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Found 12 papers, 6 papers with code
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories.
Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series
Here, we introduce TADA, a Two-stageAggregation process with Dynamic local Attention to harmonize time-wise and feature-wise irregularities in multivariate time series.
Attention-based Multi-task Learning for Base Editor Outcome Prediction
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
Attention-based Multi-task Learning for Base Editor Outcome Prediction
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.
SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification.
Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
Methods: We present an extensive evaluation of the impact of different federation and differential privacy techniques when training models on the open-source MIMIC-III dataset.
DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm
In contrast to conventional models relying on pre-computed chemical features, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs, providing superior performance in predicting drug synergies.
AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions
Background: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves.
Patient Similarity Analysis with Longitudinal Health Data
Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records.
AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks
Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional.
Neural networks versus Logistic regression for 30 days all-cause readmission prediction
Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0. 642 AUC (95% CI, 0. 640-0. 645).
