Ophthalmic Syndrome Differentiation System and Digital Chinese Medicine

In traditional Chinese medicine(TCM),ophthalmic syndrome differentiation is an ophthalmology-specific method for identifying syndromes based on the“Five Orbiculi”theory.It was devised by Professor Qing-Hua PENG through an unprecedented combination of syndrome element differentiation and ophthalmic clinical practices,based on the Clinical Terminology of Chinese Medical Diagnosis and Treatment-Syndromes of the National Standards of the People's Republic of China.This approach integrates an ophthalmic syndrome differentiation system with digital Chinese medicine(DCM),and proposes the extraction of syndrome elements of ophthalmic diseases from research on DCM.These elements are then quantified and organized to form a model of digital diagnosis and treatment specific to ophthalmology,which should help to achieve synergistic development of the ophthalmic syndrome differentiation system and DCM.

Research on Text Mining of Syndrome Element Syndrome Differentiation by Natural Language Processing

Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.