A Deep Learning Python Toolkit for Healthcare Applications.

Electronic Health Record Analysis with Python.

A toolkit for evaluating and monitoring AI models in clinical settings

The word2vec-BiLSTM-CRF model for CCKS2019 Chinese clinical named entity recognition.

A toolkit for developing foundation models using Electronic Health Record (EHR) data.

[ACL 2024] This is the code for our paper ”RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records“.

Code and Datasets for the paper "Combining structured and unstructured data for predictive models: a deep learning approach", published on BMC Medical Informatics and Decision Making in 2020.

Convert arbitrary EHR extracts to FHIR.

Graph representation learning with GNNs for predicting disease risk from family EHRs

Flask API for DDC Prescription Score

Code and Datasets for the paper "An Interpretable Risk Prediction Model for Healthcare with Pattern Attention", published on BMC Medical Informatics and Decision Making.

Code for the paper "Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases". Accepted by BMC Medical Informatics and Decision Making, 2021

Code and Datasets for the paper "Estimating Individual Treatment Effects with Time-Varying Confounders", published on ICDM 2020.

Code and Datasets for the paper "DG-Viz: Deep Visual Analytics with Domain Knowledge Guided Recurrent Neural Networks on Electronic Health Records", published on Journal of Medical Internet Research (JMIR) in 2020.

Clinical Named Entity Recognition for EHR

COPRA: Constrained Prominence Adversarial Attack and Defense on Sparse and Discrete Clinical Data

Natural language generation for discrete data in EHRs

Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection. https://doi.org/10.1093/ofid/ofz186

Convert arbitrary EHR extracts to FHIR.