Machine Learning for Antibiotic Resistance

Posted byPhilip A. Lederer MDPosted ininfectious disease

Today I turn your attention to a paper in Science by Mathew Stracy and colleagues, Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections.

The researchers used whole genome sequencing and machine learning and looked at a large number of urinary tract and wound infections. They discussed the importance of resistance being driven by “rapid reinfection” from a bacterial strain resistant to the prescribed antibiotic. The machine learning algorithm can help reduce the emergence and spread of resistant pathogens.

I’m not clear that machine learning for antibiotic resistance is ready for “prime time,” but it is getting closer. These data-driven approaches need to be validated and integrated into electronic medical records, such as EPIC.
No need to beware the Ides of March; spring is almost here. Stay positive!
Having a Growth Mindset is most important.
Singing in the Longwood Chorus a few days ago. Yay to Jeremy Faust, our conductor, and Carolyn Hsu, our president.

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Published by Philip A. Lederer MD

Thanks for visiting my website! I was born in 1980 in Columbus, Ohio and live with my family in Jamaica Plain, Massachusetts. My training is in internal medicine, public health, and infectious diseases. I am an advocate, writer, and musician, and recently I completed my first marathon.

One thought on “Machine Learning for Antibiotic Resistance”

  1. Thanks

    On Fri, Mar 4, 2022 at 4:58 AM Health and Healing wrote:

    > Philip Lederer MD posted: ” Today I turn your attention to a paper in > Science by Mathew Stracy and colleagues, Minimizing treatment-induced > emergence of antibiotic resistance in bacterial infections. The researchers > used whole genome sequencing and machine learning and looked a” >

    Reply

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