3 citations · 6 across the 5 of their papers we have counts for
5 papers
Morphology signal in whole slide image foundation models can automatically triage slides
Ayushi Sinha, Shashank Yadav, Benjamin Holmes +9
Patient exams in the cancer diagnosis and staging process typically generate several whole slide images (WSIs). One of the initial steps in training models on WSI data is identifyi…
RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale
Jason Holmes, Yuexing Hao, Mariana Borras-Osorio +22
Manual labeling limits the scale, accuracy, and timeliness of patient outcomes research in radiation oncology. We present RadOnc-GPT, an autonomous large language model (LLM)-based…
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Jiageng Wu, Bowen Gu, Ren Zhou +14
Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on l…
DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks
Ayush Chopra, Esma Gel, Jayakumar Subramanian +5
We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent…
Prediction and Inference with Missing Data in Patient Alert Systems
Curtis B. Storlie, Terry M. Therneau, Rickey E. Carter +4
We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-ICU patients using ~200 variables (vitals, lab results, assessm…