Denoising Clinical Notes for Medical Literature Retrieval with Convolutional Neural Model

Abstract

The rapid increase of medical literature poses a significant challenge for physicians, who have repeatedly reported to struggle to keep up to date with developments in research. This gap is one of the main challenges in integrating recent advances in clinical research with day-to-day practice. Thus, the need for clinical decision support (CDS) search systems that can retrieve highly relevant medical literature given a clinical note describing a patient has emerged. However, clinical notes are inherently noisy, thus not being fit to be used as queries as-is. In this work, we present a convolutional neural model aimed at improving clinical notes representation, making them suitable for document retrieval. The system is designed to predict, for each clinical note term, its importance in relevant documents. The approach was evaluated on the 2016 TREC CDS dataset, where it achieved a 37% improvement in infNDCG over state-of-the-art query reduction methods and a 27% improvement over the best known method for the task.