Jing Cui

Area of research

Psychiatry and Mental health · Neurology

Research interest

Research topics from publications: XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease; Estimating Bidirectional Transitions and Identifying Predictors of Mild Cognitive Impairment; Biomaterial-assisted photoimmunotherapy for synergistic suppression of cancer progression; 19F MRI Nanotheranostics for Cancer Management: Progress and Prospects; The path linking disease severity and cognitive function with quality of life in Parkinson’s disease: the mediating effect of activities of daily living and depression; Manganese-Based Nanotheranostics for Magnetic Resonance Imaging-Mediated Precise Cancer Management; Predicting Alzheimer's disease based on survival data and longitudinally measured performance on cognitive and functional scales; Activities of daily living as a longitudinal moderator of the effect of autonomic dysfunction on anxiety and depression of Parkinson's patients; Screening and predicting progression from high-risk mild cognitive impairment to Alzheimer’s disease; Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features. Representative work: 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. We also sought to achieve multiclassification of NC, MCI, and AD. METHODS: We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, includ BACKGROUND AND OBJECTIVES: , cognition, daily activity ability, depression, and neuropsychiatric symptoms on transition probabilities. METHODS: We constructed a retrospective cohort by reviewing patients with an MCI diagnosis at study entry and at least 2 follow-up visits between June 2005 and February 2021. Defining NC or near-NC and MCI as transient states and dementia as an absorbing state, we used continuous-time multistate Markov models to estimate instantaneous transition intensity between states, transition probabilities from one state to anothe

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