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Dicom4j

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The purpose of the dicom4j platform is to provide java components related to the Dicom Standard. For those purpose, the platform is based on 4 areas:

  • framework: framework which implements the standards
  • toolkit: offer ways to easily develop software based on the framework
  • plugins: end-user components which adress commons needs you can find in most dicom applications
  • apps: stand alone applications for end-user or tests purpose

CONNECT

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CONNECT is an open source software solution that supports health information exchange – both locally and at the national level. CONNECT uses Nationwide Health Information Network (NHIN) standards and governance to make sure that health information exchanges are compatible with other exchanges being set up throughout the country.

OpenEMed

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OpenEMed is a set of distributed healthcare information service components built around the OMG distributed object specifications and the HL7 (and other) data standards and is written in Java for platform portability. We emphasize the interoperable service functionality that this approach provides in reducing the time it takes to build a healthcare related system. It is not intended as a turnkey system but rather a set of components that can be assembled and configured to meet a variety of tasks.

WEKA

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Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

CASE

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The main goal of the Computer Assisted Search for Epidemics (CASE) project is to develop a reliable system that generates warnings when the number of reported cases of a particular infectious disease reaches a level that indicates an unusual or unexpected rate. The system is currently in use at the Swedish Institute for Infectious Disease Control (SMI). It performs daily surveillance using data obtained from the database to which all notifiable diseases are reported in Sweden.

ADDIS

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ADDIS is a software developed within the Dutch Escher-project for managing and analyzing clinical trial information.

ADDIS is a proof-of-concept system that allows us to simultaneously discover the possibilities of and the requirements on a database of structured clinical trials data. The automated discovery and (meta-)analysis of trial data, as well as benefit-risk assessment is supported.

ADDIS comes with two built-in examples:

JCAM Engine

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XML Validation Framework and Canonical XML dictionary-based exchange assembly - OASIS CAM

The OASIS CAM toolkit provides a suite of tools for XML Validation and XML Exchange design and assembly from canonical XML dictionary components.

Included in the toolkit is an XML Editor/Validation/Schema Designer along with the CAMV runtime XML validation engine.

The project implements the OASIS CAM standard & NIEM IEPD approach. The visual editor allows design of exchange structures using XML components dictionaries.

Open Health Tools Project Implementation of IHE Profiles

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To lower the barrier to integration and expedite health care interoperability, Open Health Tools provides client side implementations of several key IHE profiles. These implementations were used successfully by over 35 systems to date at the 2007-2010 Connectathons in North America and Europe.

Net4Care

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The research project Net4Care's aim is to develop a ecosystem to make it easy for small and medium sized businesses (SMBs) to build telemedical applications for the home.

The main area of support within the present edition is handling clinical observations in the home and ensuring they become available for clinician's work.

The Net4Care framework helps in this by providing:

OpenHRE

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OpenHRE is the world's first open source toolkit that provides a standard and secure means to exchange data between existing health records systems. With OpenHRE, health professionals can locate, access, and review clinical data related to a specific health consumer, or acquire de-identified clinical data for population studies.

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