Search Results for author:
Found 7 papers, 2 papers with code
Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
We propose to meta-learn an a self-supervised patient trajectory forecast learning rule by meta-training on a meta-objective that directly optimizes the utility of the patient representation over the subsequent clinical outcome prediction.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #4 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
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.
Boosting the interpretability of clinical risk scores with intervention predictions
We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions.
BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark
Most ML approaches focus on generalization performance on unseen data that are similar to the training data (In-Distribution, or IND).
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
The paradigm of pretraining' from a set of relevant auxiliary tasks and thenfinetuning' on a target task has been successfully applied in many different domains.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
