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Found 12 papers, 4 papers with code
Dataset Debt in Biomedical Language Modeling
Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP.
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
However, current medical VLMs are generally limited to 2D images and short reports, and do not leverage electronic health record (EHR) data for supervision.
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.
A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
With continued pretraining on local data, label efficiency substantially improved, such that $FM_{SM}$ required fewer than 1% of training examples to match the fully trained GBM's performance.
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.
MOTOR: A Time-To-Event Foundation Model For Structured Medical Records
MOTOR is the first foundation model for medical TTE predictions and we release a 143M parameter pretrained model for research use at [redacted URL].
Multi-Resolution Weak Supervision for Sequential Data
Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence.
Weak Supervision for Time Series: Wearable Sensor Classification with Limited Labeled Data
Using modern deep learning models to make predictions on time series data from wearable sensors generally requires large amounts of labeled data.
Snorkel: Rapid Training Data Creation with Weak Supervision
In a user study, subject matter experts build models 2. 8x faster and increase predictive performance an average 45. 5% versus seven hours of hand labeling.
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information
In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data.
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data.
Ranked #2 on
Weakly-Supervised Named Entity Recognition
on BC5CDR
