9 citations · 10 across the 6 of their papers we have counts for
8 papers
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation
Mingyu Lu, Timothy Ossowski, Tristan Naumann +2
Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as text-to-SQL and text-to-Python…
Large Language Models are Approximate Survival Estimators
Juan M Zambrano Chaves, Peniel Argaw, Risa Ueno +5
Survival analysis estimates time-to-event outcomes from patient covariates and is widely used for medical risk assessment. Patients seeking prognostic information after a diagnosis…
HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents
Qianchu Liu, Sheng Zhang, Guanghui Qin +16
As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare appli…
CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
Alyssa Unell, Noel C. F. Codella, Sam Preston +13
The National Comprehensive Cancer Network (NCCN) provides evidence-based guidelines for cancer treatment. Translating complex patient presentations into guideline-compliant treatme…
Machine Learning for Health symposium 2024 -- Findings track
Stefan Hegselmann, Helen Zhou, Elizabeth Healey +6
A collection of the accepted Findings papers that were presented at the 4th Machine Learning for Health symposium (ML4H 2024), which was held on December 15-16, 2024, in Vancouver,…
Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
Matthew B. A. McDermott, Bret Nestor, Peniel Argaw +1
Generative, pre-trained transformers (GPTs, a.k.a. "Foundation Models") have reshaped natural language processing (NLP) through their versatility in diverse downstream tasks. Howev…
Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals
Peniel N. Argaw, Elizabeth Healey, Isaac S. Kohane
Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of hi…
Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous Data
Anna L. Trella, Peniel N. Argaw, Michelle M. Li +1
Within epidemiological modeling, the majority of analyses assume a single epidemic process for generating ground-truth data. However, this assumed data generation process can be un…