Stars
Documents supporting OMOP + FHIR Terminologies Subgroup activities
Yonsei University Professional Research Personnel Wiki
This is an engine that converts data of one structure to another, based on a configuration file which describes how. There is an accompanying syntax to make writing mappings easier and more robust.
Data Ingestion and Harmonization
Contains the Java and R assets to perform Incidence calculations on a CDM
Keras implementations of Generative Adversarial Networks.
An R package for performing patient level prediction using deep learning in an observational database in the OMOP Common Data Model.
This is tool to convert ECG raw data and diagnostic from XML to CSV format
PRML study (pattern recognition & machine learning, Bishop)
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
Utilities to perform Uncertainty Quantification on Keras Models
High-quality implementations of standard and SOTA methods on a variety of tasks.
Google Research
Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
An R package for performing patient level prediction in an observational database in the OMOP Common Data Model.
Repository for our ICCV 2019 paper: Adversarial Defense via Learning to Generate Diverse Attacks
Code for paper "A deep learning framework for drug repurposing via emulating clinical trials on real world patient data" (Accepted to Nature Machine Intelligence).
Scripts and modules for training and testing neural network for ECG automatic classification. Companion code to the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network".
ATLAS is an open source software tool for researchers to conduct scientific analyses on standardized observational data
RmecabKo: R wrapper for eunjeon project (mecab-ko)
A fully Dockerized, self-hosted development environment for teams. Develop where you serve.
[OBSOLETE] Please use https://github.com/imagej/ImageJ instead.
Code for "Characterization of Overlap in Observational Studies" (AISTATS 2020)
Methods for heterogeneous treatment effect estimation