Today's email brought an interesting invitation to a webinar on December 6 on:
"Whole genome sequencing and
electronic health record machine learning for
hospital outbreak detection: A novel paradigm."
It's a Genomeweb-based webinar, sponsored by Tecan, which is involved in software and lab automation. It's December 6, 2021, at 1 pm ET. I've clipped the text below. The speaker is Dr. Lee Harrison of University of Pittsburgh.
Text clipped as:
Approaches for hospital outbreak detection have remained unchanged for years. When an outbreak is suspected, a method to establish genetic relatedness such as whole-genome sequencing (WGS) may be performed. This approach can miss outbreaks and falsely identify suspected outbreaks that are refuted by WGS.
In late 2016, University of Pittsburgh Professor of Medicine and Epidemiology, Dr. Lee Harrison and colleagues began developing the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines WGS surveillance with data mining and machine learning of the electronic health record (EHR) to detect outbreaks and correctly identify their routes of transmission, respectively. The team found EHR machine learning useful for transmission routes that cannot be identified by traditional means. The purpose of this talk is to describe the results of using this novel approach for detecting hospital outbreaks.
For a listing of Genomeweb webinars,
For a November 9 interview between healthcare expert and future Harry Glorikian and the CEO fo PAIGE, here.