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Xiaoying Wu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

JBHI Journal 2025 Journal Article

DCLA: Deep Cooperative Learning for Advancing Automated Annotation of Electronic Medical Records in Cerebral Palsy

Meirong Xiao
Qiaofang Pang
Xiyuan Yang
Yuxia Chen
Xiaoying Wu
Min Zhong
Nong Xiao
Wensheng Hou

Automated annotation of electronic medical records for patients with cerebral palsy (CP) is crucial for downstream clinical applications. However, most existing methods lack mechanisms to verify model predictions before their acceptance and suffer from labeled data scarcity. To address this challenge, we propose a Deep Cooperative Learning for Automated Annotation (DCLA) framework. DCLA integrates named entity recognition (NER) and relation extraction (RE) models that employ the multi-head attention mechanism and the global pointer to handle complex entities and relations. Building on this foundation, a cooperative learning (CL) mechanism is introduced to evaluate prediction quality through score matrices for sample ranking and selection. Low-quality predictions are verified by annotators, while high-quality predictions are accepted automatically, enabling iterative retraining with cooperatively labeled data. Experiments on a CP-specific corpus demonstrate that DCLA's NER and RE models outperform state-of-the-art methods, while the CL mechanism enhances proofreading efficiency. Overall, DCLA enhances proofreading efficiency, mitigates data scarcity, and supports continuous model refinement.

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AIIM Journal 2024 Journal Article

Walking representation and simulation based on multi-source image fusion and multi-agent reinforcement learning for gait rehabilitation

Yean Zhu
Meirong Xiao
Dan Robbins
Xiaoying Wu
Wei Lu
Wensheng Hou

In the formulation of strategies for walking rehabilitation, achieving precise identification of the current state and making rational predictions about the future state are crucial but often unrealized. To tackle this challenge, our study introduces a unified framework that integrates a novel 3D walking motion capture method using multi-source image fusion and a walking rehabilitation simulation approach based on multi-agent reinforcement learning. We found that, (i) the proposal achieved an accurate 3D walking motion capture and outperforms other advanced methods. Experimental evidence indicates that, compared to similar visual skeleton tracking methods, the proposed approach yields results with higher Pearson correlation ( r = 0. 93 ), intra-class correlation coefficient ( I C C ( 2, 1 ) = 0. 91 ), and narrower confidence intervals ( [ 0. 90, 0. 95 ] for r, [ 0. 88, 0. 94 ] for I C C ( 2, 1 ) ) when compared to standard results. The outcomes of the proposed approach also exhibit commendable correlation and concurrence with those obtained through the IMU-based skeleton tracking method in the assessment of gait parameters ( [ 0. 85, 0. 89 ] for r, [ 0. 75, 0. 81 ] for I C C ( 2, 1 ) ); (ii) multi-agent reinforcement learning has the potential to be used to solve the simulation task of gait rehabilitation. In mimicry experiment, our proposed simulation method for gait rehabilitation not only enables the intelligent agent to converge from the initial state to the target state, but also observes evolutionary patterns similar to those observed in clinical practice through motor state resolution. This study offers valuable contributions to walking rehabilitation, enabling precise assessment and simulation-based interventions, with potential implications for clinical practice and patient outcomes.

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AAAI Conference 2021 Conference Paper

Facility’s Perspective to Fair Facility Location Problems

Chenhao Wang
Xiaoying Wu
Minming Li
Hau Chan

We study the problem faced by a decision maker who wants to locate a set of facilities on a real line and allocate agents/items to the facilities. The items have given locations on the line, and can only be assigned to one of their closest facilities. The facilities are controlled by managers, who have additive utility over the items. An optimal solution that maximizes the (utilitarian or egalitarian) social welfare of the facilities may present a very unbalanced allocation of the items to the facilities and hence be perceived as unfair. In this paper, we are interested in fair allocation among facility managers and consider the well-studied proportionality and envy-freeness fairness notions and their relaxations. We assess the availability, existence, approximability, and the quality (price of fairness) of fair solutions, where the quality measures the system efficiency loss under a fair allocation compared to the one that maximizes the social welfare. further, we show that one can find a Pareto-optimal solution in polynomial time.

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TCS Journal 2020 Journal Article

The efficiency of Nash equilibria in the load balancing game with a randomizing scheduler

Xujin Chen
Xiaodong Hu
Chenhao Wang
Xiaoying Wu

We study the efficiency of Nash equilibria for the load balancing game with a randomizing scheduler. In the game, we are given a set of facilities and a set of players along with a scheduler, where each facility is associated with a linear cost function, and the players are randomly ordered by the scheduler. Each player chooses exactly one of these facilities to fulfill his task, which incurs to him a cost depending on not only the cost function of the facility he chooses and the players who choose the same facility (as in a usual load balancing game), but also his uncertain position in the uniform random ordering. From an individual perspective, each player tries to choose a facility for optimizing his own objective that is determined by a certain decision-making principle. From a system perspective, it is desirable to minimize the maximum cost among all players, which is a commonly used criterion for load balancing. We estimate the price of anarchy and price of stability for this class of load balancing games under uncertainty, provided all players follow one of the four decision-making principles, namely the bottom-out, win-or-go-home, minimum-expected-cost, and minimax-regret principles. Our results show that the efficiency loss of Nash equilibria in these decentralized environments heavily rely on player's attitude toward the uncertainty.

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YNIMG Journal 2008 Journal Article

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

Yong Fan
Susan M. Resnick
Xiaoying Wu
Christos Davatzikos

This work builds upon previous studies that reported high sensitivity and specificity in classifying individuals with mild cognitive impairment (MCI), which is often a prodromal phase of Alzheimer's disease (AD), via pattern classification of MRI scans. The current study integrates MRI and PET 15O water scans from 30 participants in the Baltimore Longitudinal Study of Aging, and tests the hypothesis that joint evaluation of structure and function can yield higher classification accuracy than either alone. Classification rates of up to 100% accuracy were achieved via leave-one-out cross-validation, whereas conservative estimates of generalization performance in new scans, evaluated via bagging cross-validation, yielded an area under the receiver operating characteristic (ROC) curve equal to 0. 978 (97. 8%), indicating excellent diagnostic accuracy. Spatial maps of regions determined to contribute the most to the classification implicated many temporal, prefrontal, orbitofrontal, and parietal regions. Detecting complex patterns of brain abnormality in early stages of cognitive impairment has pivotal importance for the detection and management of AD.

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