IEEE Journal of Biomedical and Health Informatics最新文献

Adaptive Compressed-based Privacy-preserving Large Language Model for Sensitive Healthcare. 基于自适应压缩的敏感医疗保健隐私保护大语言模型。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-08 DOI: 10.1109/JBHI.2025.3558935

Xinrong Gong, Jiaran Gao, Song Sun, Zhijie Zhong, Yifan Shi, Huanqiang Zeng, Kaixiang Yang

{"title":"Adaptive Compressed-based Privacy-preserving Large Language Model for Sensitive Healthcare.","authors":"Xinrong Gong, Jiaran Gao, Song Sun, Zhijie Zhong, Yifan Shi, Huanqiang Zeng, Kaixiang Yang","doi":"10.1109/JBHI.2025.3558935","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558935","url":null,"abstract":"The emergence of large language models (LLMs) has been a key enabler of technological innovation in healthcare. People can conveniently obtain a more accurate medical consultation service by utilizing LLMs' powerful knowledge inference capability. However, existing LLMs require users to upload explicit requests during remote healthcare consultations, which involves the risk of exposing personal privacy. Furthermore, the reliability of the response content generated by LLMs is not guaranteed. To tackle the above challenges, this paper proposes a novel privacy-preserving LLM for user-activated health, called Adaptive Compressed-based Privacy-preserving LLM (ACP2LLM). Specifically, an adaptive token compression method based on information entropy is carefully designed to ensure that ACP2LLM can preserve user-sensitive information when invoking the medical consultation of LLMs deployed on the cloud platform. Moreover, a multi-doctor one-chief physician mechanism is proposed to rationally split and collaboratively infer the patients' requests to achieve the privacy-utility trade-off. Notably, the proposed ACP2LLM also provides highly competitive performance in various token compression rates. Extensive experiments on multiple Medical Question and Answers datasets demonstrate that the proposed ACP2LLM has strong privacy protection capabilities and high answer precision, outperforming current state-of-the-art LLM methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information. 基于上下文信息的自适应运动意象解码框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-08 DOI: 10.1109/JBHI.2025.3557499

Zikai Wang, Ang Li, Zhenyu Wang, Ting Zhou, Tianheng Xu, Honglin Hu

{"title":"BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information.","authors":"Zikai Wang, Ang Li, Zhenyu Wang, Ting Zhou, Tianheng Xu, Honglin Hu","doi":"10.1109/JBHI.2025.3557499","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3557499","url":null,"abstract":"In motor imagery (MI) decoding, it still remains challenging to excavate enough contextual information of MI in different brain regions and to bridge the cross-session variance in feature distributions. In light of these issues, our study presents an innovative Bi-Stream Adaptation Network (BSAN) to bolster network efficacy, aiming to improve MI-based brain-computer interface (BCI) robustness across sessions. Our framework consists of the Bi-attention module, feature extractor, classifier, and Bi-discriminator. Precisely, we devise the Bi-attention module to reveal granular context information of MI with performing multi-scale convolutions asymptotically. Then, after features extraction, Bi-discriminator is involved to align the features from different MI sessions such that a uniform and accurate representation of neural patterns is achieved. By such a workflow, the proposed BSAN allows for the effective fusion of context coherence and session-invariance within the network architecture, therefore diminishing the reliance of redundant MI trials for MI-BCI re-calibration. To empirically substantiate BSAN, comprehensive experiments are conducted based on two public MI datasets. With average accuracies of 78.97% and 83.79% on two public datasets, and an inference time of 2.99 ms on CPU-only devices, it is believed that our approach has the potential to accelerate the practical deployment of MI-BCI.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Illuminating The Path To Enhanced Resilience Of Machine Learning Models Against The Shadows Of Missing Labels. 照亮增强机器学习模型抵御缺失标签阴影的弹性之路。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-08 DOI: 10.1109/JBHI.2025.3558846

