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Found 4 papers, 1 papers with code
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series
In this work, we use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model with high predictive power to train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction.
CAFE: Conflict-Aware Feature-wise Explanations
Feature attribution methods are widely used to explain neural models by determining the influence of individual input features on the models' outputs.
RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction
We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time.
Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes
Models developed using our framework benefit from the full range of RSPN capabilities, including the abilities to model the full distribution of the data, to seamlessly handle latent variables, missing values and categorical data, and to efficiently perform marginal and conditional inference.
