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.
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Updated
Sep 3, 2020 - Python
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.
Collection of bio-medical and clinical ner models in spacy, stanza, flair with some utility files
KDD2020 paper; Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder
attribute-based access control implementation for EHRs
CARE-ML: Predicting the use of restraint on psychiatric inpatients using EHRs and ML. Developed by sarakolding and signekb for their Master's Thesis.
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
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.
HealthDatum is an electronic health record system that provides easy means of managing clinical data.
COVID-19 EHR data analysis pipeline
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.
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.
BERT style transformer model on CMS synthetic EHR data for diagnosis and procedure prediction in PyTorch.
Official implementation of TACCO (Task-guided Co-clustering).
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