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Related Concept Videos

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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Related Experiment Video

Updated: Nov 6, 2025

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
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A Pragmatic Machine Learning Model To Predict Carbapenem Resistance.

Ryan J McGuire1, Sean C Yu2, Philip R O Payne2

  • 1Department of Internal Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.

Antimicrobial Agents and Chemotherapy
|May 11, 2021
PubMed
Summary

Machine learning accurately predicts carbapenem-resistant (CR) infections using electronic health records. This model aids early identification of CR organisms, improving patient outcomes and antibiotic stewardship.

Keywords:
antibiotic resistancecarbapenemelectronic health recordmachine learningpredictive modeling

Related Experiment Videos

Last Updated: Nov 6, 2025

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
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Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

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Area of Science:

  • Infectious Diseases
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Carbapenem-resistant (CR) organism infections pose a significant and growing threat in the United States.
  • Early identification of CR infections is crucial but challenging, despite known risk factors for antibiotic resistance.

Purpose of the Study:

  • To develop and validate a machine learning (ML) prediction model for carbapenem resistance.
  • To identify key clinical features predictive of CR infections at the time of culture collection.

Main Methods:

  • Retrospective analysis of electronic health records from a tertiary-care academic medical center (2012-2017).
  • Inclusion of patients >18 years with at least one bacterial culture, extracting demographic, clinical, medication, and laboratory data.
  • Development of an extreme gradient boosting ML model using 67 features to predict CR organisms.

Main Results:

  • The study included 68,472 patients, with 1,088 identified as having CR organisms.
  • Key predictive features included prior antibiotic exposure, central venous catheter use, and inpatient surgery.
  • The ML model achieved an area under the receiver operating characteristic curve of 0.846, with 30% sensitivity and 99% negative predictive value.

Conclusions:

  • A machine learning model utilizing readily available clinical data can effectively predict CR infections.
  • The model demonstrates a high positive predictive value (30%) for CR infections at culture collection.
  • This predictive capability supports timely clinical decision-making and targeted interventions against CR organisms.