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Explainable structuring and discovery of relevant cases for exploration of high-dimensional data

Data described by numerous features create a challenge for domain experts as it is difficult to manipulate, explore and visualize them. With the increased number of features, a phenomenom called "curse of dimensionality" arises: sparsity increases …

Système de recommandation interactif pour l’analyse de données médicales

A lightning talk about the more technical aspects of the CoSyRES project. An overview of the various packages and technical choices that constitute the working prototype used by medical experts to help them understand their data.

Exploration et système de recommandation pour l’aide au raisonnement médical

Avec la multiplication des systèmes d’information liés à la santé, l’analyse a posteriori des données mé- dicales est un enjeu important. Les informations et les connaissances potentiellement contenues dans ces gigantesques bases de données sont …

Outlier detection in high-dimensional spaces using one-dimensional neighborhoods

Detecting outliers in a dataset is a problem with numerous applica- tions in data analysis for fields such as medical care, finance, and banking or network surveillance. But in a majority of use-cases, data points are described by a lot of features, …

Visual instance-based recommendation system for medical data mining

This paper presents an instance-based algorithm allowing exploration of large medical dataset by making pairwise connection between patients. In our metric-free method, each individual in a dataset ranks every member of the dataset. By aggregating …

Représentativité, généricité et singularité : augmentation de données pour l’exploration de dossiers médicaux

In order to classify individuals according to exemplars that represent them accurately, data visualisation applied to the medical field need to avoid overgeneralization : each case must be treated as a particular instance. This paper presents an …