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Background: Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability. Methods: This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots. Results: The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features' importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (<9000) is significantly associated with AD conversion. For two-year prediction, low left inferior temporal thickness (<2) is most critical. For three-year prediction, higher FAQ scores (>4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (<1000) is the most critical feature. Conclusions: The established glass-box model EBMs with multimodal features demonstrated a superior ability with detailed interpretability in predicting AD conversion from MCI. Multi features with significant importance were identified. Further study may be of significance to determine whether the established prediction tool would improve clinical management for AD patients. [ABSTRACT FROM AUTHOR]

A structure scheme for a novel 6-DOF hybrid machine tool（ HMT） is proposed. In this scheme,a novel 4-PRP redundantly actuated parallel mechanism is proposed as the position module,which can realize three motion,with assistance of the 3-UPS&PU parallel mechanism,6-DOF machining can be realized. Firstly,the three-dimensional translations and three-dimensional rotations motion of this machine tool are analyzed based on screw theory. Then,the inverse displacement analysis model is provided by using analytical methods and the geometric relation of the mechanism. And the first-order influence coefficient matrix-Jacobian matrix and the second order influence coefficient matrix-Hessian matrix and complete kinematics model are established by using the influence coefficient method. At last,the position inverse solution is obtained by using MATLAB and the results simulation verification is carried out by using ADAMS,which verify that the correctness of the inverse displacement analysis model and the realizability of the mechanism.

The Lagrange method is applied to build dynamics model of the 6 DOF redundantly actuated hybrid machine tool. Firstly,the configuration of the 4-PRP + 3-UPS&PU machine tool is analyzed,and the coordinate system is established. Then,the expression of kinetic energy and potential energy of the machine tool are educed,and the equivalent generalized force of non-conservative force is analyzed,and the dynamics model is established by using the Lagrange method,which laid the foundation for the solution of driving force and dynamics analysis. Finally,combining with numerical examples,the numerical results of the dynamics model of the 4-PRP + 3-UPS&PU redundant drive hybrid mechanism and mechanism driving rod drive curve are obtained by MATLAB,and the correctness and feasibility of the dynamic model is verified by ADAMS simulation results.