Research

Longitudinal machine learning prediction of non-suicidal self-injury among Chinese adolescents: A prospective multicenter cohort study
Guo, X., Liu, S., Jiang, L., Xiong, Z., Wang, L., Lu, L., ... & Daniel T.L. Shek
Journal of Affective Disorders, 2025, 120110. [Q1, IF: 4.9]
code / paper

We introduce a progressive prediction framework utilizing four waves of longitudinal data and seven machine learning algorithms to predict NSSI risk among 3,483 Chinese adolescents.

Factors and pathways of non-suicidal self-injury in children: Insights from computational causal analysis
Guo, X., Wang, L., Li, Z., Feng, Z., Lu, L., Jiang, L., & Zhao, L.
Frontiers in Public Health, 2024, 12, 1305746. [Q1, IF: 3.4]
project / paper

Utilizing computational causal analysis to identify key factors and pathways contributing to non-suicidal self-injury behaviors in children, providing insights for intervention strategies.

CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction
Wang, L., Guo, X., Shi, H., Ma, Y., Bao, H., Jiang, L., Zhao, L., Feng, Z., Zhu, T., & Lu, L.
BMC Medical Informatics and Decision Making, 2025, 25(1), 165. [Q2, IF: 3.8]
code / paper

A novel deep learning framework that leverages causal relationships to improve mortality prediction in intensive care units, demonstrating superior performance over traditional approaches.

Life changes and symptoms of depression and anxiety among Chinese children and adolescents before, during, and after the COVID-19 pandemic lockdown: a combination of cross-sectional, longitudinal, and clustering studies
Zeng, Y., Song, J., Zhang, Y., Guo, X., Xu, X., Fan, L., Zhao, L., Song, H., & Jiang, L.
European Child & Adolescent Psychiatry, 2025, 34(3), 1025-1038. [Q1, IF: 4.9]
project / paper

A comprehensive analysis of mental health changes in Chinese youth during the COVID-19 pandemic, combining multiple study designs to understand the impact of lockdown measures on depression and anxiety symptoms.

Dynamic causal graph-based learning approach for predicting cognitive impairment in middle-aged and older adults
Wang, L., Guo, X., Zhou, Y., Li, Z., Jiang, L., Zhao, L., Feng, Z., & Lu, L.
In Proceedings of the 46th Annual Conference of the Cognitive Science Society, 2024, Vol. 46. [Conference]
paper

A dynamic causal graph-based machine learning approach for predicting cognitive impairment in aging populations, incorporating temporal relationships and causal mechanisms for improved accuracy.

Prediction of cognitive impairment in middle-aged and elderly people: A method based on Granger causality
Li, S., Wang, L., Guo, X., Shi, H., Ma, Y., Zhang, X., Feng, Z., & Lu, L.
In Proceedings of the 47th Annual Conference of the Cognitive Science Society, 2025, Vol. 47. [Conference]
paper

A novel approach utilizing Granger causality methods for predicting cognitive impairment in middle-aged and elderly populations, focusing on temporal causal relationships in cognitive decline.

Causality-Informed Models for Multidrug-Resistant Organism Prediction: Enabling Effective ICU Antibiotic Stewardship
Wang, L., Guo, X., Chen, Y., Li, S., Zhao, L., Feng, Z., & Lu, L.
International Journal of Antimicrobial Agents, 2026, 107767. [Q1, IF: 4.6]
code / paper

Development of causality-driven predictive models for multidrug-resistant organism (MDRO) infections in ICU settings, aimed at improving antibiotic stewardship programs and reducing healthcare-associated infections.

Academic values, academic anxiety, and non-suicidal self-injury in Chinese adolescents: A three-wave longitudinal study
Xinyu Guo, Peng, Y., Li, X., Zhao, L., Jiang, L., & Shek, D. T. L.
BMC Psychology, 2025. (Under review)

A longitudinal investigation examining the relationships between academic values, academic anxiety, and non-suicidal self-injury behaviors among Chinese adolescents across three time points.