Search Results for author:
Found 11 papers, 6 papers with code
Categorical EHR Imputation with Generative Adversarial Nets
Recently, researchers have attempted to apply GANs to missing data generation and imputation for EHR data: a major challenge here is the categorical nature of the data.
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data.
Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems
Many common approaches to solve the collaborative filtering task are based on learning representations of users and items, including simple matrix factorization, Gaussian process latent variable models, and neural-network based embeddings.
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process.
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups.
Understanding Individual Decisions of CNNs via Contrastive Backpropagation
The experiments and analysis conclude that the explanations generated by LRP are not class-discriminative.
Tensor-Train Recurrent Neural Networks for Video Classification
The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing.
Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding
We also address the problem of correlation in target features: Often a physician is required to make multiple (sub-)decisions in a block, and that these decisions are mutually dependent.
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events.
Predicting the Co-Evolution of Event and Knowledge Graphs
By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well.
Learning with Memory Embeddings
We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.
