This paper introduces an explainable machine learning framework for early-stage chronic kidney disease (CKD) screening, specifically designed for low-resource settings in Bangladesh and South Asia. The framework utilizes a community-based dataset from Bangladesh and evaluates multiple ML classifiers with feature selection techniques. Results show that the ML models achieve high accuracy and sensitivity, outperforming existing screening tools and demonstrating strong generalizability across independent datasets from India, the UAE, and Bangladesh.
MBZUAI graduate Svetlana Maslenkova worked with Assistant Professor Mohammad Yaqub on a project focused on the earlier detection of kidney failure using tabular data. Maslenkova's master's thesis involved predicting Acute Kidney Injury (AKI) using Electronic Health Records (EHR), specifically the MIMIC-IV v2.0 database. She found that patient weight distribution was a factor in the severity of kidney failure. Why it matters: This research highlights the potential of AI and machine learning to improve healthcare outcomes through the analysis of often-overlooked tabular data in electronic health records.
KAUST Ph.D. student Michał Mańkowski's research on kidney allocation strategies was recognized as one of the American Journal of Transplantation's "Top 10 Articles of 2019." The research demonstrated how an accelerated allocation strategy could increase the utilization of kidneys at risk for non-use, potentially reducing discard rates. Mańkowski aims to translate his U.S.-focused research to improve organ transplantation within the Saudi Arabian healthcare system. Why it matters: This research has the potential to improve organ transplant outcomes and resource allocation in Saudi Arabia, addressing a critical healthcare need.
MBZUAI is developing AI algorithms to intelligently process data from wearables and home sensors for remote patient monitoring. The algorithms aim to analyze multiple strands of health data to provide a more comprehensive view of a patient's health, distinguishing between genuine emergencies and benign situations. MBZUAI's provost, Professor Fakhri Karray, believes this approach could handle 20-25% of diagnoses virtually, reducing the burden on healthcare systems. Why it matters: This research could significantly improve healthcare efficiency and accessibility in the UAE and beyond by enabling more effective remote patient monitoring and reducing unnecessary hospital visits.
MBZUAI mourns the passing of UAE President Sheikh Khalifa bin Zayed Al Nahyan. The university offers condolences to the Royal family, the UAE government, and the people. The Ministry of Presidential Affairs declared 40 days of official mourning. Why it matters: This event marks a significant moment of transition and reflection for the UAE and its institutions.