Department of research methodology includes department of Epidemiology, Clinical Epidemiology, Statistics, and Big Data and Artificial Intelligence Center. Clinical Epidemiology Department and Statistics Department provide epidemiological approaches and statistical techniques support for high-quality clinical research and population research. The Big Data and Artificial Intelligence Center is responsible for clinical research data management, establishment of data acquisition standards in line with international standards, and research on application of big data analysis techniques and methods such as machine learning and deep learning. The Big Data and Artificial Intelligence Center has established a high performance computing cluster system with 338 teraflops, using internationally advanced IT technologies, data mining technologies, deep learning technologies and artificial intelligence technologies to provide bigdata-driven decision-making support for disease diagnosis, clinical decision-making, and disease prevention & control.
Missions of the Department:
Clinical research design, statistical analysis and data management. Establishing public platform for big data storage, management and analysis, etc. Conducting research and training on health care big data analysis methodologies. Undertaking the work of Clinical Research Big Data Alliance of Ministry of Science and Technology of the People's Republic of China.
Hao Li, Department Director
Dual PhD in Neurobiology and Statistics. He has accumulated valuable experience in statistical consulting & analysis, statistical approaches design and clinical epidemiology methodology design.
Departments of Research Methodology
In order to promote the application of big data technologies in clinical research, big data software & hardware platforms have been established and further formulating the Clinical-research Big Data Platform through the data standardization and data archiving from previous clinical research.
The Data Elements Management System for Major Chronic Diseases has been established for ensuring the standardization of clinical research data and providing a collaborative research application platform for Clinical Research Big Data Alliance. The standardized clinical research datasets for neurological diseases was established through the standardization of existing clinical-research data sets under the reference of NINDS/NIH and WS. After standardization, these previous clinical-research databases would be updated and archived.
The data of the Clinical-research Big Data Platform includes large cohort study data (including population cohort, clinical registration cohort, clinical trial, and intervention cohort, etc.), data based on original electronic medical records, medical quality control data of neurological diseases, neuroimaging data, omics data, etc.
To ensure credible big data sharing and security, the Clinical research Data Statistics & Analysis Platform, the High-performance Computing Platform, the Imaging Interpretation & Analysis platform were established based on the Virtual Private Cloud System which will optimize the database management and data sharing platform, achieving centralized data management, strict quality controlling, and opening-up & sharing of clinical research data.
The Clinical Research Data Analysis Platform was established for improving the efficiency of clinical research data analysis and ensuring the safety & reliability of data analysis environment. The platform consists of Cloud Desktop Cluster System (used for clinical research data statistical analysis) and the High-Performance Computing Cluster. In Phase 1, the Desktop Cloud Cluster System consisted of 6 dual-channel high-performance servers offering Statistical analysis and image interpretation platform for 100 users from China National Clinical Research Center for Neurological Diseases. In Phase 2, it will offer statistical analysis platform for users from NCRCND and its collaborative research network center; the number of available users will increase to 300. In Phase 3, the system will be deployed on the public cloud system, which will greatly improve the coverage of data sharing, meanwhile, ensure the data security and consistency.