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Xiaochen Wang

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10 papers

2 author rows

JBHI Journal 2025 Journal Article

Closed-Loop Respiratory Intervention Enhances Sleep Ventilation and Oxygen Saturation in Healthy Participants With Rapid High-Altitude Exposure

Yilin Yang
Wei Li
Hanyu Chen
Xiaochen Wang
Linhong Ji
Boda Zhou
Chong Li

Individuals who rapidly exposed to high-altitude environments are at risk of developing acute mountain sickness, which can inhibit the respiratory center or cause upper airway obstruction, leading to sleep apnea (SA). SA reduces oxygen saturation (SPO2) during sleep, which not only impairs sleep quality but affects cognitive and memory function. Positive airway pressure ventilation helps alleviate SA, but existing devices are prone to failure at high altitudes and are unable to realize real-time intervention based on user's physiological parameters. In this paper, we propose a respiratory ventilation system which addresses the issue of equipment failure at high altitudes through the implementation of an atmospheric pressure compensation algorithm. Additionally, we have developed a closed-loop algorithm that adjusts the inhalation and exhalation pressure based on the user's SPO2 during sleep. Experimental evaluations were conducted at an altitude of 3650 m, where participants were randomly assigned to receive closed-loop respiratory intervention, bi-level positive airway pressure (Bi-PAP) ventilation, and sham stimulation on three days. Heart rate (HR), SPO2, tidal volume (VT), respiratory rate (Rf) and sleep papameters were collected, and sleep quality was assessed. Experimental results showed that participants experienced an 26. 3% elevate in ventilation (p=0. 004, 0. 002, 0. 003, respectively), an 8% increase in SPO2 (p $< $ 0. 001 on three days), reduction in apnea events and an enhancement in deep sleep duration and sleep stability. These findings demonstrate that the incorporation of the closed-loop algorithm has significantly enhanced the system's effectiveness, offering a novel solution for addressing sleep apnea in high-altitude environments.

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AAAI Conference 2025 Short Paper

Developing Multimodal Healthcare Foundation Model: From Data-driven to Knowledge-enhanced

Xiaochen Wang

Foundation models in general domains have leveraged multimodal knowledge graphs to great effect, yet the healthcare sector lacks such comprehensive structures, presenting a significant gap in current research. Based on previous exploration with pure data-driven approaches, this proposal describes a two-stage project aiming to enhance multimodal healthcare foundation model with domain knowledge. The first stage is to construct a robust multimodal healthcare knowledge graph based on established healthcare taxonomies, such as UMLS, and enriched with data from multimodal clinical databases like MIMIC-CXR. This knowledge graph will incorporate medical images as cross-modal instances linked to healthcare terminologies, enhancing the depth and applicability of the graph. In the second stage, the knowledge graph will serve as a foundational tool in training healthcare foundation models with enhanced capabilities, particularly in reducing hallucination and managing concept ambiguity through the novel use of reinforcement learning techniques like Direct Preference Optimization (DPO). This research is expected to make significant contributions to the domain of healthcare AI by enabling more accurate, reliable, and explainable AI-driven diagnostics and interventions.

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NeurIPS Conference 2025 Conference Paper

Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials

Yifan Pu
Jixuan Ying
Qixiu Li
Tianzhu Ye
Dongchen Han
Xiaochen Wang
Ziyi Wang
shao xinyu

