Forecasting Response to Treatment with Global Deep Learning and Patient-Specific Pharmacokinetic Priors

no code implementations • 22 Sep 2023 • Willa Potosnak, Cristian Challu, Kin G. Olivares, Artur Dubrawski

Our global-local architecture improves over patient-specific models by 9. 2-14. 6%.

Time Series

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

3 code implementations • 15 Apr 2022 • Chirag Nagpal, Willa Potosnak, Artur Dubrawski

Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death.

BIG-bench Machine Learning counterfactual +1

The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks

2 code implementations • 11 Apr 2021 • Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker, Ernest Pokropek, Willa Potosnak, Suyash Bire, Salomey Osei, Björn Lütjens

In comparison, graph neural networks (GNNs) are capable of modeling large-scale spatial dependencies and are more interpretable due to the explicit modeling of information flow through edge connections.

Graph Neural Networks for Improved El Niño Forecasting

1 code implementation • 2 Dec 2020 • Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker, Ernest Pokropek, Willa Potosnak, Salomey Osei, Björn Lütjens

Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO).

Multivariate Time Series Forecasting Spatio-Temporal Forecasting