Optimization of Multidimensional Clinical Information System for Schizophrenia

Schizophrenia is a serious mental disease whose pathogenesis has not been fully elucidated. Its clinical evaluation and diagnosis still highly depend on the clinical experience of doctors. It is of great scientific value and clinical significance to study the inducing factors and neuropathological mechanism of schizophrenia. Based on the four research problems of schizophrenia, this paper analyzes the data types that need to be stored in clinical trials and scientific research, including basic information, case report data, neuropsychological and cognitive function evaluation, magnetic resonance data, electroencephalogram (EEG) data, and intestinal flora data. Through the demand analysis of the system, including the data management part, data analysis part, the functional demand of the system management part, and the overall nonfunctional demand of the system, the overall architecture design, functional module division, and database table structure design of the system are completed. Adopting Browser/Server (B/S) architecture and front-end and back-end separation mode and applying Java and Python programming language, based on spring framework and database, a multidimensional information management system for schizophrenia is designed and implemented, which includes four modules: data analysis, data management, system management, and security control. In addition, each functional module of the system is designed and implemented in detail, and the software operation flow of each module is illustrated with the sequence diagram. Finally, the multidimensional data of schizophrenia collected in our laboratory were used for system test to verify whether the system can meet the needs of clinical big data management of schizophrenia and the multidimensional information management system of schizophrenia can meet the needs of clinical big data management. The information management system helps schizophrenic researchers to carry out data management and data analysis. It also has advantages that are easy to use, safe, and efficient and has strong scalability in data management, data analysis, and scalability. It reflects the innovation of the system and provides a good platform for the management, research, and analysis of clinical big data of schizophrenia.