Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting
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Background: Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying pa... ...
