Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.
We propose two variants, SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively.
Access to electronic health record (EHR) data has motivated computational advances in medical research.
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable.
-Interpretation:The representations learned by deep learning methods should align with medical knowledge.
RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.
Ranked #2 on Disease Trajectory Forecasting on UK CF trust
Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data.
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.