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INFLUENZANET
DEMOS
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:
the Big Data analytics algorithms are often dependent on the quality and quantity of the data for accurate conclusions
the influenzanet data is not always collected within the same weeks and, in some cases, data is missing
the fact that the data is not collected throughout the year but rather in a time window can disturb the identification of periodicity in the data
it was often noted that the confirmation of at least a part of the questionnaires with biological data (for data annotation) would be of great value
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.
designed to improve the user experience in exploring the Influenzanet dataset through visualization modules composing topic dedicated live monitoring dashboards
the visualization modules based on this dashboard do not require technical skills and enable the data exploration by a diversity of professionals
the system includes a powerful querying engine based on the open-source information retrieval software library Lucene
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
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.
Preliminary experiments compare the incidence of ILI in Portugal to the weather data (rainfall/humidity/temperature) during a full year (from 2.11.2008 to 21.3.2019) to identify newly defined influenza seasons
Other experiments compare seasons between countries or ILI definitions within the incidence in one country
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
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 approach permits us to compare the behavior of ILI seasons by comparing their encoded topology
It also permits us to identify periodicity in the data
We also show the complementary potential of this qualitative method to quantitative methods such as Fourier analysis and dynamical time warping.
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 portal SearchPoint exhibits the clustered keywords of a query on the MEDLINE/PubMed open dataset, after searching for a keyword. This interactive visual tool helps to surface information we are looking for, avoiding the standard answer that is biased by definition. SearchPoint can be used in any language with any document set (public or private) that can be indexed, analyzed and visualized with this approach.
MIDAS SearchPoint Demo
designed to improve the search engine experience; the user provides further information to the search by interacting with the system by dragging a pointer over word clouds
these word clouds are produced by cosine similarity to an "average" centred on the topics in each abstract of the set of selected papers, clustered using the k-means algorithm
it can be used with any document set (public or private) that can be indexed, analyzed and visualized with this approach.
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.
MeSH CLASSIFIER
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.
designed to classify free text of any nature with the classes of MeSH Headings where MEDLINE is based on, to which health professionals are familiar with MIDAS MeSH Classifier Demo
it can classify articles that haven't yet been annotated by the NHS, or official WHO documents of interest
it can also classify news articles and be used to monitor worldwide news based on the classification provided at the MeSH Headings
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.
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