How Machine Learning and AI Can Support the Fight Against COVID 19

In this session, we show how to leverage CORD dataset, containing more than 400000 scientific papers on COVID and related topics, and recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. The idea explored in our talk is to apply modern NLP methods, such and named entity recognition (NER) and relation extraction to articles abstracts (and, possibly, full text), to extract some meaningful insights from the text, and to enable semantically rich search over the paper corpus. We first investigate how to train NER model using Medical NER dataset from Kaggle, and specialized version of BERT (PubMedBERT) as a feature extractor, to allow automatic extraction of such entities as medical condition names, medicine names and pathogens. Entity extraction alone can provide us with some interesting findings, such as how approaches to COVID treatment evolved with time, in terms of mentioned medicines. We demonstrate h