arXiv subjects

Shurui Wu

Publications and source records attributed to Shurui Wu.

7 recordsLinked to original sources

arXiv2025

Multimodal Health Risk Prediction System for Chronic Diseases via Vision-Language Fusion and Large Language Models

With the rising global burden of chronic diseases and the multimodal and heterogeneous clinical data (medical imaging, free-text recordings, wearable sensor streams, etc.), there is an urgent need for a unified multimodal AI framework that can proactively predict individual health risks. We propose VL-RiskFormer, a hierarchical stacked visual-language multimodal Transformer with a large language model (LLM) inference head embedded in its top layer. The system builds on the dual-stream architecture of existing visual-linguistic models (e.g., PaLM-E, LLaVA) with four key innovations: (i) pre-training with cross-modal comparison and fine-grained alignment of radiological images, fundus maps, and wearable device photos with corresponding clinical narratives using momentum update encoders and debiased InfoNCE losses; (ii) a time fusion block that integrates irregular visit sequences into the causal Transformer decoder through adaptive time interval position coding; (iii) a disease ontology map adapter that injects ICD-10 codes into visual and textual channels in layers and infers comorbid patterns with the help of a graph attention mechanism. On the MIMIC-IV longitudinal cohort, VL-RiskFormer achieved an average AUROC of 0.90 with an expected calibration error of 2.7 percent.

cs.AI↗

arXiv2025

Research on Personalized Medical Intervention Strategy Generation System based on Group Relative Policy Optimization and Time-Series Data Fusion

With the timely formation of personalized intervention plans based on high-dimensional heterogeneous time series information becoming an important challenge in the medical field today, electronic medical records, wearables, and other multi-source medical data are increasingly generated and diversified. In this work, we develop a system to generate personalized medical intervention strategies based on Group Relative Policy Optimization (GRPO) and Time-Series Data Fusion. First, by incorporating relative policy constraints among the groups during policy gradient updates, we adaptively balance individual and group gains. To improve the robustness and interpretability of decision-making, a multi-layer neural network structure is employed to group-code patient characteristics. Second, for the rapid multi-modal fusion of multi-source heterogeneous time series, a multi-channel neural network combined with a self-attention mechanism is used for dynamic feature extraction. Key feature screening and aggregation are achieved through a differentiable gating network. Finally, a collaborative search process combining a genetic algorithm and Monte Carlo tree search is proposed to find the ideal intervention strategy, achieving global optimization. Experimental results show significant improvements in accuracy, coverage, and decision-making benefits compared with existing methods.

cs.AI↗

arXiv2025

Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method

As the prevalence of mental health crises increases on social media platforms, identifying and preventing potential harm has become an urgent challenge. This study introduces a large language model (LLM)-based text transfer recognition method for social network crisis intervention, enhanced with domain-specific mental health knowledge. We propose a multi-level framework that incorporates transfer learning using BERT, and integrates mental health knowledge, sentiment analysis, and behavior prediction techniques. The framework includes a crisis annotation tool trained on social media datasets from real-world events, enabling the model to detect nuanced emotional cues and identify psychological crises. Experimental results show that the proposed method outperforms traditional models in crisis detection accuracy and exhibits greater sensitivity to subtle emotional and contextual variations.

cs.CL↗

arXiv2023

Validity of black hole complementarity in the context of generalized uncertainty principle

Recently, Elias C. Vagenas et al and Yongwan Gim et al studied the validity of the no-cloning theorem in the context of generalized uncertainty principle (GUP), but they came to conflicting conclusions. With this in mind, we investigate the corrections to the temperature for Schwarzschild black hole in the context of different forms of GUP, and obtain the required energy to duplicate information for the Schwarzschild black hole, it shows that the required energy is greater than the mass of black hole, i.e. the no-cloning theorem in the present of GUP is safe.

gr-qc↗

arXiv2022

Thermodynamics of the Bardeen Black Hole with quintessence matter on the EGUP framework

In this paper, we have used the extended generalized uncertainty principle to investigate the thermodynamics of the Bardeen Black Hole with quintessence matter on the EGUP framework. We started with a brief perusal of the Extended generalized uncertainty principle. Subsequently, the EGUP-modified Hawking temperature, heat capacity, and entropy functions of the Bardeen Black Hole are obtained, which show that the modified uncertainty principles and normalization factor restrict the lower bound value of horizon radius to affect the Hawking temperature. Moreover, we compare and discuss the findings within the generalized uncertainty principle (GUP) and extended uncertainty principle (EUP). In addition, we examine the equation of state associated with the pressure and the volume.

gr-qc↗

arXiv2021

Ringing of a black hole in a dark matter halo

Recently, we obtained the simple metrics of a spherically symmetric black hole in a dark matter halo, and extended to the case of rotation. As the characteristic sound of black holes, quasinormal modes (QNMs) are one of the important means to understand black holes currently. Based on these two metrics of a spherically symmetric black hole, we study the QNMs of cold dark matter (CDM) and scalar field dark matter (SFDM) models using the methods of the material field perturbations and the gravitational perturbation, and make comparisons with the Schwarzschild black hole. Our results show that black hole QNMs of CDM and SFDM in a dark matter halo are different from the Schwarzschild black hole, unlike a Schwarzschild black hole with a prominent power-law tail. The different kinds models of dark matter can be distinguished by their QNMs. The time of QNMs ringing and frequencies increase with increasing parameter $l$. The overall QNMs of CDM are stronger than that of SFDM in the same condition, which is easier to be detected. In addition, QNMs frequencies using the sixth-order WKB method and the Prony method are in good agreement.

gr-qc↗

arXiv2021

Echoes of novel black-bounce spacetimes

Gravitational wave echoes can be used as a significant observable to understand the properties of black holes horizon. In addition, echoes would also closely relate to the unique properties of compact objects. In this work we study the evolution of electromagnetic field and scalar field under the background of novel black-bounce spacetimes. Our results show an obvious echoes signal that can characterize the properties of novel black-bounce spacetimes, and a detailed analysis about the characteristics of the echoes signal is given. By studying the quasinormal ringdown of the three states of novel black-bounce spacetimes, including black holes in $0 2 M$, we find that the echoes signal only appears when $a>2M$ in this spacetime, but when the parameter $a$ increases to a threshold, the echoes signal will be transformed into a quasinormal ringdown of the two-way traversable wormhole.

gr-qc↗