Abstract
Post-market drug-safety procedures, known as pharmacovigilance, require a team of skilled physicians or epidemiologists to manually investigate each adverse event. This disclosure describes artificial intelligence (AI) agent-based, cloud computing techniques that automate the end-to-end cognitive workflow of investigating potential adverse drug events (ADEs). Moving beyond current techniques, which only perform statistical signal detection, the described techniques enable the determination of biological causality. Upon receiving a statistical signal of an adverse event, plausible biological hypotheses that can explain the signal are autonomously generated. A set of specialized software agents are dispatched to forage for evidence across disparate data sources (real-world evidence platforms, scientific literature, genomic databases, etc.). The results generated by the agents are synthesized into findings that score the likelihood of a causal link. The final output is a detailed, auditable causality dossier that enables human safety experts to make faster, better-informed decisions.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singh, Teginder; Neelakandan, Ramakrishnan; Patel, Bakul; Chen, Justin; Dattani, Kiran; Hamill, Sean; and Bochove, Jacob Van, "Autonomous Causal Inference Using Artificial Intelligence Agents", Technical Disclosure Commons, (December 09, 2025)
https://www.tdcommons.org/dpubs_series/9000