Extraction of SNOMED concepts from medical record texts.
Clinicians have traditionally documented patient data using natural language text. With the increasing prevalence of computer systems in health care, an increasing amount of medical record text will be stored electronically. However, for such textual documents to be indexed, shared, and processed adequately by computers, it will be important to be able to identify concepts in the documents using a common medical terminology. Automated methods for extracting concepts in a standard terminology would enhance retrieval and analysis of medical record data. This paper discusses a method for extracting concepts from medical record documents using the medical terminology SNOMED-III (Systematized Nomenclature of Human and Veterinary Medicine, Version III). The technique employs a linear least squares fit that maps training set phrases to SNOMED concepts. This mapping can be used for unknown text inputs in the same domain as the training set to predict SNOMED concepts that are contained in the document. We have implemented the method in the domain of congestive heart failure for history and physical exam texts. Our system has a reasonable response time. We tested the system over a range of thresholds. The system performed with 90% sensitivity and 83% specificity at the lowest threshold, and 42% sensitivity and 99.9% specificity at the highest threshold.
Oliver
D E
DE
Section on Medical Informatics, Stanford University School of Medicine, CA 94305.
Altman
R B
RB
D000465:Algorithms; D005069:Evaluation Studies as Topic; D006333:Heart Failure / Q000145:classification; D006801:Humans; D016018:Least-Squares Analysis; D008491:Medical Informatics Applications; D008499:Medical Records / Q000145:classification*; D012680:Sensitivity and Specificity; D012984:Software; D013358:Subject Headings; D009626:Terminology as Topic