Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies.

BACKGROUND: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. OBJECTIVES: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. SOURCES: A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. CONTENT: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. IMPLICATIONS: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

Pollen analysis reveals plants foraged by Africanized honeybees in the southern Pantanal, Brazil.

The pollen diet of Africanized honeybees Apis mellifera L. was studied during seven months (October 2006 to April 2007) in a natural forest fragment in the southern Pantanal, sub-region of Abobral, Mato Grosso do Sul. The analysis of the pollen diet was based on direct observations of the bees visiting flowers as well as through the use of a pollen trap installed in a wild colony in a tree hole in the same forest fragment. The total of 28 species in 15 botanical families were observed as potential sources of pollen for A. mellifera, with visits registered in 24 of these species in 13 botanical families. In the pollen trap we recorded 25 pollen types. This study is the first report to use this type of trap for pollen collection in the Neotropical region and aimed to identify the polliniferous bee plants of Brazilian Pantanal.