Hospital-Acquired Infections can be Predicted by Machine Learning

Machine Learning (ML) used by Finnish researchers predicts the risk of developing a severe and life-threatening infection from ‘hospital bug.’ This technology will thereby reduce hospital-acquired or nosocomial infections caused by Staphylococcus epidermidis.

Staphylococcus epidermidis is an ubiquitous colonizer of healthy human skin, but it is also a notorious source of serious infections with indwelling devices and surgical procedures such as hip replacements.

‘High-potential nosocomial infections occurring during surgery can be reduced by proactively identifying high-risk genotypes with the help of the machine learning device.’


A team of microbiologists and geneticists from the Aalto-University and the University of Helsinki, Finland, combined large-scale population genomics and in vitro measurements of immunologically relevant features of these bacteria.

Using ML, they could successfully predict the risk of developing infection from the genomic features of a bacterial isolate, according to a study published in the journal Nature Communications.

It has not been known whether all members of the S. epidermidis population colonizing the skin asymptomatically are capable of causing such infections, or if some of them have a heightened tendency to do so when they enter either the bloodstream or a deep tissue.

But, the new finding opens the door for future technology where high-risk genotypes are identified proactively when a person is to undergo a surgical procedure, which has high potential to reduce the burden of nosocomial infections caused by S. epidermidis, the researchers noted.

Source: IANS