  In order to submit a claim to insurance companies, a doctor needs to code a
patient encounter with both the diagnosis (ICDs) and procedures performed
(CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant
procedures code is a cumbersome and time-consuming task as a doctor has to
choose from around 13,000 procedure codes with no predefined one-to-one
mapping. In this paper, we propose a state-of-the-art deep learning method for
automatic and intelligent coding of procedures (CPTs) from the diagnosis codes
(ICDs) entered by the doctor. Precisely, we cast the learning problem as a
multi-label classification problem and use distributed representation to learn
the input mapping of high-dimensional sparse ICDs codes. Our final model
trained on 2.3 million claims is able to outperform existing rule-based
probabilistic and association-rule mining based methods and has a recall of
90@3.
