3 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.AI2023★ 1 cited
Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer
Narmada Naik, Ayush Khandelwal, Mohit Joshi +9
Causal discovery is becoming a key part in medical AI research. These methods can enhance healthcare by identifying causal links between biomarkers, demographics, treatments and ou…
cs.LG2023
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
Javier González, Risa Ueno, Cliff Wong +12
The rapid digitization of real-world data presents an unprecedented opportunity to optimize healthcare delivery and accelerate biomedical discovery. However, these data are often f…
cs.CL2022★ 3 cited
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Sam Preston, Mu Wei, Rajesh Rao +11
Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and ti…