CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
COVID-19 EHR data analysis pipeline
KDD2020 paper; Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder
attribute-based access control implementation for EHRs
Official implementation of TACCO (Task-guided Co-clustering).
Controllable Sequence Editing for Counterfactual Generation
LLM graph-RAG SQL generator for large databases with poor documentation
Official implementation of "FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records" (MLHC 2024)
Collection of bio-medical and clinical ner models in spacy, stanza, flair with some utility files
Code for "Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer"
JAMIA: A Novel Generative Multi-Task Representation Learning Approach for Predicting Postoperative Complications in Cardiac Surgery Patients
This repository hosts a cutting-edge deep learning model developed to predict 6-month incident heart failure utilizing electronic health records (EHRs). Heart failure is a multifaceted medical condition characterized by its significant impact on patients' well-being and healthcare systems.
The SynMed EHR Generator is a tool for creating synthetic Electronic Health Records (EHRs) represented as Knowledge Graphs in RDF and FHIR formats.
BERT style transformer model on CMS synthetic EHR data for diagnosis and procedure prediction in PyTorch
In this project, we will create a deep learning model trained on EHR data (Electronic Health Records) to find suitable patients for testing a new diabetes drug.
HealthDatum is an electronic health record system that provides easy means of managing clinical data.
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