Background: Immunological Non-Response (INR) accelerated the progression of AIDS disease and brought serious difficulties to the treatment of HIV-1 infected people. The current definition of INR lacked a credible consensus, which affected the diagnosis, treatment and scientific research of INR.
Methods: We systematically analyzed the open source INR related references, used visualization techniques and machine learning classification models to propose the features, models and criteria that define INR.
Results: We summarized some consensus on the definition of INR. Among the features that defined INR, CD4+ T-cell absolute number and ART time were the best feature to define INR. The supervised learning classification model had high accuracy in defining INR, and the Support Vector Machine (SVM) had the highest accuracy in the commonly used supervised classification learning model. Based on supervised learning model and visualization technology, we proposed some criteria that could help to reach a consensus on INR definition.
Conclusion: This study provided consensus, features, model and criteria for defining INR.
Published Date: 2022-04-04; Received Date: 2022-03-08