Prediction of hospital stay duration in heart failure patients using machine learning.

Mortality prediction using MIMIC-III dataset

Experiments applying FIDDLE on MIMIC-III and eICU. https://doi.org/10.1093/jamia/ocaa139

Hierarchical Graph Pooling with Structure Learning

GAMENet : Graph Augmented MEmory Networks for Recommending Medication Combination

深度学习100例、深度学习DL、图片分类、目标识别、目标检测、自然语言处理nlp、文本分类、TensorFlow、PyTorch

A Multimodal Transformer: Fusing Clinical Notes With Structured EHR Data for Interpretable In-Hospital Mortality Prediction

Python suite to construct benchmark machine learning datasets from the MIMIC-III 💊 clinical database.

PyTorch library for MIMIC III Benchmark experiments

This is the final project of the data science course based on MIMIC III data

Implementation of a multi-layer perceptron (MLP) for heart failure prediction based on the MIMIC-III dataset.

🧪Yet Another ICU Benchmark: a holistic framework for the standardization of clinical prediction model experiments. Provide custom datasets, cohorts, prediction tasks, endpoints, preprocessing, and …

MIMIC Code Repository: Code shared by the research community for the MIMIC family of databases

This project focuses on analyzing and predicting Gene-Disease Associations (GDA) using graph-based machine learning techniques. It leverages curated datasets, protein-protein interaction (PPI) data…

An Industrial Graph Neural Network Framework

CodaLab Competitions

Data for "Understanding Isomorphism Bias in Graph Data Sets" paper.

This repository provides an open source implementation of the Spatio-Temporal GAT introduced by Zhang et al in "Spatial-Temporal Graph Attention Networks:A Deep Learning Approach for Traffic Foreca…

Pytorch Geometric Tutorials

This is the repository for the collection of Graph Neural Network for Traffic Forecasting.

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

A PyTorch implementation of T-GCN

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Graph Neural Network Library for PyTorch

Must-read papers on graph neural networks (GNN)