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Found 10 papers, 5 papers with code
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.
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
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.
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.
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].
Instability in clinical risk stratification models using deep learning
While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised.
Ontology-driven weak supervision for clinical entity classification in electronic health records
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.
Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Massively multi-label prediction/classification problems arise in environments like health-care or biology where very precise predictions are useful.
Using Ontologies To Improve Performance In Massively Multi-label Prediction
Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions.
