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In Studies in health technology and informatics ; h5-index 23.0

The number of scientific publications is constantly growing to make their processing extremely time-consuming. We hypothesized that a user-defined literature tracking may be augmented by machine learning on article summaries. A specific dataset of 671 article abstracts was obtained and nineteen binary classification options using machine learning (ML) techniques on various text representations were proposed in a pilot study. 300 tests with resamples were performed for each classification option. The best classification option demonstrated AUC = 0.78 proving the concept in general and indicating a potential for solution improvement.

Danilov Gleb, Ishankulov Timur, Orlov Yuriy, Shifrin Mikhail, Kotik Konstantin, Potapov Alexander


Text classification, artificial intelligence, machine learning, natural language processing, neurosurgery, topic modeling