CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks

KDD2020 paper; Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder

COVID-19 EHR data analysis pipeline

attribute-based access control implementation for EHRs

Pipeline of Data Extraction, Preprocessing, Representation, and Training for MIMIC-III

Official implementation of TACCO (Task-guided Co-clustering).

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

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.

This pilot project deploys a supplemental OMOP table to map external measures of rurality (RUCC and RUCA).

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.

Electronic Health Record Analysis using SQLite

Exploratory Data Analysis of synthetic COVID-19 patient data generated by Synthea, focusing on patient demographics, healthcare encounters, and prevalent conditions.

HealthDatum is an electronic health record system that provides easy means of managing clinical data.

EHR data engineering using MIMIC-IV and K-MIMIC

CARE-ML: Predicting the use of restraint on psychiatric inpatients using EHRs and ML. Developed by sarakolding and signekb for their Master's Thesis.

This research uncovers the increased suicide risk in men with mental illness post-hospitalization, analyzing 1.4M+ cases. It highlights the importance of targeted interventions based on identified risk factors.