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Machine Learning Application to Combat Superbugs in Hospitals: A Primer to Infection Prevention Practitioners |Biomedgrid

Journal: American Journal of Biomedical Science & Research (Vol.16, No. 5)

Publication Date:

Authors : ;

Page : 520-523

Keywords : Machine learning; Hospital infections; Artificial intelligence; Prediction; Infection control;

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Healthcare-Associated Infections (HAIs) which defined as infections arising and developing during hospital stay or during the process of medical care in healthcare facilities. HAIs represent the most serious threat to patient safety, and it also represent global public health concern. HAIs have a significant clinical as well as financial impact due to prolonged hospitalization, increased mortality, and morbidity, increased antimicrobial resistance and increased direct costs for medical services. Surviellance in its conventional way, in which every patient's file is reviewed for the presence of HAIs, is time consuming and labor intensive. To improve the efficiency and strength of infection prevention and surveillance systems, information technology, data science and artificial intelligence have been recently applied. We need tools that help prediction, early diagnosis, surveillance, and treatment of HAIs to prevent human efforts of disease containment from being overwhelmed.

Last modified: 2024-04-12 22:01:04