Predicting User Reported Symptoms Using a Gated Neural Network

Lahari Poddar, Wynne Hsu, Mong-Li Lee

Abstract: Detection of Adverse Drug Events (ADE) or side effects of different treatments are necessary to minimize potential health risks of patients. Given the prevalence of user reported content on the web, recent research has focused on the automatic discovery of potential side effects from these online platforms. However, it is not clear whether the symptoms that a patient experiences, are solely side effects of a particular treatment (or a combination of them), or there are other confounding factors influencing them. In this work, we characterize the reported symptoms along with their severity for patients, based on their past interactions with various treatments and their pre-existing medical conditions. We analyze a large dataset from a symptoms tracking app, and observe a strong correlation between a patient's existing health condition(s) and the symptoms he or she experiences across different treatments. We develop a multi-objective neural network with gating mechanism, to predict the possible symptoms and their overall severity level for a set of treatments, for a given patient. Experimental results demonstrate the effectiveness of our model over state-of-the-art approaches. Furthermore, our adaptation of the gating mechanism imbues the network with the ability of justifying its predictions.