Xiaoyu Lv, PH.D.

Research experiences:

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My research focuses on integrating multimodal neuroimaging data with machine learning techniques to develop suicide behavior prediction models and explore the neural mechanisms underlying suicidal intent and behavior from a brain function perspective. Additionally, I aim to create personalized suicide behavior prediction models based on existing clinical suicide data, with the goal of embedding suicide prevention into every household and effectively reducing the incidence of suicidal behavior. Regarding personalized treatment strategies, I published a study as the first author in Communications Biology, where I proposed a variational relevance evaluation algorithm framework. This framework successfully applied machine learning in non-big-data environments by combining variational methods based on small sample datasets, providing an important basis for future individualized suicide behavior prediction model development.