Track 2: Business & Applications – Wednesday 8 November
13:30 – 14:15
Building Text Analysis Models to Understand & Predict Adverse Events Occurring With FDA-Approved Drugs
Various drugs approved by the FDA have been associated with adverse medical events in patients. These events are reported to the FDA through different sources, including physicians at hospitals and clinics, pharmacists, and patients. Most of these adverse events are documented in texts. However, manual analysis of these texts for root cause are time consuming and produce qualitative results at best. This session shows how building text analysis models in order to understand the themes related to drug adverse events can help us quantitatively understand which adverse events are most common within a cluster of FDA-approved drugs.
Presented by: Qais Hatim
A New Way of Working Graph & Semantics, Text Analytics, & Linked Data
Using case studies of real-world client projects, Smartlogic’s CEO presents, discusses, and demonstrates how post-relational databases, text analytics, AI, semantics, and linked data are delivering rapid returns on investment in data intensive industries. Cases range from predictive analytics and financial risk assessment to compliance, superior superior customer service, and unified enterprise intelligence within industries including banking, life sciences, media, and healthcare. The talk looks at the technology, the opportunity, lessons learned, and the keys to project success.
Presented by: Jeremy Bentley