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Posts from November 2020

Finding the missing pieces of the puzzle

I think one of the most-used phrases of 2020 is “these are unprecedented times.”

I did hear another phrase recently that I liked: it is a “dark privilege” to be living through this global pandemic. No doubt, we live in an interesting time! In decades to come, people will talk about 2020 and the impact of coronavirus on so many global factors – economy, climate, travel, and of course population and individual health. As well as the dark clouds, there is of course light – and much light comes from the collaborative efforts of institutions, healthcare organizations and governments to find the best responses to this challenge, particularly in the areas of drug repurposing and vaccine development.

And this, to me, is where natural language processing-based text mining comes in. Whenever scientists, researchers and clinicians are faced with a challenge, one critical asset is identifying as much information about the problem as possible. Whatever information there is, locally and globally, that can be found, gathered and understood, will enable the right decisions to be taken. Relevant information on the biology of SARS-CoV2, from COVID-19 as a disease, patient demographics and co-morbidities, global or regional spread, and possible drugs that might treat the symptoms and impacts of this disease is all just the tip of a data iceberg. Much of this data exists in unstructured text: scientific papers, preprints, clinical trial records, adverse event reports, electronic healthcare records, even news feeds and social media can all provide information on epidemiological factors, for example.