A retrospective study in Abu Dhabi, UAE, developed a machine learning-based prognostic system to predict the risk of seven complications in COVID-19 patients using data from 3,352 patient encounters. The system, trained on data from the first 24 hours of admission, achieved high accuracy (AUROC > 0.80) in predicting complications like AKI, ARDS, and elevated biomarkers in geographically split test sets. The models primarily used gradient boosting and logistic regression.
A KAUST Rapid Research Response Team (R3T) is collaborating with healthcare stakeholders to combat COVID-19. Xin Gao and his Structural and Functional Bioinformatics (SFB) Group are developing an AI-based diagnosis pipeline from CT scans of COVID-19 patients. The AI pipeline aims to address the high false negative rates associated with nucleic acid detection. Why it matters: This research could improve COVID-19 diagnostics and potentially inform understanding of viral pathogenesis.
KAUST Professor Xin Gao formed part of the Rapid Research Response Team (R3T) to address the COVID-19 pandemic. Gao's team developed and deployed an AI system to assist clinicians in improving the accuracy of COVID-19 diagnoses. The lecture outlines how the AI system was built and implemented. Why it matters: This showcases how GCC academic institutions are leveraging AI to address pressing healthcare challenges.
A KAUST team led by Xin Gao developed an AI model for COVID-19 detection from CT scans, addressing limitations of existing methods. The model incorporates a novel embedding strategy, a CT scan simulator, and a 2.5D deep-learning algorithm. Tested at King Faisal Specialist Hospital, the model demonstrated high accuracy in detecting COVID-19 cases. Why it matters: This research provides a valuable tool for rapid and accurate COVID-19 diagnosis in the region, especially in early-stage infections, improving healthcare outcomes.
This paper examines the relationship between COVID-19 spread and weather patterns across 89 cities in Saudi Arabia using machine learning. The study uses daily COVID-19 case reports from the Saudi Ministry of Health and historical weather data. The results indicate that temperature and wind speed have the strongest correlation with the spread of COVID-19, with a random forest model achieving the best performance.