Learning Semantic Relationships from Medical Codes

We demonstrate the value of learning dense representations (embeddings) of collections of codes representing various domains ofmo medical information. These embeddings are learned jointly using sparse representations of diagnosis, procedures and prescriptions extracted from medical claims, in order to infer semantic relationships both within, as well as between domains. We show that learning effective embeddings allows for a rich representation of a patient's clinical state at a point in time, a mechanism for assigning robust clinical similarity between patients, and a data representation which is generally useful in modeling various health care related events, such as the next most likely event (i.e. diagnosis, procedure or prescription), or the likelihood of a specific event in the future (e.g. an emergency room visit). Three methods are showcased in this paper including: general embedding, task-specific embedding, and a combination of the two which we have deemed "super" embedding for the purpose of this paper.