糖尿病肾脏疾病(diabetic kidney disease, DKD)是糖尿病最常见的严重并发症，是全球慢性肾脏病的主要原因和终末期肾脏病的首位原因，给患者、家庭和社会都带来了沉重的疾病负担。建立风险预测模型以早期识别DKD高危人群，并使用个性化的诊疗方案，对降低DKD的发生率和疾病进展具有重要意义。过去几十年，许多学者构建了DKD早期发生风险预测模型。随着大数据和人工智能的发展，通过机器学习和深度学习算法构建预测模型已成为研究热点。本文从DKD早期发生风险模型构建方法层面进行综述，比较了传统预测方法、机器学习和深度学习算法的优劣，并总结了DKD早期发生风险预测模型的研究现状，旨在为DKD的预测模型构建和临床早期干预提供参考。

Diabetic kidney disease (DKD), the most common and severe complication of diabetes, represents a leading global cause of chronic kidney disease (CKD) and the foremost etiology of end-stage renal disease (ESRD). This condition imposes substantial disease burdens on patients, families, and healthcare systems worldwide. The development of risk prediction models to identify high-risk individuals for DKD at an early stage, coupled with personalized diagnostic and therapeutic strategies, holds critical importance for reducing the incidence and progression of DKD. Over the past several decades, numerous scholars have constructed predictive models for early DKD occurrence. With the advancement of big data and artificial intelligence, building predictive models through machine learning and deep learning algorithms has become a research hotspot. This article provided an overview of the methods for constructing risk models for the early occurrence of DKD, compared the advantages and disadvantages of traditional prediction methods, machine learning, and deep learning algorithms, and summarized the current research status of risk prediction models for the early occurrence of DKD, aiming to provide a reference for the construction of prediction models and early clinical intervention of DKD.