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Found 17 papers, 4 papers with code
Triple Simplex Matrix Completion for Expense Forecasting
Forecasting project expenses is a crucial step for businesses to avoid budget overruns and project failures.
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection
This paper focuses on the trial site selection task and proposes FRAMM, a deep reinforcement learning framework for fair trial site selection.
DeCom: Deep Coupled-Factorization Machine for Post COVID-19 Respiratory Syncytial Virus Prediction with Nonpharmaceutical Interventions Awareness
Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children.
Clinical trial site matching with improved diversity using fair policy learning
More recently, the issue of diversity and inclusion in clinical trials is gaining importance.
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion
Tensor completion aims at imputing missing entries from a partially observed tensor.
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem.
SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations
On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19. 43% and improves 2. 88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.
Change Matters: Medication Change Prediction with Recurrent Residual Networks
Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions.
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting.
PyHealth: A Python Library for Health Predictive Models
PyHealth consists of data preprocessing module, predictive modeling module, and evaluation module.
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during both training and inference.
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions.
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Ranked #2 on
Drug Discovery
on KIBA
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare.
Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis.
