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

attribute-based access control implementation for EHRs

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

COVID-19 EHR data analysis pipeline

Collection of bio-medical and clinical ner models in spacy, stanza, flair with some utility files

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

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.

Electronic Health Record Analysis using SQLite

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

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.

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

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

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.

Tool for EHR & mutation profile based patient clustering & visualization, developed in partial fulfillment of the requirements for the course “Medical Informatics” at the University Medical Center Göttingen.

BERT model on CMS synthetic EHR data for diagnosis and procedure prediction in PyTorch