Vision Transformers (ViTs) have become a universal backbone for both image recognition and image generation. Yet their Multi–Head Self–Attention (MHSA) layer still performs a quadratic query–key interaction for \emph{every} token pair, spending the bulk of computation on visually weak or redundant correlations. We introduce \emph{Visual–Contrast Attention} (VCA), a drop-in replacement for MHSA that injects an explicit notion of discrimination while reducing the theoretical complexity from $\mathcal{O}(N^{2}C)$ to $\mathcal{O}(N n C)$ with $n\! \ll\! N$. VCA first distils each head’s dense query field into a handful of spatially pooled \emph{visual–contrast tokens}, then splits them into a learnable \emph{positive} and \emph{negative} stream whose differential interaction highlights what truly separates one region from another. The module adds fewer than $0. 3$\, M parameters to a DeiT-Tiny backbone, requires no extra FLOPs, and is wholly architecture-agnostic. Empirically, VCA lifts DeiT-Tiny top-1 accuracy on ImageNet-1K from $72. 2\%$ to \textbf{$75. 6\%$} (+$3. 4$) and improves three strong hierarchical ViTs by up to $3. 1$\%, while in class-conditional ImageNet generation it lowers FID-50K by $2. 1$ to $5. 2$ points across both diffusion (DiT) and flow (SiT) models. Extensive ablations confirm that (i) spatial pooling supplies low-variance global cues, (ii) dual positional embeddings are indispensable for contrastive reasoning, and (iii) combining the two in both stages yields the strongest synergy. VCA therefore offers a simple path towards faster and sharper Vision Transformers. The source code is available at \href{https: //github. com/LeapLabTHU/LinearDiff}{https: //github. com/LeapLabTHU/LinearDiff}.

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NeurIPS Conference 2024 Conference Paper

FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection

Jiaqi Wang
Xiaochen Wang
Lingjuan Lyu
Jinghui Chen
Fenglong Ma

This study introduces the Federated Medical Knowledge Injection (FedMEKI) platform, a new benchmark designed to address the unique challenges of integrating medical knowledge into foundation models under privacy constraints. By leveraging a cross-silo federated learning approach, FedMEKI circumvents the issues associated with centralized data collection, which is often prohibited under health regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the USA. The platform is meticulously designed to handle multi-site, multi-modal, and multi-task medical data, which includes 7 medical modalities, including images, signals, texts, laboratory test results, vital signs, input variables, and output variables. The curated dataset to validate FedMEKI covers 8 medical tasks, including 6 classification tasks (lung opacity detection, COVID-19 detection, electrocardiogram (ECG) abnormal detection, mortality prediction, sepsis protection, and enlarged cardiomediastinum detection) and 2 generation tasks (medical visual question answering (MedVQA) and ECG noise clarification). This comprehensive dataset is partitioned across several clients to facilitate the decentralized training process under 16 benchmark approaches. FedMEKI not only preserves data privacy but also enhances the capability of medical foundation models by allowing them to learn from a broader spectrum of medical knowledge without direct data exposure, thereby setting a new benchmark in the application of foundation models within the healthcare sector.

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AIIM Journal 2024 Journal Article

Medical knowledge graph completion via fusion of entity description and type information

Xiaochen Wang
Runtong Zhang
Butian Zhao
Yuhan Yao
Hongmei Zhao
Xiaomin Zhu

Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely solely on triplet structural information, overlooking the rich information hidden within entity property sets, leading to bottlenecks in performance enhancement when dealing with the intricate relations of MKGs. Inspired by the semantic sensitivity and explicit type constraints unique to the medical domain, we propose BioBERT-based graph embedding model. This model represents an evolvable framework that integrates graph embedding, language embedding, and type information, thereby optimizing the utility of MKGs. Our study utilizes not only WordNet as a benchmark dataset but also incorporates MedicalKG to compare and corroborate the specificity of medical knowledge. Experimental results on these datasets indicate that the proposed fusion framework achieves state-of-art (SOTA) performance compared to other baselines. We believe that this incremental improvement provides promising insights for future medical knowledge graph completion endeavors.

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IJCAI Conference 2024 Conference Paper

Recent Advances in Predictive Modeling with Electronic Health Records

Jiaqi Wang
Junyu Luo
Muchao Ye
Xiaochen Wang
Yuan Zhong
Aofei Chang
Guanjie Huang
Ziyi Yin

The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With the advancements in machine learning techniques, deep learning has demonstrated its superiority in various applications, including healthcare. This survey systematically reviews recent advances in deep learning-based predictive models using EHR data. Specifically, we introduce the background of EHR data and provide a mathematical definition of the predictive modeling task. We then categorize and summarize predictive deep models from multiple perspectives. Furthermore, we present benchmarks and toolkits relevant to predictive modeling in healthcare. Finally, we conclude this survey by discussing open challenges and suggesting promising directions for future research.

