Fasten is an open-source, self-hosted, personal/family electronic medical record aggregator, designed to integrate with 100,000's of insurances/hospitals/clinics
Medplum is a healthcare platform that helps you quickly develop high-quality compliant applications.
A Deep Learning Python Toolkit for Healthcare Applications.
Electronic Health Record Analysis with Python.
The code repository for the prototypes included in the eBook "Inspired EHRs - Designing for Clinicians" (inspiredEHRs.gov). The code of the prototypes is made available under the Apache 2.0 open source license. This license agreement allows anyone to freely use the code and ideas presented in this book, subject to the conditions listed at http:/…
Modular, Production-Ready EHR
Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research
The word2vec-BiLSTM-CRF model for CCKS2019 Chinese clinical named entity recognition.
Toolkit for health AI implementation
Code for the old version of BioMedICUS, for the new version see the biomedicus3 repository.
Machine-readable version of electronic health record phenotypes for Kuan V. and Denaxas S. et al.
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
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.
Code and Datasets for the paper "Combining structured and unstructured data for predictive models: a deep learning approach", published on BMC Medical Informatics and Decision Making in 2020.
Fall Detection in EHR using Word Embeddings and Deep Learning
💗 npm package for accessing UMLS REST APIs (unofficial)
A public repo to share methylation risk scores (MRS) derived from UCLA's electronic health records.
AI for Medicine Course by DeepLearning.ai
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