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Young children are at high risk of severe outcomes due to pneumonia. The World Health Organization has established criteria to help guide their referral to hospital, but improved clinical decision tools are needed to ensure that children at risk are not missed. In this issue, Patrick Staunton and colleagues, using data from Malawi, report the development of a machine learning model that predicts hospitalization of young children within seven days of initial presentation with pneumonia in a primary care setting. The model outperformed existing risk prediction tools, suggesting that its clinical implementation may benefit early identification of children with severe respiratory disease.
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