Automatic classification of foot examination findings using clinical notes and machine learning.
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.
Pakhomov
Serguei V S
SV
Department of Pharmaceutical Care and Health Systems, University of Minnesota, Twin Cities, MN, USA. pakh0002@umn.edu
Hanson
Penny L
PL
Bjornsen
Susan S
SS
Smith
Steven A
SA
D001185:Artificial Intelligence; D003625:Data Collection; D048909:Diabetes Complications / Q000175:diagnosis; D005240:Feasibility Studies; D005528:Foot; D005534:Foot Diseases / Q000175:diagnosis*; D006801:Humans; D016347:Medical Records Systems, Computerized; D010808:Physical Examination / Q000145:classification*; D011785:Quality Assurance, Health Care; D015203:Reproducibility of Results; D013358:Subject Headings