BackgroundKidney involvement frequently occurs in systemic lupus erythematosus (SLE), and its clinical manifestations are complicated. We profiled kidney involvement in SLE patients using deep learning based on data from the National Database of Designated Incurable Diseases of Japan.MethodsWe analyzed the cross-sectional data of 1655 patients with SLE whose Personal Clinical Records were newly registered between 2015 and 2017. We trained an artificial neural network using clinical data, and the extracted characteristics were evaluated using an autoencoder. We tested the difference of population proportions to analyze the correlation between the presence or absence of kidney involvement and that of other clinical manifestations.ResultsData of patients with SLE were compressed in a feature space in which the anti-double-stranded deoxyribonucleic acid (anti-dsDNA) antibody titer, antinuclear antibody titer, or white blood cell count contributed significantly to distinguishing patients. Many SLE manifestations were accompanied by kidney involvement, whereas in a subgroup of patients with high anti-dsDNA antibody titers and low antinuclear antibody titers, kidney involvement was positively and negatively correlated with hemolytic anemia and inflammatory manifestations, respectively.ConclusionAlthough there are various combinations of SLE manifestations, our study revealed that some of them are specific to kidney involvement. SLE profiles extracted from the objective analysis will be useful for categorizing SLE manifestations.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10157-023-02337-x.