Mining Data and Simulation Group

Description: The group dedicates mainly its activity to the analysis of data in different domains with special emphasis on the medical field in which the group collaborates with professionals from hospitals to analyze data at different levels: organizational, clinic notes, images, brain activity.

Techniques: Data Mining supervised, semisupervised and non-supervised methods, structure and non structured preprocessing methods: image 2D and 3D segmentation, annotation, classification and fusion, text mining, natural language processing (NLP), heuristic optimization techniques, stream mining, visual analytics, statistical analysis, CRISP-DM methodology, SQL and NonSQL data management.

Infrastructure: Technical resources: the group uses its own computers together with the services provided by the CESVIMA.

One of the main focus these days of MIDAS is the analysis of medical information to extract knowledge that can be the basis for precision medicine. This can be decomposed in the following lines:

EHR (electronic health records) analysis and understanding, which involves natural language processing, indexing and knowledge discovery. The integration of the knowledge obtained from the analysis together with other information contained in the EHR can help identifying subjects for clinical trials, finding common patterns of behavior of drugs and treatment, ….

MEG data analysis. Big data analytics is being applied to predict biomarkers for early stages of Alzheimer and Parkinson, as well as other pathologies (for instance those derived from TBI – traumatic brain injury). The analysis is performed based on the Magnetoencephalography records integrated with psychological tests and clinical data.

Medical image processing, analysis and understanding: algorithms and tools to help diagnosis and prognosis, as well as monitoring illnesses progress; image annotation for integrating with other health records.

Neuroinformatics tools and services: Technology and toolkits to provide microscopy image storage, indexing and processing, neuroinformatics databasing and atlasing and laboratory information management systems.

Complex data visualization and interaction: Neuro- and bio-data navigation tools and representation techniques combined with interactive data analysis techniques. Interactive steering of supervised and semi-supervised data exploration and analysis.

Biomedical data retrieval, representation and analysis: Automatic extraction of biomedical knowledge from unstructured sources such as text. Extraction and analysis of medical knowledge from social media. Medical complex networks: human disease networks.