EGADSS (Evidence-based Guideline and Decision Support System) is an open source tool that is designed to work in conjunction with primary care Electronic Medical Record (EMR) systems to provide patient specific point of care reminders in order to aid physicians provide high quality care. EGADSS is designed as a stand alone system that would respond to requests from existing Electronic Medical Records such as Wolf, Med Access, and MedOffIS to provide patient specific clinical guidance based on its internal collection of guidelines.
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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.
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.
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.
Brainstorm is a collaborative open-source Matlab application dedicated to magnetoencephalography (MEG) and electroencephalography(EEG) data visualization, processing and cortical source estimation.
The intention is to make a comprehensive set of tools available to the scientific community involved in MEG/EEG experimental research.
For physicians and researchers, the interest of this software package resides in its rich and intuitive graphic interface, which does not require any programming knowledge.
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:
MassChroQ (Mass Chromatogram Quantification) software performs quantification of data obtained from mass-spectrometry techniques. It is particularly well suited for peptide quantification of LC-MS (Liquid Chromatography - Mass Spectrometry) data. It performs chromatographic alignment, XIC extraction, peak detection and quantification on identified peptides, with or without isotopic labeling, on high or low resolution data and it takes into account peptide or protein fractionation.
The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing, and visualization. It consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python. VTK supports a wide variety of visualization algorithms including scalar, vector, tensor, texture, and volumetric methods, as well as advanced modeling techniques such as implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation.
The Spatiotemporal Epidemiological Modeler (STEM) tool is designed to help scientists and public health officials create and use spatial and temporal models of emerging infectious diseases. These models can aid in understanding and potentially preventing the spread of such diseases.
i2b2 (Informatics for Integrating Biology and the Bedside) is an NIH-funded National Center for Biomedical Computing based at Partners HealthCare System. The i2b2 Center is developing a scalable informatics framework that will enable clinical researchers to use existing clinical data for discovery research and, when combined with IRB-approved genomic data, facilitate the design of targeted therapies for individual patients with diseases having genetic origins. This platform currently enjoys wide international adoption by the CTSA network, academic health centers, and industry.