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A Pragmatic Machine Learning Model To Predict Carbapenem Resistance - PMC
Source: ncbi.nlm.nih.gov
Article summary:
1. The article discusses the development of a machine learning model to predict carbapenem resistance.
2. The model was developed using data from 68,472 patients admitted to a tertiary-care academic medical center between 2012 and 2017.
3. The model had an area under the receiver operating characteristic curve of 0.846, with a sensitivity of 30%, positive predictive value (PPV) of 30%, and negative predictive value of 99%.
Article analysis:
The article is generally reliable and trustworthy in its reporting, as it provides detailed information about the development of a machine learning model to predict carbapenem resistance. The authors provide clear descriptions of the methods used for data collection and analysis, as well as the results obtained from the model. Furthermore, they provide evidence for their claims by citing relevant studies in the field.
However, there are some potential biases that should be noted. First, the study was conducted at a single tertiary-care academic medical center, which may limit its generalizability to other settings or populations. Additionally, while the authors cite relevant studies in support of their claims, they do not explore any counterarguments or alternative perspectives on antibiotic resistance or machine learning models for predicting CR infections. Finally, while the authors note that inappropriate antibiotic coverage can lead to antibiotic resistance, they do not discuss any potential risks associated with overuse or misuse of antibiotics in clinical practice.