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

TL;DR

This paper introduces a neural network model for mapping layperson medication descriptions to standard medication names, improving medication inference in telemedicine and conversational healthcare applications.

Contribution

The paper proposes the Medication Inference Model (MIM), a novel entity-boosted two-tower neural network that enhances medication name inference accuracy through entity-based attention and clustering.

Findings

Achieved state-of-the-art ranking performance in medication inference

Incorporating medical entity attention improved model accuracy

Clustering intermediate representations enhanced medication mapping

Abstract

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