Large Scale Diagnostic Code Classification for Medical Patient Records.

A critical, yet not very well studied problem in medical applications is the issue of accu- rately labeling patient records according to diagnoses and procedures that patients have undergone. This labeling problem, known as coding, consists of assigning standard medi- cal codes (ICD9 and CPT) to patient records. Each patient record can have several corre- sponding labels/codes, many of which are correlated to specific diseases. The cur- rent, most frequent coding approach involves manual labeling, which requires considerable human effort and is cumbersome for large patient databases. In this paper we view medical coding as a multi-label classification problem, where we treat each code as a label for patient records. Due to government regu- lations concerning patient medical data, pre- vious studies in automatic coding have been quite limited. In this paper, we compare two efficient algorithms for diagnosis coding on a large patient dataset.