Alzheimer's disease classification based on nonlinear high-order features and hypergraph convolutional neural network / 生物医学工程学杂志

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.

Gridded Cleaning Strategy for Medical Big Data / 医学信息学杂志

Depending on Shanghai medical big data center and taking the medical big data after quality control and before data utilization as research object,the paper establishes the data cleaning frame,gives the evaluation method for data availability,finds out the corresponding cleaning strategies according to the clustering analysis of data characteristics and repeatedly deduces the accuracy,reliability of the strategy,thus providing a strong support for the analysis and utilization of medical big data.

Framework design and requirements for information system security grading protection of medical organizations / 中华医院管理杂志

Description of the security grading protection used in the security protection system for information systems in medical organizations. Elaboration of the research ideas, process and some outcomes for the Fundamental Requirements for Security Grade Protection of Information Systems in Medical Organizations, from the five aspects of system modeling, grading guidance for industries, threat and risk analysis, security objective output, and security adjustment.