🤖 AI Frontiers hosted by NLP/LLM Interest Group

Abstract: Real-world data (RWD) studies and target trial emulation (TTE) are becoming increasingly important for comparative effectiveness, safety evaluation, and regulatory-grade real-world evidence. Yet in practice, turning a clinical question into a credible TTE still requires substantial manual effort: investigators must define cohorts, compose statistical analysis plans (SAPs), align time zero and follow-up, execute analyses, and document design decisions in a form that is both review-ready and interpretable. Recent agentic systems automate parts of this workflow, but they often remain fragmented and rarely treat debiasing—especially negative-control-based adjustment for residual and potentially unmeasured confounding—as a core design objective.We present MATTE (Multi-agent Target Trial Emulation), an end-to-end agentic TTE framework that links hypothesis interpretation, protocol and SAP generation, cohort construction, effect estimation, negative control outcome (NCO)-guided debiasing, and FDA-aware compliance auditing within a single traceable workflow. By integrating study design, bias assessment, and compliance auditing into one human-reviewable pipeline, MATTE aims to make RWD-based evidence generation more scalable, interpretable, and audit-ready for translational and regulatory use. This talk describes the current MATTE system snapshot and its expected evaluation behavior while full benchmarking is ongoing.

Howard Chan Tsai Hor, PhD, is a Postdoctoral Researcher in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine, University of Pennsylvania, working with Dr. Yong Chen. His research focuses on agentic AI for biomedical evidence generation, real-world data analysis, and target trial emulation using large-scale electronic health records. He develops methods that combine causal inference, trustworthy machine learning, negative control outcome calibration, and automated statistical analysis planning to support scalable, interpretable, and audit-ready real-world evidence workflows. Howard has authored more than 16 peer-reviewed papers at leading AI/ML venues, including the International Conference on Learning Representations (ICLR), and in journals including IEEE Transactions on Medical Imaging (TMI) and IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).