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Background: A new learning-based patient similarity measurement was proposed to measure patients' similarity for heterogeneous electronic medical records (EMRs) data.Methods: We first calculated feature-level similarities according to the features' attributes. A domain expert provided patient similarity scores of 30 randomly selected patients. These similarity scores and feature-level similarities for 30 patients comprised the labeled sample set, which was used for the semi-supervised learning algorithm to learn the patient-level similarities for all patients. Then we used the k-nearest neighbor (kNN) classifier to predict four liver conditions. The predictive performances were compared in four different situations. We also compared the performances between personalized kNN models and other machine learning models. We assessed the predictive performances by the area under the receiver operating characteristic curve (AUC), F1-score, and cross-entropy (CE) loss.Results: As the size of the random training samples increased, the kNN models using the learned patient similarity to select near neighbors consistently outperformed those using the Euclidean distance to select near neighbors (all P values < 0.001). The kNN models using the learned patient similarity to identify the top k nearest neighbors from the random training samples also had a higher best-performance (AUC: 0.95 vs. 0.89, F1-score: 0.84 vs. 0.67, and CE loss: 1.22 vs. 1.82) than those using the Euclidean distance. As the size of the similar training samples increased, which composed the most similar samples determined by the learned patient similarity, the performance of kNN models using the simple Euclidean distance to select the near neighbors degraded gradually. When exchanging the role of the Euclidean distance, and the learned patient similarity in selecting the near neighbors and similar training samples, the performance of the kNN models gradually increased. These two kinds of kNN models had the same best-performance of AUC 0.95, F1-score 0.84, and CE loss 1.22. Among the four reference models, the highest AUC and F1-score were 0.94 and 0.80, separately, which were both lower than those for the simple and similarity-based kNN models.Conclusions: This learning-based method opened an opportunity for similarity measurement based on heterogeneous EMR data and supported the secondary use of EMR data. [ABSTRACT FROM AUTHOR]

Background: Programmed cell death protein 1 (PD-1) plays an important role in immune response regulation as a co-inhibitory signal during T cell activation. However, there is little known about the serum autoantibody profile of PD-1 in systemic lupus erythematosus (SLE), a disease characterized by the breakdown of immune tolerance to self-antigens and an excessive production of autoantibodies. Thus, we aim to investigate the serum levels and function of anti-PD-1 in patients with new-onset SLE., Methods: Serum levels of anti-PD-1 IgG and IgM isotypes were detected in new-onset SLE patients (n = 90), rheumatoid arthritis (n = 50), primary Sjogren's syndrome (n = 50), ankylosing spondylitis (n = 25), and healthy controls (HC) (n = 80) using an enzyme-linked immunosorbent assay (ELISA). The correlation of anti-PD-1 with clinical characteristics and laboratory parameters of patients with new-onset SLE was analyzed. The effects of purified anti-PD-1 IgG from SLE patients on T cell proliferation were measured using flow cytometry., Results: The data revealed increased levels of anti-PD-1 IgG, but not IgM, especially in new-onset SLE patients, and the positive rate of anti-PD-1 IgG was 30 (33.3%). The level of anti-PD-1 IgG was closely associated with malar rash (OR = 15.773), arthritis (OR = 22.937), serositis (OR = 16.008), hematological (OR = 35.187), renal (OR = 8.306), and neurological involvement (OR = 37.282). Moreover, the serum levels of anti-PD-1 IgG were positively correlated with the SLE disease activity index (SLEDAI) score (r = 0.296, p = 0.0046) and the erythrocyte sedimentation rate (ESR) (r = 0.2446, p = 0.0201). In vitro examination showed that purified anti-PD-1 IgG obtained from SLE patients enhanced T cell proliferation when co-cultured with dendritic cells (DCs)., Conclusions: The current study indicates, for the first time, that the serum levels of co-inhibitor autoantibodies against PD-1 are elevated in new-onset SLE patients and are associated with disease activity in SLE. Autoantibodies against PD-1, facilitating T cell proliferation, revealed a new insight into the function of negative regulation signals involved in the pathogenesis of SLE.