An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records

Ramakanth Kavuluru, Anthony Rios, Yuan Lu
2015 Artificial Intelligence in Medicine  
: Diagnosis codes are assigned to medical records in healthcare facilities by trained coders by reviewing all physician authored documents associated with a patient's visit. This is a necessary and complex task involving coders adhering to coding guidelines and coding all assignable codes. With the popularity of electronic medical records (EMRs), computational approaches to code assignment have been proposed in the recent years. However, most efforts have focused on single and often short
more » ... al narratives, while realistic scenarios warrant full EMR level analysis for code assignment. training dataset size, for general EMRs, label calibration to select the optimal number of labels is an indispensable final step.
doi:10.1016/j.artmed.2015.04.007 pmid:26054428 pmcid:PMC4605853 fatcat:sxqh2dxh35dntcaw2ayvu4e3se