430 citations · 717 across the 7 of their papers we have counts for
7 papers
Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
Yuan Xue, Nan Du, Anne Mottram +2
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 repr…
Large Language Models Encode Clinical Knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu +27
Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications i…
Instability in clinical risk stratification models using deep learning
Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne +9
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. We study th…
Boosting the interpretability of clinical risk scores with intervention predictions
Eric Loreaux, Ke Yu, Jonas Kemp +8
Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future inte…
BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark
Anand Avati, Martin Seneviratne, Emily Xue +3
Machine learning has recently demonstrated impressive progress in predictive accuracy across a wide array of tasks. Most ML approaches focus on generalization performance on unseen…
Concept-based model explanations for Electronic Health Records
Diana Mincu, Eric Loreaux, Shaobo Hou +7
Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse outcomes in electronic health records (EHRs) due to their ability to encode past clinical states.…