Diagnosis of infectious factors in patients with chronic glomerular disease using deep learning‐based health information data

Abstract

The study was aimed to explore the effect of information health data based on deep learning of neural network to diagnose the infectious factors of patients with chronic glomerular disease (CGD) and evaluate its diagnostic effect. Ninety patients with CGD were selected and randomly rolled into control group A, control group B, and observation group, with 30 cases in each group. Big data scientific research analysis platform was used for data integration, convolutional neural network (CNN) was employed for feature analysis, correlation analysis, and screening of disease-related biomarkers. The patients were diagnosed by observation of symptoms and signs, combined diagnosis of blood test and urine test, and information health data diagnosis based on deep learning CNN. As a result, the specificity, sensitivity, and accuracy of information health data diagnosis based on deep learning CNN were 78.9%, 87.6%, and 92.1%, respectively.The main sources of infections in patients were lung infections, bloodstream infections, urinary system infections, skin and soft tissue infections, and upper respiratory tract infections. Amongst them, lung infection accounted for the highest proportion, reaching 65.4%, followed by blood infection (11.2%) and skin tissue infection (9.6%).The pathogens of infection were mainly bacteria, viruses, fungi, tuberculosis, and pneumocystis pneumonia (PCP), amongst which bacterial infections accounted for the highest proportion (31.5%), followed by PCP (25.6%). In short, the information health data based on deep learning CNN had high specificity, sensitivity, and accuracy for the diagnosis of CGD. The main infectious factors of CGD were pulmonary infection and blood infection, and the pathogens were mainly bacteria and viruses.

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

“…It would be interesting to investigate the results of NUMERATE for other smart healthcare applications, such as skin disease detection (Hossen et al, 2022), chronic glomerular disease detection (Zhou et al, 2021), and vascular aging assessment (Shin, 2022).…”

“…It outperformed previous disease detection methods in terms of accuracy. It would be interesting to investigate the results of NUMERATE for other smart healthcare applications, such as skin disease detection (Hossen et al, 2022), chronic glomerular disease detection (Zhou et al, 2021), and vascular aging assessment (Shin, 2022). At NUMERATE, interpreting the result is in itself a difficult task.…”