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Found 5 papers, 2 papers with code
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.
Learning an Adaptive Learning Rate Schedule
The learning rate is one of the most important hyper-parameters for model training and generalization.
Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes
Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations.
Analyzing the Role of Model Uncertainty for Electronic Health Records
We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.
