APPLICATIONS OF THE EXTENSION INNOVATION METHOD IN TREATMENT REGIMENS GENERATION WITH DEEP LEARNING

Abstract

Traditional treatment research lays much emphasis on therapeutic effects of a single drug or combination drugs, failing to explore appropriate treatment regimens that can be formed based on the state of an illness, medical history, financial capacity, and adverse drug reactions. For this reason, the extension innovation method is introduced to implement doctors’ thinking process in formulating treatment regimens in deep learning. First, an extension model is established for diseases according to the basic-element theory and the extension set theory based on the magnetic resonance image classification result under deep learning. Subsequently, extension analysis is made to analyze pathogenesis and corresponding treatment procedures; and multiple feasible treatment regimens are generated through extension transformation. At last, priority-degree evaluation is carried out to quantitatively assess the proposed treatment regimens and select a better regime from them. Here, ankylosing spondylitis (AS) is taken for example to validate the feasibility of applying the extension innovation method in treatment regime generation. Main contributions of this research are that both the extension model and the priority-degree evaluation method are introduced in treatment regime generation. In this way, after the deep learning method was used to automatically extract and classify medical image data features and determine the disease activity stage of ankylosing spondylitis, a formal and quantitative effective method can be provided for establishing the framework model of diseases and evaluating corresponding therapeutic regimens, then a foundation can be laid for arithmetic research on intelligent treatment regime generation via computer simulation of human thinking.