have focused on the origin of the disease, partly because of the difficulty in ascertaining sufficient longitudinal clinical data to study the disease course, according to the study published in the Journal of the American Medical Informatics Association. Electronic medical records may provide such a tool. We have previously shown that genomic signals of MS risk may be replicated using EMR-derived cohorts. In this paper, we evaluated algorithms to extract detailed clinical information for the disease course of MS.
The study used algorithms based on ICD-9 codes, text keywords and medications to identify 5,789 patients with MS, and collected detailed data on the clinical course of the patients disease to measure progression of disability. For all clinical traits extracted, precision was at least 87 percent and specificity was greater than 80 percent.
Many studies have identified individuals serving as cases and controls for disease status using EMR data, the study notes. This is one of the first studies to focus on specific traits of a disease by text mining of the EMR. A few other studies have used text mining approaches to extract blood pressures, pacemaker implantations and left ventricular ejection fractions as a marker of heart failure. We have shown that detailed clinical information valuable to research studies is recorded in medical records of individuals with MS, and that this information can be extracted in a highly reliable manner.
The study, Automated Extraction of Clinical Traits of Multiple Sclerosis in Electronic Medical Records, is available here.
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