An adaptive deep learning model to differentiate syndromes of infectious fever in smart medicine

Recently, smart medicine has been considered as a promising technique to treat the intractable diseases by combining deep learning techniques with medical Internet of Things. As an important component in the integration of traditional and western medicine, smart medicine is particularly effective to treat infectious fever. Before the cause of infectious fever diseases is ascertained, the Chinese medicine intervention is able to alleviating symptoms and strive for time for the causes detection. However, accurate syndrome differentiation, a difficult issue in infectious fever, is the premise of the Chinese medicine intervention. This work presents a possible adaptive deep learning model by integrating an adaptive dropout function into the stacked auto-encoder for computer-aided syndrome differentiation in infective fever. Moreover, we summarize the main syndromes and prescriptions in infectious fever. This work is expected to further the development of smart medicine, especially smart Chinese medicine. More importantly, it points out a novel research direction and medical technique in the treatment of infectious fever in clinic.