MBZUAI researchers developed a machine-learning method to predict antimicrobial resistance (AMR) by analyzing electronic health records. The system predicts if a patient will experience AMR when prescribed an antibiotic or if infected with a bacterium. Published in Scientific Reports, the innovation helps physicians identify patients at risk for AMR by using patient demographics, lab results, and physician notes. Why it matters: This approach can help combat the rise of drug-resistant bacteria by providing timely predictions and supporting more informed prescription decisions.
KAUST researchers have discovered that combining ultraviolet sunlight with phages increases the susceptibility of antibiotic-resistant bacteria to sunlight disinfection. This breakthrough addresses the growing threat of antimicrobial resistance, as the rate of discovering new antibiotics has slowed. The team demonstrated this method's effectiveness against a pathogenic E. coli strain found in Saudi wastewater. Why it matters: This research offers a promising alternative to traditional antibiotics, particularly relevant in regions like Singapore and the GCC where treated wastewater is a crucial water supply source.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.
KAUST Professor Hernando Ombao is leading the Biostatistics Group to develop statistical models for projecting hospitalization surges during the COVID-19 pandemic. The group uses techniques like time series analysis and stationary subspace analysis to understand complex biological processes. The models aim to provide public health officials with accurate hospitalization estimates under varying scenarios. Why it matters: This research contributes to preparedness and resource allocation in healthcare systems during public health crises, with potential applications beyond COVID-19.
MBZUAI researchers are developing AI applications for malaria prevention in Indonesia using sensory data fusion and digital twins. Another MBZUAI team is using machine learning and computer vision to detect cardiovascular disease from CT scans in collaboration with the University of Oxford. AI-powered remote patient monitoring is also being explored for proactive interventions and chronic disease management. Why it matters: These projects demonstrate the potential of AI to address healthcare challenges in underserved communities and improve disease prevention and management in the region.