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Forecasting hospitalizations with AI

MBZUAI · Invalid Date

MBZUAI Professor Agathe Guilloux developed the SigLasso model to forecast hospitalizations using real-time data from Google and Météo France during the COVID-19 pandemic. The model integrates mobility data and weather patterns to predict hospitalization rates 10-14 days in advance. SigLasso outperformed industry standards like GRU and Neural CDE in reducing reconstruction error. Why it matters: This research demonstrates the potential of AI to improve healthcare resource allocation and crisis management by accurately predicting patient flow using readily available data.

A shock to the system

KAUST · Jul 19

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.