In Jordan, antimicrobial resistance (AMR) has emerged as a significant concern, ranking as the fourth leading cause of mortality in the country. Despite this alarming statistic, there is a lack of comprehensive data on the demographics and clinical characteristics associated with AMR cases in Jordan.
A recent retrospective analysis conducted at Al-Hussein/Salt Hospital shed light on the prevalent microorganisms contributing to antibiotic resistance in the region. The study identified Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus as the most commonly isolated bacteria exhibiting resistance to antibiotics.
To predict the distribution patterns of antibiotic-resistant microorganisms in Jordan, researchers have turned to machine learning techniques. Specifically, the Random Forest (RF) model demonstrated superior accuracy in forecasting AMR patterns, underscoring the importance of continued monitoring to facilitate appropriate antibiotic therapy.
By leveraging advanced technologies such as machine learning, healthcare professionals in Jordan can take proactive steps to combat the rise of antibiotic resistance. Through targeted interventions and surveillance efforts, it is possible to mitigate the impact of AMR and safeguard the effectiveness of antibiotics for future generations.
