A World of Difference: Technology-Assisted Review in the Fight Against COVID-19

Using Continuous Active Learning® (CAL®) Technology-Assisted Review (TAR) — a 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.

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Editor’s Note: It is a good thing when advanced eDiscovery technologies and protocols can be applied to audit, investigation, and litigation efforts to increase the effectiveness and efficiency of discovery. However, it is a great thing when these same technologies and protocols can be applied to help medical researchers and clinicians save time in identifying and assessing information that may help in the development of COVID-19 policies and treatments. In the following two article extracts from the University of Waterloo,  Maura Grossman and Gordon Cormack’s work in supporting researchers, clinicians, and public health officials through the expert application of Technology-Assisted Review (TAR) with Continuous Active Learning® (CAL®) is provided as an example of a great use of advanced eDiscovery technologies and protocols.

Using Technology-Assisted Review to Find Effective Treatments and Procedures to Mitigate COVID-19

An extract from an article published by the University of Waterloo (Cheriton School of Computer Science)

Since the COVID-19 pandemic began, researchers and clinicians have rushed to understand the available treatments and procedures to mitigate this rapidly growing threat to human health. 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.

The urgency of the COVID-19 pandemic has necessitated a transformation in interdisciplinary collaboration, as well as in the process for completing systematic reviews for evidence-based medicine, so that reviews that used to take researchers many months to complete can now be 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 Continuous Active Learning® (CAL®) Technology-Assisted Review (TAR) — a 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.

Read the complete article on Using Technology-Assisted Review to Find Effective Treatments and Procedures to Mitigate COVID-19


AI Helping Physicians, Policy Makers Get The COVID-19 Information They Need

An extract from an article published by the University of Waterloo (Media Relations)

A new artificial intelligence tool is being used to help medical researchers at a Toronto-area hospital to shave months off the time they need to identify clinical studies available to help physicians treat COVID-19 patients.

In building the AI-driven search tool, researchers at the University of Waterloo used a machine-learning approach originally developed to expedite the review of documents in high-stakes litigation, to help the researchers mine through thousands of new studies on COVID-19 quickly.

“Searching and finding studies for systematic reviews has traditionally been a time-consuming and laborious process that uses keyword search,” said Maura Grossman, computer science research professor at the University of Waterloo. “It’s a long process that involves the manual screening of abstracts, and finally full papers.” Grossman worked with the primary developer of the tool, fellow computer science professor at the University of Waterloo, Gordon Cormack. The tool is being deployed in conjunction with the knowledge synthesis team at St. Michael’s Hospital on behalf of Health Canada, which commissioned the systematic review.

Read the complete article AI Helping Physicians, Policy Makers Get The COVID-19 Information They Need

Additional Reading

Source: ComplexDiscovery

Business as Unusual? Eighteen Observations on eDiscovery Business Confidence in the Summer of 2020

The results of the recent Summer 2020 eDiscovery Business Confidence Survey present the unfortunate and continuing impact of COVID-19 on the business of eDiscovery. However, for these pandemic-driven results to be fully understood, they should be viewed through the contextual lens of the results of all nineteen surveys that have been administered to eDiscovery professionals since the inception of the eDiscovery Business Confidence Survey in early 2016.



Check Out the Observations Now!

ComplexDiscovery combines original industry research with curated expert articles to create an informational resource that helps legal, business, and information technology professionals better understand the business and practice of data discovery and legal discovery.

All contributions are invested to support the development and distribution of ComplexDiscovery content. Contributors can make as many article contributions as they like, but will not be asked to register and pay until their contribution reaches $5.

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