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Turning the Output of the Microbiology Laboratory Into Gold

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Manage episode 426190221 series 2991760
Content provided by American Society for Microbiology. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by American Society for Microbiology or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

The European alchemists of the 12th century sought to find the philosopher’s stone, a substance that would transmute base metals, such as lead, into precious metals, such as silver and gold. Today, we discuss whether data analysis, including machine learning, can transmute base laboratory data into precious clinical tools. We will use antimicrobial susceptibility testing as a case-study for new applications of data analysis. Some of the questions we will address include:

  • How can relatively simple data analyses be used to build upon current methods of verification of antimicrobial susceptibility testing?
  • How do commercial systems analyze individual susceptibility results and can we improve on this analysis using new methods?
  • Finally, what is the long-term potential for leveraging laboratory data and other clinical data to improve and support clinical decision making? And what needs to happen to realize this goal?
Guests: Related article:

The modern alchemy of clinical pathology: turning the output of microbiology laboratory operations into gold

Links:
  • Join ASM for up to 50% off the publication fees when you publish in JCM or any of the ASM journals.
  • Watch this episode: youtu.be/rWuQ0nSWL1Y

This episode of Editors in Conversation is brought to you by the Journal of Clinical Microbiology and hosted by JCM Editor in Chief, Alex McAdam and Dr. Elli Theel. JCM is available at https://jcm.asm.org and on https://twitter.com/JClinMicro. Visit journals.asm.org/journal/jcm to read articles and/or submit a manuscript.

  continue reading

89 episodes

Artwork
iconShare
 
Manage episode 426190221 series 2991760
Content provided by American Society for Microbiology. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by American Society for Microbiology or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

The European alchemists of the 12th century sought to find the philosopher’s stone, a substance that would transmute base metals, such as lead, into precious metals, such as silver and gold. Today, we discuss whether data analysis, including machine learning, can transmute base laboratory data into precious clinical tools. We will use antimicrobial susceptibility testing as a case-study for new applications of data analysis. Some of the questions we will address include:

  • How can relatively simple data analyses be used to build upon current methods of verification of antimicrobial susceptibility testing?
  • How do commercial systems analyze individual susceptibility results and can we improve on this analysis using new methods?
  • Finally, what is the long-term potential for leveraging laboratory data and other clinical data to improve and support clinical decision making? And what needs to happen to realize this goal?
Guests: Related article:

The modern alchemy of clinical pathology: turning the output of microbiology laboratory operations into gold

Links:
  • Join ASM for up to 50% off the publication fees when you publish in JCM or any of the ASM journals.
  • Watch this episode: youtu.be/rWuQ0nSWL1Y

This episode of Editors in Conversation is brought to you by the Journal of Clinical Microbiology and hosted by JCM Editor in Chief, Alex McAdam and Dr. Elli Theel. JCM is available at https://jcm.asm.org and on https://twitter.com/JClinMicro. Visit journals.asm.org/journal/jcm to read articles and/or submit a manuscript.

  continue reading

89 episodes

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