4 citations · 7 across the 5 of their papers we have counts for
5 papers
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
Cameron Cagan, Pedram Fard, Jiazi Tian +3
Clinical notes contain many of the signs and symptoms that bring patients to care, yet this information rarely reaches structured fields. Existing extraction approaches either rely…
Optimization Instability in Autonomous Agentic Workflows for Clinical Symptom Detection
Cameron Cagan, Pedram Fard, Jiazi Tian +3
Autonomous agentic workflows that iteratively refine their own behavior hold considerable promise, yet their failure modes remain poorly characterized. We investigate optimization…
The Average Patient Fallacy
Alaleh Azhir, Shawn N. Murphy, Hossein Estiri
Machine learning in medicine is typically optimized for population averages. This frequency weighted training privileges common presentations and marginalizes rare yet clinically c…
An Agentic AI Workflow for Detecting Cognitive Concerns in Real-world Data
Jiazi Tian, Liqin Wang, Pedram Fard +7
Early identification of cognitive concerns is critical but often hindered by subtle symptom presentation. This study developed and validated a fully automated, multi-agent AI workf…
tSPM+; a high-performance algorithm for mining transitive sequential patterns from clinical data
Jonas Hügel, Ulrich Sax, Shawn N. Murphy +1
The increasing availability of large clinical datasets collected from patients can enable new avenues for computational characterization of complex diseases using different analyti…