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Zhixiao Wang

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6 papers

2 author rows

AIIM Journal 2026 Journal Article

Hierarchical classification for differential diagnosis of fever of unknown origin: A multi-task learning approach with self-adaptive representation sharing

Zhixiao Wang
Yu Tian
Jian Liu
Tianshu Zhou
Yunqing Qiu
Jingsong Li

Leveraging label dependencies as prior knowledge during both training and testing has proven valuable across diverse domains such as image annotation and text categorization. In our previous research, we successfully reframed the clinical challenge of aiding decision-making for patients with fever of unknown origin (FUO) as a hierarchical classification problem, validating its feasibility through local methods. However, these approaches still encounter challenges, including high training costs and potential error propagation during predictions. Moreover, existing global approaches for exploiting label dependencies impose strict prerequisites—such as fixed data modalities, manual specification of information-sharing directions, and equal-length label sequences—that limit their applicability to FUO etiologies. In this paper, we introduce a novel global hierarchical classification method based on a multi-task learning architecture for the early diagnosis of FUO patients. Our method leverages multimodal clinical data and a predefined label hierarchy and comprises three key components: a task decomposition strategy employing End-of-Sequence (EOS) markers (with each parent node in the label hierarchy corresponding to an individual classification task), a multimodal data feature extraction and fusion module, and a self-adaptive representation sharing module (Sa-RSM). We evaluated our approach on an experimental dataset extracted from electronic health records (EHRs) of a large-scale tertiary hospital in China, spanning January 2011 to October 2020 and comprising 34, 051 hospital admissions of 30, 794 FUO patients. Our results clearly demonstrate that the proposed method not only achieves superior predictive performance but also proactively halts predictions at coarser-grained classification tasks. Moreover, even in cases of misclassification, our method exhibits lower mistake severity, underscoring its potential clinical utility.

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JBHI Journal 2024 Journal Article

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

Qianghua Liu
Yu Tian
Tianshu Zhou
Kewei Lyu
Zhixiao Wang
Yixiao Zheng
Ying Liu
Jingjing Ren

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients’ electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0. 7873, recall@10 of 0. 9020 and hits@10 of 0. 9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

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JBHI Journal 2023 Journal Article

Integrating Medical Domain Knowledge for Early Diagnosis of Fever of Unknown Origin: An Interpretable Hierarchical Multimodal Neural Network Approach

Zhixiao Wang
Jian Liu
Yu Tian
Tianshu Zhou
Qianghua Liu
Yunqing Qiu
Jingsong Li

Accurate and interpretable differential diagnostic technologies are crucial for supporting clinicians in decision-making and treatment-planning for patients with fever of unknown origin (FUO). Existing solutions commonly address the diagnosis of FUO by transforming it into a multi-classification task. However, after the emergence of COVID-19 pandemic, clinicians have recognized the heightened significance of early diagnosis in patients with FUO, particularly for practical needs such as early triage. This has resulted in increased demands for identifying a wider range of etiologies, shorter observation windows, and better model interpretability. In this article, we propose an interpretable hierarchical multimodal neural network framework (iHMNNF) to facilitate early diagnosis of FUO by incorporating medical domain knowledge and leveraging multimodal clinical data. The iHMNNF comprises a top-down hierarchical reasoning framework (Td-HRF) built on the class hierarchy of FUO etiologies, five local attention-based multimodal neural networks (La-MNNs) trained for each parent node of the class hierarchy, and an interpretable module based on layer-wise relevance propagation (LRP) and attention mechanism. Experimental datasets were collected from electronic health records (EHRs) at a large-scale tertiary grade-A hospital in China, comprising 34, 051 hospital admissions of 30, 794 FUO patients from January 2011 to October 2020. Our proposed La-MNNs achieved area under the receiver operating characteristic curve (AUROC) values ranging from 0. 7809 to 0. 9035 across all five decomposed tasks, surpassing competing machine learning (ML) and single-modality deep learning (DL) methods while also providing enhanced interpretability. Furthermore, we explored the feasibility of identifying FUO etiologies using only the first N -hour time series data obtained after admission.

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NeurIPS Conference 2023 Conference Paper

Scalable Fair Influence Maximization

Xiaobin Rui
Zhixiao Wang
Jiayu Zhao
Lichao Sun
Wei Chen

Given a graph $G$, a community structure $\mathcal{C}$, and a budget $k$, the fair influence maximization problem aims to select a seed set $S$ ($|S|\leq k$) that maximizes the influence spread while narrowing the influence gap between different communities. While various fairness notions exist, the welfare fairness notion, which balances fairness level and influence spread, has shown promising effectiveness. However, the lack of efficient algorithms for optimizing the welfare fairness objective function restricts its application to small-scale networks with only a few hundred nodes. In this paper, we adopt the objective function of welfare fairness to maximize the exponentially weighted summation over the influenced fraction of all communities. We first introduce an unbiased estimator for the fractional power of the arithmetic mean. Then, by adapting the reverse influence sampling (RIS) approach, we convert the optimization problem to a weighted maximum coverage problem. We also analyze the number of reverse reachable sets needed to approximate the fair influence at a high probability. Further, we present an efficient algorithm that guarantees $1-1/e - \varepsilon$ approximation.

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JBHI Journal 2021 Journal Article

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice

Yong Shang
Yu Tian
Min Zhou
Tianshu Zhou
Kewei Lyu
Zhixiao Wang
Ran Xin
Tingbo Liang

Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about information showing disease risks beyond their specialties, resulting in delayed and missed diagnoses or improper management. In this study, we introduced an electronic health record (EHR)-oriented knowledge graph system to efficiently utilize non-used information buried in EHRs. EHR data were transformed into a semantic patient-centralized information model under the ontology structure of a knowledge graph. The knowledge graph then creates an EHR data trajectory and performs reasoning through semantic rules to identify important clinical findings within EHR data. A graphical reasoning pathway illustrates the reasoning footage and explains the clinical significance for clinicians to better understand the neglected information. An application study was performed to evaluate unconsidered chronic kidney disease (CKD) reminding for non-nephrology clinicians to identify important neglected information. The study covered 71, 679 patients in non-nephrology departments. The system identified 2, 774 patients meeting CKD diagnosis criteria and 10, 377 patients requiring high attention. A follow-up study of 5, 439 patients showed that 82. 1% of patients who met the diagnosis criteria and 61. 4% of patients requiring high attention were confirmed to be CKD positive during follow-up research. The application demonstrated that the proposed approach is feasible and effective in clinical information utilization. Additionally, it's valuable as an explainable artificial intelligence to provide interpretable recommendations for specialist physicians to understand the importance of non-used data and make comprehensive decisions.

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ICRA Conference 1989 Conference Paper

Design and characterization of a linear motion piezoelectric microactuator

Zhixiao Wang
Musa K. Jouaneh
David A. Dornfeld

The design and characterization of a fast linear microactuator is described. The microactuator is constructed of number of piezoelectric plates that are placed on both sides of a sliding mass. Actuation of the plates in an accordionlike fashion provides a linear displacement of +or-0. 36 mm. In addition, it has force capability of over 80 g, and is provided with both position and force-sensing capabilities. Characterization of the dynamic performance of the microactuator is presented, and initial tests on it show that it has a second-order system behavior with nonlinear response. The resolution of the microactuator is affected by the performance of the capacitive-type position sensor, and varies from 3 mu m to less than 0. 6 mu m. >

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