Why carry out this study?

Hematological patients with febrile neutropenia presenting with multidrug-resistant Gram-negative bacilli (MDR-GNB) infections frequently receive inappropriate empirical antibiotic therapy which increases their morbidity and mortality.

Current studies aiming to identify patients at risk for MDR-GNB in this population use single predictive analysis focused on small sets of variables.

We hypothesized that machine learning using information stored in electronic health records could be useful to predict MDR-GNB in these patients.

What was learned from the study?

Clinical data stored directly in electronic health records can be used to identify risk factors for MDR-GNB infections in severe hematological patients at FN onset.

The high quantity of data allowed us to identify new risk factors for MDR infections.

Machine learning has proved useful for clinical predictors in MDR-GNB infections, thereby helping to provide personalized medical care.