Using Structured EHR Data and SVM to Support ICD-9-CM Coding

This study proposes a methodology to support coding professionals in assigning ICD-9-CM codes to inpatient episodes. This subject has been predominantly addressed through the use of natural language processing methods, which show limited generalizability. To surpass this issue, this paper proposes a methodology entailing an adaptive data processing method based on structured electronic health record data, whereby raw clinical data is mapped into a feature set, and based on which supervised learning algorithms are trained. After applying a filter method for feature selection, support vector machine (SVM) classifiers are trained to obtain predictions for assigning codes to each episode. This approach is tested using a dataset of inpatient episodes from a department of Internal Medicine. Classifiers exhibited F1-measure values around 52%. Recall was generally higher than precision, which is considered valuable for coding support purposes. Analyzing results on an individual code basis sheds light on some key-issues regarding the use of structured electronic health record data in supporting clinical coding.