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.
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