Rahul Thapa — accepted AI-conference papers

2024

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo Reis, Rahul Thapa +24

AAAI 2024technical

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains chall…

Cited by 67 Source ↗PDF ↗Scholar ↗

2024

SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals

Rahul Thapa, Bryan He, Magnus Ruud Kjaer, Hyatt Moore IV, Gauri Ganjoo, Emmanuel Mignot +1

ICML 2024poster

Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We curate a large polysomnography dataset from over 14,000 participants comprising over 100,000 hours of multi-modal sleep recordings. Leveraging this extens…

Cited by 11 Source ↗PDF ↗code ↗Scholar ↗

2023

EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason Alan Fries, Nigam Shah

NeurIPS 2023spotlight

While the general machine learning (ML) community has benefited from public datasets, tasks, and models, the progress of ML in healthcare has been hampered by a lack of such shared assets. The success of foundation models creates new challenges for healthcare ML by requiring access to shared pretrai…

Cited by 36 Source ↗PDF ↗code ↗Scholar ↗