Author

Young-Je Sim

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Review Open access Sep 2026

Interpretable machine learning for predicting moderate-to-severe insomnia risk in Chinese adults with autism spectrum disorder

Objective To develop a risk prediction model for moderate-to-severe insomnia among adults with autism spectrum disorder (ASD) and to identify key predictive features using interpretable machine learning methods, to support exploratory risk estimation and stratification. Methods This study used data from the 2024 Psycho...

Zhen-Hao Lin, Yuwen Shangguan, Young-Je Sim et al. · Frontiers in Psychiatry · 0 citations

Open access Sep 2026

Activity-function transitions and interpretable machine learning for predicting incident depressive symptoms among ACE-exposed middle-aged and older adults: a multi-cohort study

Background Ad--verse childhood experiences (ACEs) are associated with increased risk of depressive symptoms in later life. This study aimed to develop and externally validate an interpretable machine-learning model for predicting incident depressive symptoms among ACE-exposed middle-aged and older adults. Methods Data...

Zhen-Hao Lin, Yuwen Shangguan, Young-Je Sim et al. · Frontiers in Public Health · 0 citations

#explainable ai Open access Sep 2026

Predictive value of physical activity and walking time for dual cognitive-physical decline: a multinational cohort study using temporal deep learning and explainable AI

PA was associated with lower risk and more favorable transition patterns, although transitions to less impaired states should not be interpreted as definitive functional recovery, and Multistate Markov analyses suggested dynamic cognitive-physical state transitions.

Zhen-Hao Lin, Hao-Nan Liu, Young-Je Sim et al. · European Review of Aging and... · 0 citations