Text-mining & Machine Learning to predict tomorrow’s Medical breakthroughs

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The rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora, and of tracking complex temporal changes within it.

Open Access version in St Andrews Research Repository

16-20 Aug. 2016
Identification of promising research directions using machine learning aided medical literature analysis / Victor Andrei ; Ognjen Arandjelovic.
Published in: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference
DOI: 10.1109/EMBC.2016.7591231

 

New Academic Chair on “Industrial Data Analytics & Machine Learning”

The ENS Paris-Saclay, Atos and the CEA (French commission for alternate energies including atomic) signed an agreement on October 20th, 2016.

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The chair is a cooperative program oriented towards industrial applications of artificial intelligence, setting the foundations for a unique ecosystem in France.

It includes training at the master and PhD level (MVA : Mathematics, Vision, Learning), joint research projects bringing together field experts and academic researchers, and support for the development of spin-offs.

cp-cea-atos-enseng-pdf