This project provides a simple but flexible Perl Toolkit for using the HL7 protocol. The toolkit consists of a Perl API, an implementation of a pluggable forking HL7 server, and an HL7 message queue daemon for developing HL7 capable applications in Perl.
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Occupancy in certain hospital patient care units is impacted by procedure scheduling policies and practices. For example, intensive care unit occupancy is strongly related to open heart surgery schedules. Similarly, occupancy in obstetrical postpartum units is impacted by the daily number of scheduled labor inductions and cesarean sections. That was the motivation for this project.
OBsched is a set of optimization models and supporting software for exploring the relationship between patient scheduling and nursing unit occupancy in hospitals.
HL7 file viewer, in tree-view format, with associated segment/field documentation. The latest release now includes editing, at all levels in the tree-view, e.g segment, field or component values. Purpose is for testing and bug-tracing HL7 communications.
The Medical Imaging Interaction Toolkit (MITK) is a free open-source software system for development of interactive medical image processing software. MITK combines the Insight Toolkit (ITK) and the Visualization Toolkit (VTK) with an application framework. As a toolkit, MITK offers those features that are relevant for the development of interactive medical imaging software covered neither by ITK nor VTK.
Core features of the MITK platform:
Snofyre is an open source, service oriented API for creating SNOMED CT enabled applications in Java. It provides a number of SNOMED CT related services out of the box. These services can be used:
- as a starter for understanding how to add SNOMED CT functionality to an application.
- to rapidly prototype a SNOMED CT enabled application.
Snofyre API aims to
- reduce the 'ramp up' time needed to understand
- and embed SNOMED CT functionality in an application.
Ruby HL7 is a simple library for parsing and generating HL7 2.x messages. 3.x support is planned in the future.
The MedicalExplorationToolkit (METK) was designed for loading, visualizing and exploring segmented medical data sets. It is a framework of several modules in MeVisLab, a development environment for medical image processing and visualization.
- Case Management: Load and save whole cases of segmented structures e.g. for surgery planning, educational training or intra operative visualization.
- Basic Visualization in 2D and 3D: Visualize segmented structures in multiple manner e.g. iso surface rendering, stippling, hatching, silhouettes, volume rendering, 2d overlays.
A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.
The National Cancer Institute (NCI) has launched the caBIG initiative to accelerate research discoveries and improve patient outcomes by linking researchers, physicians, and patients throughout the cancer community.
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.