INFLUENZANET Influenzanet is a system to monitor the activity of influenza-like-illnesses with the aid of internet volunteers. Influenzanet obtains its data directly from the population, contrasting with the traditional system of sentinel networks of mainly primary care physicians. Influenzanet was shown to be a fast and flexible monitoring system whose uniformity allows for direct comparison of ILI rates between countries. This type of data brings specific challenges as the following:
INFLUENZANET DASHBOARD The Influenzanet dashboard permits the user of the Influenzanet coordinating institution to profit of data visualization modules that feed on his/her datasets collecting the ILI season data, built in ElasticSearch. This tool enables one to query the dataset and produce different types of data visualization modules that can later integrate a customized dashboard. This dashboard can be used in any language with any document set (public or private) that can be indexed, analyzed and visualized with this approach.
This system was developed by the AI Lab at the IJS and refocused by Quintelligence within the MIDAS project to visually analyze the MEDLINE dataset. It can be implemented in premises to work with proprietary data. It is currently available as Open Source under the BSD license. StreamStory is a multi-scale data analysis tool for multivariate continuously time-varying data streams. It represents the data streams in a qualitative manner using states and transitions. Users can upload their own dataset or use one of the pre-loaded datasets. StreamStory can also be used as a monitoring tool, showing in real-time the state of the monitored process, activity and anomaly detection.
This system is under research, build in-house in the context of the PhD of our colleague Luka Stopar at the Institute Jozef Stefan in Ljubljana, research partner of Quintelligence. It is currently available as Open Source under the BSD license. Topological Data Analysis applies qualitative methods of topology inferring high-dimensional structure from low-dimensional representations and studying properties of a continuous space by the analysis of a discrete sample of it. The basic technique encodes topological features of a given point cloud by diagrams representing the lifetime of those topological features.
This analysis is done over a series of freely available software tools that implement the state-of-the-art algorithms to compute persistent homology [Ripser and Perseus], and to calculate persistent landscapes [persistent landscapes toolbox] (including the bottleneck distance between two persistence diagrams). SEARCHPOINT
The core system was developed by the AI Lab at the IJS and refocused by Quintelligence within the MIDAS project to analyze the MEDLINE dataset. It can be implemented in premises to work with proprietary data. It is currently available as Open Source under the BSD license. The MeSH Classifier is a tool developed by Quintelligence to classify free text with the latest MeSH Headings provided by NHS. It is based on the DMOZ classifier, learning over 80+ years of MEDLINE data, and over the MeSH tree with 16 major categories and a max of 13 levels of deepness. It provides all the classifying categories with position number and (cosine) similarity weight, with a slider and a number of max categories visible. It available through a web app and an API.
The core system was developed by the AI Lab at the IJS and refocused by Quintelligence within the MIDAS project to use the MeSH Headings to classify free text. It can be implemented in premises to work with proprietary data. It is currently available as Open Source under the BSD license. ![]() |