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✅   Development, validation and economic evaluation of a machine learning algorithm for predicting the probability of kidney damage in patients with hyperuricaemia: protocol for a retrospective study

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Introduction Accurate identification of the risk factors is essential for the effective prevention of hyperuricaemia (HUA)-related kidney damage. Previous studies have established the efficacy of machine learning (ML) methodologies in predicting kidney damage due to other chronic diseases. Nevertheless, a scarcity of precise and clinically applicable prediction models exists for assessing the risk of HUA-related kidney damage. This study aims to accurately predict the risk of developing HUA-related kidney damage using a ML algorithm, which is based on a retrospective database. Methods and analysis This retrospective study aims to collect clinical data on outpatients and inpatients from the Sichuan Provincial People’s Hospital, China, covering the period from 1 January 2018 to 31 December 2021 with a ...

BMJ     Nov 29, 2024