Insight ReportHow Machine Learning Can Help Identify Adverse Drug Reactions Coresight Research January 30, 2018 Executive SummaryIn this report, we explore how machine learning can help identify the possibility of adverse drug reactions (ADRs). More than half of Americans regularly take prescription drugs—four of them on average—and 75% of Americans take at least one over-the-counter drug regularly. About 53% of Americans who use prescription drugs get them from more than one provider and 35% state that a healthcare provider has never reviewed their medication to see if they could stop taking it. Annually, US hospital emergency rooms see more than1 million people for ADRs. More than a quarter of these patients need to be admitted for further treatment. Machine-learning algorithms, such as the Apriori algorithm, can run through vast, unstructured data sets of medications, conditions, drug effects, patient histories, and other variables, and calculate the probability of an ADR, to a certain degree. Already a subscriber? Log in You are currently viewing a preview of this report. Please select an access option to view the full report. Hide Options - Show Options + Get unlimited access to all our research with one of our subscription plans. View Subscription Plans or Contact us to purchase this report. Contact us ✕ This document was generated for Other research you may be interested in: Three Data Points We’re Watching This Week, Week 15: US CPG LatestUS Store Tracker Extra, November 2025: Burlington Stores Takes Total Opened Retail Space to 88 Million Square FeetWeekly US Store Openings and Closures Tracker 2025, Week 12: Forever 21 To Close All Stores; Dollar General Announces Major Store Expansion PlanUS CPG Sales Tracker: In-Store CPG Sales Fall While Beauty Remains Resilient