We included the code to obtain the results reported in the study below:

The MedTimeline library aims to provide a standardised process for pre-processing and generating patient journey timelines for the cardiology EHR dataset. Developed by Louise Rigny (Great Ormond Street Hospital for Children)

Used machine learning in R to assess individual variables involved in the intra-operative pathology consultation process.

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

ABP Imputation with EHR medication data is the second version of the ABP Imputer model.

Electronic Health Record Analysis using SQLite

This project aims to make predictions of stroke cases based on simple health data. Supervised machine learning algorithm was used after processing and analyzing the data. The model has predicted Stroke cases with 92.00% of sensitivity.

This is implementation of my Masters thesis work and aims at predicting the patient mortality from the MIMIC-III v1.4 Database

Data challenge from Holmusk to compare the risk of bleeding between two treatments using EHR extracted data.

For fully integrated EHR for the future

Eases access to Caboodle data warehouse from Epic Electronic Health Record

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.

COVID-19 EHR Benchmark - Online Platform

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

Discovering healthcare insights through data-driven analysis of EHRs. This project comprises three subprojects: data cleaning & preparation, exploratory data analysis and lastly, predictive modeling for in-hospital mortality and clinical risk stratification.

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.

The code is used for my capstone which is about the user acceptability of blockchain technology to exchange EHR. For the survey analysis I used PLS SEM

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.

Deep Learning Methods for Chest X-ray Classification