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ICML Conference 2020 Conference Paper

BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

Xiaochen Wang
Arash Pakbin
Bobak J. Mortazavi
Hongyu Zhao
Donald K. K. Lee

The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with the underlying readings. Survival analysis, in particular hazard estimation, is well-suited to analyzing this stream of data to predict disease onset as a function of the time-varying vitals. This paper introduces the software package BoXHED (pronounced ‘box-head’) for nonparametrically estimating hazard functions via gradient boosting. BoXHED 1. 0 is a novel tree-based implementation of the generic estimator proposed in Lee et al. (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner. BoXHED is also the first publicly available software implementation for Lee et al. (2017). Applying it to a cardiovascular disease dataset from the Framingham Heart Study reveals novel interaction effects among known risk factors, potentially resolving an open question in clinical literature. BoXHED is available from GitHub: www. github. com/BoXHED.

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ICRA Conference 2018 Conference Paper

Incorporate Oblique Muscle Contractions to Strengthen Soft Robots

Xiaochen Wang
Hossein Faraji
Yigit Mengüç

For the state-of-the-art of soft robotics, the current actuation mechanisms cannot produce shear forces, neither are the current stiffening mechanisms adaptive to various deformations. Consequently, the soft robots gain strength at the price of losing flexibility. To fill this gap, we proposed a new mechanism based on the muscle arrangements and incompressible property identified in biological hydrostatic skeletons. Beside longitudinal and transverse muscles, the proposed mechanism includes the oblique arrangement which is proved to play an indispensable role of producing shear forces. The effectiveness of the new mechanism is demonstrated through a benchmark problem - carrying a distributed load at the initial horizontal configuration, thus indicating an improved direction to realise shape-independent load-carrying capability of soft robotics. Furthermore, the proposed mechanism may explain how elephants coordinate the two contradicting properties, strength and flexibility, during their trunk manipulations.

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ICRA Conference 2017 Conference Paper

Introducing rotary force to a template model can explain human compliant slope walking

Xiaochen Wang
Tao Geng

Like level-ground walking, biological experiments have shown that humans largely maintain compliant leg behavior during slope walking, which greatly reduces the mechanical cost of transportation. Nowadays biped robots are becoming more and more functional on irregular terrains, yet no theoretical model can describe explicitly the cause of the characteristic ground reaction force (GRF) patterns observed in human slope walking. To fill the knowledge gap, this study extended Geyer's template biped level-ground walking model to explaining the slope walking GRF. By comparing the current Geyer's model with the human slope walking data, it was reasoned out that only using radial force from the legs could not account for the shifted anterior-posterior (AP) GRF without breaking the compliant leg behavior in the normal direction. With introducing the leg rotary force, the extended Geyer's model was then able to address the shifted AP GRF effectively. For legged robotics, this study primarily suggests that letting the leg rotary force collaborate with the compliant leg behaviour can improve the agility and energy-efficiency of dynamic walking on irregular terrains.

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IROS Conference 2014 Conference Paper

A new coefficient-adaptive orthonormal basis function model structure for identifying a class of pneumatic soft actuators

Xiaochen Wang
Tao Geng
Yahya Elsayed
Tommaso Ranzani
Chakravarthini M. Saaj
Constantina Lekakou

The class of Pneumatically-driven Lower-pressure Soft Actuators (PLSA) is a popular research topic as it can be potentially used in the surgical robotic applications. One fundamental problem lying in the PLSA research is the lack of a generally validated model for the complex nonlinear dynamic behaviours. In this paper, a new coefficient-adaptive orthonormal basis function model structure is specifically developed for the identification of the general PLSAs. It is a parameter-independent way directly used to identify the dynamic relation between the actuating pressures and the principal degrees of freedom of a PLSA, the bending and the steering. The approach is based on a modified auxiliary kinematic setting. Following the discussion of the identification procedure, the implementations for the double chamber bending and steering are demonstrated. The results show that the proposed approach can accurately capture the nonlinear pressure-shape dynamics. The approach is also efficient in the real-time applications. It can be further used to improve the current control design for the PLSAs in robotic applications.

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