Medplum is a healthcare platform that helps you quickly develop high-quality compliant applications.
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Updated
Jun 7, 2024 - TypeScript
Medplum is a healthcare platform that helps you quickly develop high-quality compliant applications.
Fasten is an open-source, self-hosted, personal/family electronic medical record aggregator, designed to integrate with 100,000's of insurances/hospitals/clinics
Basic Patient resource browser to provide visibility into your FHIR servers.
Toolkit for evaluating and monitoring AI models in clinical settings
A toolkit for developing foundation models using Electronic Health Record (EHR) data.
Electronic Health Record Analysis with Python.
Research Data Management Platform (RDMP) is an open source application for the loading,linking,anonymisation and extraction of datasets stored in relational databases.
Measurement based care infrastructure for absolutely everyone
Modular, Production-Ready, Open-Source EHR
Graph representation learning with GNNs for predicting disease risk from family EHRs
A Deep Learning Python Toolkit for Healthcare Applications.
The project uses blockchain and smart contracts to let individuals manage and secure their health data. Its goal is to empower people to control their health information, communicate better with healthcare providers, and drive innovation in healthcare.
PARALLEL AND DISTRIBUTED ELECTRONIC HEALTH RECORD MANAGEMENT SYSTEM
Revolutionize healthcare data management with our EHR system built on Next.js, Prisma, MongoDB, and NextAuth. Experience seamless navigation and robust security through role-based authentication, ensuring precise access controls for healthcare professionals. Elevate efficiency and reliability in patient care with our state-of-the-art solution
OpenEMR is the most popular open source electronic health records and medical practice management solution. ONC certified with international usage, OpenEMR's goal is a superior alternative to its proprietary counterparts.
Prediction of hospital stay duration in heart failure patients using machine learning.
Machine-readable version of electronic health record phenotypes for Kuan V. and Denaxas S. et al.
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.
Sarah's GitHub Bio
💗 npm package for accessing UMLS REST APIs (unofficial)
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