OpenMRS API and web application code

Monorepo that holds all of HospitalRun's v2 projects.

Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.

Code and website related to the eICU Collaborative Research Database

Open platform to manage and share standardized clinical data, designed by @ppazos at CaboLabs Health Informatics.

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://opensource.org/licenses/Apache-2.0.

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

Free and open source Electronic Health Record

Medkey Hospital Information System main repository: Practice Management for Practicioners & Hospitals, EHR, Patient Engagement

PDD Graph : Bridging MIMIC-III and Linked Data Cloud

Natural language generation for discrete data in EHRs

Redesigning the Patient Portal Experience with SMART on FHIR.

OpenEHR library implementing ADL 2, AOM 2 and RM 1.0.4

openEHR REST API Specifications

Library and CLI for randomly generating medical data like you might get out of an Electronic Health Records (EHR) system

APIs para ANDES

Java library to map LOINC-encoded test results to Human Phenotype Ontology

A curated list of awesome Digital Global Health resources and software.

Point of care system for AMPATH clinics

ANDES: Aplicaciones Neuquinas de Salud

Development build for SMART Cancer Navigator

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

Mapping the MIMIC-III database to the FHIR resources

Iryo's pilot project for refugee camps

NER and Relation Extraction from EHR records.

Convert arbitrary EHR extracts to FHIR.

Medkey tool for module/plugin installation

Phenotyping algorithms for common biomarkers in primary care EHR for UK Biobank