WellAI data scientists Daniel Satchkov and Sergei Polevikov will present their most recent research entitled "Reading 25 Million Studies in Seconds: Implications for Fighting COVID-19 and Managing a Portfolio" at a free webinar on August 25, 2020. The webinar will take place from 12pm to 1pm EST, and is jointly organized by the Society of Quantitative Analysts (SQA) and WellAI. Discussion will be partly based on a study "Artificial Intelligence-powered search tools and resources in the fight against COVID-19" published in the Journal of the International Federation of Clinical Chemistry and Laboratory Medicine in June 2020, and is currently available through the PubMed database of the National Institutes of Health (NIH). Sergei Polevikov, CEO of WellAI and a board director at SQA, explained: "We wanted to share our unique experience as we believe our work is relevant to both medical researchers and finance professionals. WellAI data scientists had built a free COVID-19 analytical tool for medical researchers around the world in early April 2020, to help fight the pandemic. As some of us had also had previous experience as data scientists in the finance industry, we found some interesting similarities and differences in a way one applies machine learning algorithms in healthcare versus applying those in finance. What better place to share this knowledge than the SQA – one of the most recognized organization in the United States among the quantitative investment professionals?"

Hospitals in Paris are trialling Big Data and machine learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes. The result was the first contribution to an open source framework of code designed to carry out the analysis over a scalable, distributed framework. Machine learning is employed to determine which algorithms provide the best indicator of future trends, when they are fed data from the past. The core of the analytics work involves using time series analysis techniques – looking for ways in which patterns in the data can be used to predict the admission rates at different times. This code is already being put to use in several other projects involving healthcare and finance.