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Automatic Classification of Foot Examination Findings Using Clinical Notes and Machine Learning

^a Department of Pharmaceutical Care and Health Systems, University of Minnesota, Twin Cities, MN
^b Department of Health Care Policy and Research, Mayo Clinic, Rochester, MN
^c Department of Endocrinology, Mayo Clinic, Rochester, MN.

^_* Correspondence: Serguei V.S. Pakhomov, PhD, 7-125F Weaver-Densford Hall, 308 Harvard Street S.E., Minneapolis, MN 55455 (Email: pakh0002{at}umn.edu).

Received for publication: 08/07/07; accepted for publication: 12/10/07.

We examine the feasibility of a machine learning approach to identification of foot examination (FE) findings from the unstructured text of clinical reports. A Support Vector Machine (SVM) based system was constructed to process the text of physical examination sections of in- and out-patient clinical notes to identify if the findings of structural, neurological, and vascular components of a FE revealed normal or abnormal findings or were not assessed. The system was tested on 145 randomly selected patients for each FE component using 10-fold cross validation. The accuracy was 80%, 87% and 88% for structural, neurological, and vascular component classifiers, respectively. Our results indicate that using machine learning to identify FE findings from clinical reports is a viable alternative to manual review and warrants further investigation. This application may improve quality and safety by providing inexpensive and scalable methodology for quality and risk factor assessments at the point of care.