Simankov Nikolay, Tahzima Rachid, Massart Sebastien, Soyeurt Helene

{"title":"Illuminating The Path To Enhanced Resilience Of Machine Learning Models Against The Shadows Of Missing Labels.","authors":"Simankov Nikolay, Tahzima Rachid, Massart Sebastien, Soyeurt Helene","doi":"10.1109/JBHI.2025.3558846","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558846","url":null,"abstract":"The sensitivity of state-of-the-art supervised classification models is compromised by contamination-prone biomedical datasets, which are vulnerable to the presence of missing or erroneous labels (i.e., inliers). Starting from codon frequencies, electrocardiogram signals, biomarkers, morphological features, and patient questionnaires, we attempted to cover a wide range of typical biomedical databases exposed to the risk of missing data labeled as negative values (inlier contamination). In some very niche fields, such as image recognition, missing labels have received a lot of attention, but in biomedical and clinical research, where outliers are almost systematically filtered, inliers have remained orphans. Our study introduced a pragmatic and innovative automated methodology that consists of upcycling one-class semi-supervised anomaly detection (OCSSAD) models for filtering potential inliers in training datasets. Five OCSSAD and two ensemble methods were benchmarked on 6 databases with 10 different contamination levels and 10 random samples, achieving an average Matthews correlation coefficient (MCC) of 78$pm$17% in validation, whereas 22 supervised classifiers achieved an average MCC score of 81$pm$9% trained with the complete and uncontaminated trainset.Therefore, by filtering the training set with an isolation forest, the average resilience to inliers of 22 tested Machine Learning models increased from 69$pm$11% to 95$pm$1%, including neural networks and gradient-boosting methods. Taken together, our study showcased the efficacy of our versatile approach in enhancing the resilience of Machine Learning models and highlighted the importance of accurately addressing the inliers challenge in the domains of medical and Life Sciences.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Herbal Medicine-Drug Interaction Prediction Using Large Language Models. 利用大语言模型增强草药药物相互作用预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-07 DOI: 10.1109/JBHI.2025.3558667

Sisi Yuan, Zhecheng Zhou, Xinyuan Jin, Linlin Zhuo, Keqin Li

{"title":"Enhancing Herbal Medicine-Drug Interaction Prediction Using Large Language Models.","authors":"Sisi Yuan, Zhecheng Zhou, Xinyuan Jin, Linlin Zhuo, Keqin Li","doi":"10.1109/JBHI.2025.3558667","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558667","url":null,"abstract":"Investigating potential interactions between drugs and herbal medicines helps optimize combined treatment strategies and supports personalized and precision medicine. Deep learning-based methods have been successful in predicting drug-related interactions. However, these methods face challenges such as low data quality and uneven distribution. Large language models (LLMs) effectively address these challenges through their extensive knowledge bases. Motivated by this, we integrate LLMs, one-hot encoding, and variational graph autoencoders (VGAEs) to propose a herbal medicine-drug interaction (HDI) prediction model. First, LLMs are employed to extract features from drug SMILES, generating high-quality molecular representations. Second, one-hot encoding is applied to herbal medicines with multiple natural products to construct feature vectors and improve model interpretability. Finally, VGAEs are utilized to reconstruct herbal medicine-drug graphs and predict unknown HDIs. Additionally, we differentiate between herbal medicine-drug similarity and the degree of individual drug or herbal medicine nodes to mitigate the dominance of high-degree nodes in VGAE message flow. Multiple experiments were conducted to validate the significance of the proposed model and its key components. This method shows great potential for applications in traditional Chinese medicine formulation optimization, new drug development, and precision medicine. Our code and data are accessible at: https://github.com/sisyyuan/HDI.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Progressive Mining and Dynamic Distillation of Hierarchical Prototypes for Disease Classification and Localisation. 用于疾病分类和定位的分层原型的渐进挖掘和动态提炼。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-07 DOI: 10.1109/JBHI.2025.3558508

Chong Wang, Fengbei Liu, Yuanhong Chen, Chun Fung Kwok, Michael Elliott, Carlos Pena-Solorzano, Davis James McCarthy, Helen Frazer, Gustavo Carneiro

{"title":"Progressive Mining and Dynamic Distillation of Hierarchical Prototypes for Disease Classification and Localisation.","authors":"Chong Wang, Fengbei Liu, Yuanhong Chen, Chun Fung Kwok, Michael Elliott, Carlos Pena-Solorzano, Davis James McCarthy, Helen Frazer, Gustavo Carneiro","doi":"10.1109/JBHI.2025.3558508","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3558508","url":null,"abstract":"Constructing effective representation of lesions is essential for disease classification and localization in medical image analysis. Prototype-based models address this by leveraging visual prototypes to capture representative lesion patterns, yet effectively handling the complexity of diverse lesion characteristics remains a critical challenge, as they typically rely on single-level, fixedsize prototypes and suffer from prototype redundancy. In this paper, we present HierProtoPNet, a new prototypebased framework designed to handle the complexity of lesions in medical images. HierProtoPNet leverages hierarchical visual prototypes across different semantic feature granularities to effectively capture diverse lesion patterns. To prevent redundancy and increase utility of the prototypes, we devise a novel prototype mining paradigm to progressively discover semantically distinct prototypes, offering multi-level complementary analysis of lesions. Also, we introduce a dynamic knowledge distillation strategy that allows transferring essential classification information across hierarchical levels, thereby improving generalisation performance. Comprehensive experiments show that HierProtoPNet achieves state-of-the-art classification performances in three benchmarks: binary breast cancer screening, multi-class retinal disease diagnosis, and multilabel chest X-ray classification. Quantitative assessments also illustrate HierProtoPNet's significant advantages in weakly-supervised disease localisation and segmentation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-Based Prediction of miRNA-Drug Associations with Multisource Information and Metapath Enhancement Matrices. 基于多源信息和元路径增强矩阵的mirna -药物关联图预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-04 DOI: 10.1109/JBHI.2025.3558303

