Search Results for author:
Found 11 papers, 8 papers with code
Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs
We find that longer context models improve predictive performance -- our Mamba-based model surpasses the prior state-of-the-art on 9/14 tasks on the EHRSHOT prediction benchmark.
meds_reader: A fast and efficient EHR processing library
The growing demand for machine learning in healthcare requires processing increasingly large electronic health record (EHR) datasets, but existing pipelines are not computationally efficient or scalable.
WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks
To address this gap we present WONDERBREAD, the first benchmark for evaluating multimodal FMs on BPM tasks beyond automation.
Automating the Enterprise with Foundation Models
We conduct initial experiments showing that multimodal FMs can address the limitations of traditional RPA with (1) near-human-level understanding of workflows (93% accuracy on a workflow understanding task) and (2) instant set-up with minimal technical barrier (based solely on a natural language description of a workflow, ECLAIR achieves end-to-end completion rates of 40%).
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
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
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
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
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.
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs
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
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.
