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Automatic Quality of Life Prediction Using Electronic Medical Records

By Serguei Pakhomov, Nilay Shah, Penny Hanson, Saranya Balasubramaniam and Steven A. Smith

Abstract

Health related quality of life (HRQOL) is an important variable used as a risk factor for prognosis and as an outcome in clinical studies and for quality improvement. We explore the use of a general purpose natural language processing system (Metamap) in combination with Support Vector Machines (SVM) for predicting patient responses on standardized HRQOL assessment instruments from the text of physician’s notes. We surveyed 669 patients in the Mayo Clinic diabetes registry using two instruments designed to assess functioning: EuroQoL5D and SF36/SD6. Clinical notes for these patients were represented as sets of medical concepts using Metamap. SVM classifiers were trained using various feature selection strategies. The best concordance between the HRQOL instruments and automatic classification was achieved along the “pain” dimension (positive agreement –.76, negative agreement – .78, kappa – .54) using Metamap. We conclude that clinician’s notes may be used to develop a surrogate measure of patient’s HRQOL status

Topics: Articles
Publisher: American Medical Informatics Association
OAI identifier: oai:pubmedcentral.nih.gov:2656045
Provided by: PubMed Central
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