Making health data reportable: using AI to encode free-text drug records

Concomitant medications (conmeds) are routinely collected as part of a clinical trial, bringing greater context to observed outcomes and potentially revealing events of interest that could be missed through other means. This type of data is often collected in a free-text manner without reference to master drug list. Consequently, the captured drug data is nonstandard and contains spelling errors, extraneous information and a mixture of trade and generic drug names. We show how a simple neural network model trained on freely available drug data can assist humans in rapidly coding manually entered drug names to Anatomic Therapeutic Classification codes, which are an accepted standard of encoding a drug ingredient in terms of its target organ or system and therapeutic effect. In its present form, this system will be applied to aid in the manual drug coding process to finalise our conmed dataset for the ASpririn in Reducing Events in the Elderly clinical trial.