AI Seminar Series 2022: Katrina Kalantar

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On Sept. 16, Katrina Kalantar – a Computational Biologist at the Chan Zuckerberg Initiative – presented "Methods for Improving Diagnosis of Infectious Diseases" at the AI Seminar.

Infectious diseases are a leading cause of morbidity and mortality worldwide. One persistent challenge in the mitigation of infectious diseases is in the ability to accurately diagnose the etiology of infections using standard clinical diagnostics. Metagenomic next-generation sequencing (mNGS) has transformed disease surveillance by enabling the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. However, mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample and downstream processing to translate the multidimensional data into actionable information. In this talk, Katrina Kalantar presents work by our team to develop tools and facilitate trainings that enable researchers around the world to analyze mNGS data, gaining further insight into the microbial composition of diverse sample types. Katrina Kalantar then discusses how these tools have been applied alongside a variety of machine-learning techniques to improve the diagnosis of infections in two distinct patient cohorts. In an initial study, these tools were applied to a cohort of 92 adults with acute respiratory failure due to infectious and non-infectious causes and the development of logistic regression models enabled improved diagnosis of lower respiratory tract infection. In a recent follow-up study, support vector machines were trained to classify patients with and without sepsis amongst a heterogeneous cohort of critically ill patients. Altogether, this talk will highlight the value of mNGS for diagnosis of infectious diseases, the tools that underlie the ability to develop, and considerations in early development of diagnostic tools.

Watch the full presentation below:

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