Client success story
Adverse Event Root Cause Analysis
Business Value Delivered: Higher Drug Approval Rates
Summary: Using AI To Identify Adverse Event Root Causes
Identifying the root causes of drug adverse events is a complex task that can benefit from a combination of AI methods spanning machine learning, natural language processing (NLP), knowledge graphs, and causal inference techniques
Challenge: Data Needed For AE Analysis Is Complex
The data required for the identification of the root cause of drug adverse events is complex and heterogeneous, and includes health records, trial data and biomedical literature. Ad-hoc methods risk missing signals and not robustly interpreting time-series data to extract causal relationships. Not identifying the root cause risks it recurring, affecting drug approvals and impacting drug labelling.
Solution: Applying AI and Machine Learning Can Find Root Cause
bPrescient’s team has experience using machine learning, natural language processing, knowledge graphs, and causal inference techniques to tackle analysis of adverse events. Linking event data to existing biological knowledge and accurately moving beyond correlation to causation are critical components of an effective strategy and can improve trial success rates and reduce post-approval risk profiles.
Outcome: Reliable AE Root Cause Identification Speeds Approval
bPrescient’s team can bring to bear modern AI and machine learning techniques to the complex identification of adverse event root causes, incorporating fine-tuning that allows focus on different disease areas and drug modalities.
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