Exploring machine learning methods for predicting systemic lupus erythematosus with herpes

Abstract

ObjectivesTo investigate whether machine learning, which is widely used in disease prediction and diagnosis based on demographic data and serological markers, can predict herpes occurrence in patients with systemic lupus erythematosus (SLE).MethodsA total of 286 SLE patients were included in this study, including 200 SLE patients without herpes and 86 SLE patients with herpes. SLE patients were randomly divided into a training group and a test group, and 18 demographic characteristics and serological indicators were compared between the two groups.ResultsWe selected basophil, monocyte, white blood cell, age, immunoglobulin E, SLE Disease Activity Index, complement 4, neutrophil, and immunoglobulin G as the basic features of modeling. A random forest model had the best performance, but logistic and decision tree analyses had better clinical decision‐making benefits. Random forest had a good consistency between feature importance judgment and feature selection. The 10‐fold cross‐validation showed the optimization of five model parameters.ConclusionThe random forest model may be an excellently performing model, which may help clinicians to identify SLE patients whose disease is complicated by herpes early.

Systemic lupus in the era of machine learning medicine

“…Other outcomes for patients with SLE included reduced risk of breast cancer with the presence of prognostic genetic biomarkers (ie, IRF7, IFI35 and EIF2AK2 gene expression) identified with LASSO. 165 Models for the prediction of joint erosions LR model (AUC 0.806), 164 herpes infection (RF, AUC 0.942) 167 and hypothyroidism (RF, AUC 0.772) 166 have also been developed using clinical and serological data. Among the selected features for these models, autoantibodies were found to be important predictors, for example, anti-carbamylated protein and anti-citrullinated protein antibodies for joint erosion 164 and anti-dsDNA and anti-SSB/La for hypothyroidism.…”

“…Among the selected features for these models, autoantibodies were found to be important predictors, for example, anti-carbamylated protein and anti-citrullinated protein antibodies for joint erosion 164 and anti-dsDNA and anti-SSB/La for hypothyroidism. 167 ML models showed promise in predicting the risk of hospitalisation and length of stay from EMR data (best performing models LSTM and XGBoost, AUC 0.88) 20 41 42 142 169 and associated healthcare costs from administrative databases. 17 142 …”

Artificial intelligence differentiates abdominal Henoch‐Schönlein purpura from acute appendicitis in children

“…7 Machine learning, a vital branch of AI, can handle large-scale data simultaneously and provide rapid prediction results with high accuracy and automation. [8][9][10] It integrates medical data electronically and can encapsulate higher-order non-linear interactions between predictors-which traditional modeling approaches like logistic regression models cannot handle. 11 These AI/ machine learning advantages have developed it into various software systems applied in various life sciences.…”

“…It uses computer systems to implement prediction or decision‐making tasks using algorithms and statistical models 7 . Machine learning, a vital branch of AI, can handle large‐scale data simultaneously and provide rapid prediction results with high accuracy and automation 8–10 . It integrates medical data electronically and can encapsulate higher‐order non‐linear interactions between predictors—which traditional modeling approaches like logistic regression models cannot handle 11 .…”