Search Results for author: Michael Wornow

Found 7 papers, 3 papers with code

Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems

no code implementations27 Feb 2024 Alison Callahan, Duncan McElfresh, Juan M. Banda, Gabrielle Bunney, Danton Char, Jonathan Chen, Conor K. Corbin, Debadutta Dash, Norman L. Downing, Sneha S. Jain, Nikesh Kotecha, Jonathan Masterson, Michelle M. Mello, Keith Morse, Srikar Nallan, Abby Pandya, Anurang Revri, Aditya Sharma, Christopher Sharp, Rahul Thapa, Michael Wornow, Alaa Youssef, Michael A. Pfeffer, Nigam H. Shah

Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.

Zero-Shot Clinical Trial Patient Matching with LLMs

no code implementations5 Feb 2024 Michael Wornow, Alejandro Lozano, Dev Dash, Jenelle Jindal, Kenneth W. Mahaffey, Nigam H. Shah

First, we design an LLM-based system which, given a patient's medical history as unstructured clinical text, evaluates whether that patient meets a set of inclusion criteria (also specified as free text).

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

1 code implementation NeurIPS 2023 Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason A. Fries, Nigam H. Shah

The success of foundation models creates new challenges for healthcare ML by requiring access to shared pretrained models to validate performance benefits.

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

2 code implementations NeurIPS 2023 Eric Nguyen, Michael Poli, Marjan Faizi, Armin Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Chris Ré

Leveraging Hyena's new long-range capabilities, we present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level - an up to 500x increase over previous dense attention-based models.

In-Context Learning Language Modelling +1

The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs

1 code implementation22 Mar 2023 Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A. Pfeffer, Jason Fries, Nigam H. Shah

The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations.

Cut out the annotator, keep the cutout: better segmentation with weak supervision

no code implementations ICLR 2021 Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Re

We propose a framework that fuses limited label learning and weak supervision for segmentation tasks, enabling users to train high-performing segmentation CNNs with very few hand-labeled training points.

Data Augmentation Few-Shot Learning +4

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