Ming-Yang Wu, Peng-Wei Hu, Zhu-Hong You, Jun Zhang, Lun Hu, Xin Luo

{"title":"Graph-Based Prediction of miRNA-Drug Associations with Multisource Information and Metapath Enhancement Matrices.","authors":"Ming-Yang Wu, Peng-Wei Hu, Zhu-Hong You, Jun Zhang, Lun Hu, Xin Luo","doi":"10.1109/JBHI.2025.3558303","DOIUrl":"10.1109/JBHI.2025.3558303","url":null,"abstract":"Recent studies have demonstrated that miRNA expression dysregulation is closely related to the occurrence of various diseases; thus, miRNA-based drug development strategies have received increasing research interest. Most existing computational methods focus on the attribute information of individual nodes and are limited to the direct associations between nodes, thereby ignoring the complex associations inherent in the network. This limitation may lead to the loss of key potential information, which impacts the prediction accuracy. To address these issues, we propose a multisource information fusion and metapath enhancement matrix based graph autoencoder (MSMP-GAE) to predict the potential associations between miRNAs and drugs. The proposed MSMP-GAE model comprises a metapath instance extraction module, a metapath feature-enhanced encoder module, a weighted feature fusion module, and a graph autoencoder. First, we construct an miRNA-drug heterogeneous network using experimentally validated miRNA-drug interactions and integrate various miRNA and drug features into an initial feature matrix to comprehensively represent their intrinsic property information. Then, we extract metapath instances from the interaction network, generate multiple metapath enhancement matrices, and fuse them with the initial feature matrix to generate high-quality node feature embeddings. Finally, we employ the graph autoencoder for fivefold cross-validation on a public dataset and test it on an independent test set. Experimental results demonstrate that the proposed MSMP-GAE model obtained an area under the curve (AUC) and AUPR values of 98.61% and 98.23%, respectively, which is considerably better than the several state-of-the-art methods. This highlights the importance of the higher-order complex associations between nodes in the miRNA-drug association (MDA) prediction task and provides a new method and approach to advance MDA prediction.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Journal of Biomedical and Health Informatics Information for Authors IEEE生物医学与健康信息学杂志作者信息

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-04 DOI: 10.1109/JBHI.2025.3551179

引用次数: 0

Guest Editorial: Artificial Intelligence and Internet of Medical Things (AI IoMT) 嘉宾评论：人工智能与医疗物联网（AI IoMT）

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-04 DOI: 10.1109/JBHI.2025.3547248

Gwanggil Jeon;Abdellah Chehri;Xiaochun Cheng;Giancarlo Fortino

引用次数: 0

IEEE Journal of Biomedical and Health Informatics Publication Information IEEE生物医学与健康信息学杂志出版信息

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-04 DOI: 10.1109/JBHI.2025.3551181

引用次数: 0

FGLFA: A Federated Graph Learning-based Cross-Network Layer Feature Alignment Model for Major Depressive Disorder Identification. 基于联邦图学习的抑郁症识别跨网络层特征对齐模型。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-02 DOI: 10.1109/JBHI.2025.3557094

Zhuqing Jiao, Xinshun Ding, Zhengwang Xia, Chun Liu, Yudong Zhang

{"title":"FGLFA: A Federated Graph Learning-based Cross-Network Layer Feature Alignment Model for Major Depressive Disorder Identification.","authors":"Zhuqing Jiao, Xinshun Ding, Zhengwang Xia, Chun Liu, Yudong Zhang","doi":"10.1109/JBHI.2025.3557094","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3557094","url":null,"abstract":"It is a challenge to centralize medical datasets due to privacy, security, and storage issues for major depressive disorder (MDD). Federated Learning offers a solution for collaborative training without centralized storage. Nonetheless, it often overlooks the issue of data heterogeneity across sites. We propose a Federated Graph Learning-based Cross-Network Layer Feature Alignment (FGLFA) model for MDD identification. Specifically, it trains and tests Graph Sampling and Aggregation (GraphSAGE) networks separately for each site to extract graph-structured features from site-specific data. The GraphSAGE network integrates the residual connections (RCs), thereby mitigating gradient vanishing during model training and accelerating convergence speed. The feature alignment module aligns cross-network layer features across sites to minimize the discrepancies in feature distributions between sites. The experimental results show that the FGLFA model achieves an average ACC of 65.1% and F1-score of 70.9% across three sites. This demonstrates consistent advantages over mainstream federated paradigms, reducing variance by 23%. The proposed method improves the accuracy of MDD identification, and provides a more efficient tool for early diagnosis and treatment of brain diseases.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0