Impulsionando a descoberta de tratamentos na medicina através da representação distribuída de palavras

Word embeddings are mathematical and computational representations that consist of high dimensional vectors capable of encoding the meaning of terms or sentences in a text. This well-established approach enhanced many Natural Language Processing applications, since it can be easily generated from large textual datasets by a different set of algorithms. In this study, we have extended a recently discovered use of word embeddings: the ability to uncover potential implicit information in a corpus (also known as latent knowledge) that may not be achievable with human analysis alone. More specifically, our work combines word embeddings computed through diverse unsupervised methods in order to extract latent knowledge that could anticipate clinical discoveries in the field of medicine. By using a massive amount of scientific papers related to a high deadly cancer called Acute Myeloid Leukemia, our study shows that currently approved therapies could have been investigated earlier due to drug testing notifications issued by our framework. Therefore, our strategy collaborates to a faster drug analysis and biomedical discoveries. Details about our proposal and in-depth analysis of the results can be found in Berto et al. [2].