Date of Award

6-13-2018

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2018.

Department

Computer Engineering

First Advisor

Yi Fang

Abstract

Prescription and over-the-counter drugs are abundant now more than ever, and many people use them on a regular basis. These drugs come with a variety of side e↵ects, ranging from common to very rare. However, the symptoms listed on drug packaging might not be the only symptoms that a consumer might experience when taking a specific drug. When experiencing a symptom, consumers might think they have a disease, turn to the Web for answers, and attempt to diagnose themselves when a disease is not necessarily the cause. Instead, these symptoms might be reactions to medications; however, the first thought that people have when experiencing a symptom is that they most likely have a disease. This often hinders us from thinking of alternative solutions. In addition, people often take multiple drugs simultaneously, which makes pinpointing the source of a bad reaction or unexpected symptom increasingly difficult. These factors make it challenging to locate the true source of a symptom. Our product, Symptom Search, is a tool that assists in this search for answers. Symptom Search uses FDA Adverse Effects Report data and machine learning to provide users with a method to search for potential root causes of their symptoms. Our system conveys how likely given drugs are correlated with given symptoms, and it suggests other products that could be triggering symptoms or reactions based on other users’ interactions with the products.

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