A non-linear relationship between blood pressure and mild cognitive impairment in elderly individuals: A cohort study based on the Chinese longitudinal healthy longevity survey (CLHLS).

Background: Hypertension is an established risk factor for mild cognitive impairment (MCI) in elderly individuals. Nevertheless, the impact of different levels of blood pressure on the progression of MCI remains uncertain. This study aims to investigate the non-linear relationship between blood pressure and MCI in the elderly and detect the critical blood pressure threshold, thus, improving blood pressure management for individuals at high risk of MCI.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.

Background: Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level.

Estimating Bidirectional Transitions and Identifying Predictors of Mild Cognitive Impairment.

Background And Objectives: Various resources exist for treating mild cognitive impairment (MCI) or dementia separately as terminal events or for focusing solely on a 1-way path from MCI to dementia without taking into account heterogeneous transitions. Little is known about the trajectory of reversion from MCI to normal cognition (NC) or near-NC and patterns of postreversion, which refers to cognitive trajectories of patients who have reversed from MCI to NC. Our objectives were to (1) quantitatively predict bidirectional transitions of MCI (reversion and progression), (2) explore patterns of future cognitive trajectories for postreversion, and (3) estimate the effects of demographic characteristics, , cognition, daily activity ability, depression, and neuropsychiatric symptoms on transition probabilities.

A Stacking Framework for Multi-Classification of Alzheimer's Disease Using Neuroimaging and Clinical Features.

Background: Alzheimer's disease (AD) is a severe health problem. Challenges still remain in early diagnosis.

Objective: The objective of this study was to build a Stacking framework for multi-classification of AD by a combination of neuroimaging and clinical features to improve the performance.