Search Results for author: Jonas Kemp

Found 6 papers, 2 papers with code

Instability in clinical risk stratification models using deep learning

no code implementations20 Nov 2022 Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne, Adam D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen

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

no code implementations6 Jul 2022 Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Ming-Jun Chen

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

no code implementations20 Sep 2019 Zhen Xu, Andrew M. Dai, Jonas Kemp, Luke Metz

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

1 code implementation6 Sep 2019 Jonas Kemp, Alvin Rajkomar, Andrew M. Dai

Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations.

General Classification Language Modelling

Analyzing the Role of Model Uncertainty for Electronic Health Records

1 code implementation10 Jun 2019 Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai

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.

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