April 07, 2021
It’s Technology Assisted Review (TAR) day! And there may never have been a more important use for TAR than this one!
As reported by the University of Waterloo (Using technology-assisted review to find effective treatments and procedures to mitigate COVID-19), researchers and clinicians have rushed to understand the available treatments and procedures to mitigate COVID-19 since the pandemic began. As you can imagine, the sheer volume of studies published on COVID-19 — in countries spanning the globe — as well as lessons learned from prior epidemics and pandemics, simply cannot be gathered and assessed quickly enough using traditional manual methods during this time of crisis.
However, a transformation in interdisciplinary collaboration, as well as in the process for completing systematic reviews for evidence-based medicine, has made it so that reviews that used to take researchers many months to complete can now accomplished in a matter of days or weeks.
To guide decisions by healthcare providers on the frontlines of the healthcare crisis, Cheriton School of Computer Science Professors Maura R. Grossman and Gordon V. Cormack have been working with the knowledge synthesis team at St. Michael’s Hospital in Toronto, on behalf of the Canadian Frailty Network and Health Canada, to automate these literature searches. The goal of their efforts is to help the team to quickly identify clinical studies that have evaluated the effectiveness and safety of various measures to keep nursing care facilities safe, as well as treatments for patients with COVID-19. The results of these systematic reviews are then used to support treatment and policy decisions. Importantly, new evidence must be interpreted in light of the lessons learned from studies conducted during previous epidemics and pandemics, such as Severe Acute Respiratory Syndrome (SARS), which caused a disease outbreak beginning in 2002, and Middle East Respiratory Syndrome (MERS), which caused a disease outbreak beginning in 2012.
Using the Continuous Active Learning (CAL) TAR approach supervised machine learning approach that Professors Grossman and Cormack originally developed to expedite the review of documents in high-stakes legal cases — they have now applied this same method to automate literature searches in massive databases containing health-related studies for systematic reviews.
To give you a sense of the time savings involved: An average systematic review can take a team of researchers a year or more to produce. In this time of emergency, Professors Grossman and Cormack and the St. Michael’s researchers have combined AI technology with a larger review team to produce systematic reviews in about two weeks. Two weeks!
Here’s a link to the article and also a link to another discussion of the effort. Needless to say, it’s never been more important than now to expedite processes related to fighting COVID-19, so it’s great to see Maura and Gordon use their CAL approach to help in that effort. Hopefully, it gets us closer to that “light at the end of the tunnel” with regard to the COVID-19 pandemic (and perhaps better prepare ourselves against future pandemics).
So, what do you think? Do you think that technology will help us end the pandemic sooner? Please share any comments you might have or if you’d like to know more about a particular topic.
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