A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

通过患者登记、大型流行病学研究、电子健康记录、行政索赔、临床试验存储库、移动健康设备和分子数据库收集了大量肾脏病学数据。这些大数据的应用，特别是使用机器学习算法，为获得对肾脏疾病的新见解、促进个性化医疗和改善患者护理提供了独特的机会。努力让科学界免费获取大量数据、提高对数据共享重要性的认识以及先进计算算法的可用性将促进大数据在肾脏病学中的应用。然而，在访问、协调和整合来自不同来源的不同格式的数据集方面存在挑战，提高数据质量并确保数据安全，并保护患者和研究参与者的权利和隐私。此外，对计算机技术校准和预测准确性的怀疑削弱了对数据驱动的医学突破的乐观情绪。旨在研究肾脏健康和疾病的机器学习算法必须能够处理这一专业的细微差别，必须随着医疗实践的不断发展而适应，并且必须对外部和未来的数据集具有全球性和前瞻性的适用性。