University of Queensland researchers have used machine learning to help predict the risk of secondary bacterial infections in hospitalized COVID-19 patients.
The machine learning technique can help detect whether antibiotic use is critical for patients with these infections. The research is published in Lancet Microbe.
Associate professor Kirsty Short, PhD, from the School of Chemistry and Molecular Biosciences says secondary bacterial infections can be extremely dangerous for those hospitalized with COVID-19.
“Estimates of the incidents of secondary bacterial infections in COVID-19 patients are broad, but in some studies, 100% of fatal cases have suffered a bacterial co-infection,” says Short in a release. “To reduce the risk of bacterial co-infections, it would be theoretically possible to just treat all COVID-19 patients with antibiotics.
“However, there’s a danger that over-treating with antibiotics could potentially lead to antibiotic resistance and the creation of bacterial superbugs. We’ve helped develop a robust predictive model to determine the risk of bacterial infections in COVID-19 patients, facilitating a careful use of antibiotics.”
The technique is known as the “least absolute shrinkage and selection operator”—or LASSO for short. Blood samples of COVID-19 patients from six countries were analyzed using the LASSO technique.
The team found that the expression of seven genes in a COVID-19 patient can predict their risk of developing a secondary respiratory bacterial infection after 24 hours of hospital admission.
Meagan Carney, PhD, from the School of Mathematics and Physics says these seven genes will now guide clinicians to making a more informed choice when it comes to antibiotic use.
“This data raises the exciting possibility that gene transcription and analysis at the time of clinical presentation at a hospital, together with machine learning, can change the game for antibiotic prescription,” Carney says in a release.
She also emphasizes that LASSO is simplified compared to the complex machine learning methods surrounding artificial intelligence that are currently being discussed in the media.
“Research projects like this one, which use much less complex machine learning methods, can help bridge the gap between data scientists and scientists in other fields,” she says in a release. “We should strive towards making data science less of a black box and inspire scientists across the world to better understand how it can revolutionize the medical industry.”
The researchers would like to acknowledge the extensive international collaboration of clinicians, virologists, bioinformaticians, and many other experts that made this study possible, including the PREDICT Consortium, the Snow Foundation, and the Nepean Hospital.
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