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Recent advancements in medical entity linking have been applied in the areaof scientific literature and social media data. However, with the adoption oftelemedicine and conversational agents such as Alexa in healthcare settings,medical name inference has become an important task. Medication name inferenceis the task of mapping user friendly medication names from a free-form text toa concept in a normalized medication list. This is challenging due to thedifferences in the use of medical terminology from health care professionalsand user conversations coming from the lay public. We begin with mappingdescriptive medication phrases (DMP) to standard medication names (SMN). Giventhe prescriptions of each patient, we want to provide them with the flexibilityof referring to the medication in their preferred ways. We approach this as aranking problem which maps SMN to DMP by ordering the list of medications inthe patient's prescription list obtained from pharmacies. Furthermore, weleveraged the output of intermediate layers and performed medicationclustering. We present the Medication Inference Model (MIM) achievingstate-of-the-art results. By incorporating medical entities based attention, wehave obtained further improvement for ranking models.

Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network