IEEE Journal of Biomedical and Health Informatics最新文献_第5页

Towards Artificial Intelligence-based Decision Support for Large-scale Screening for Atrial Fibrillation. 基于人工智能的房颤大规模筛查决策支持

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-13 DOI: 10.1109/JBHI.2025.3579621

Markus Lueken, Jannik Mettner, Nicolai Spicher, Michael Gramlich, Nikolaus Marx, Steffen Leonhardt, Matthias D Zink

{"title":"Towards Artificial Intelligence-based Decision Support for Large-scale Screening for Atrial Fibrillation.","authors":"Markus Lueken, Jannik Mettner, Nicolai Spicher, Michael Gramlich, Nikolaus Marx, Steffen Leonhardt, Matthias D Zink","doi":"10.1109/JBHI.2025.3579621","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3579621","url":null,"abstract":"Atrial fibrillation is a prevalent cardiac arrhythmia, significantly increasing the risk of stroke, heart failure, and mortality. Early detection, especially during asymptomatic and paroxysmal stages, is essential for effective intervention. This study explores the application of deep neural networks in simplified ECG screening to enhance population-wide detection of atrial fibrillation. A handheld device, MyDiagnostick, was employed for large-scale ECG data acquisition within a pharmacy-based clinical trial on 7295 subjects aged 65 years and older. Automated diagnosis yielded 6.08% of AF prevalence in the given dataset. The data were then analyzed using a validated deep neural network model for the detection of cardiac arrhythmia in 12-lead ECG data for feature extraction and detection of atrial fibrillation. In addition, we investigate the capabilities of explainable artificial intelligence to provide diagnostic support for cardiologists and assess the feasibility of implementing deep neural networks in wearable devices for continuous monitoring. The study also emphasizes the importance of interpretability in artificial intelligence models for medical applications, leveraging explainable artificial intelligence to highlight ECG segments indicative of atrial fibrillation. Our findings demonstrate the efficacy of deep neural networks in atrial fibrillation detection with an F1-score of 86% vs. 81% of the automated ECG stick analysis and the potential for their integration into wearable technology by successfully reducing the number of weights by 99% without significant loss of accuracy, providing a robust tool for early diagnosis and continuous monitoring of atrial fibrillation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289432","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

A Post-Quantum Blockchain and Autonomous AI-Enabled Scheme for Secure Healthcare Information Exchange. 用于安全医疗信息交换的后量子区块链和自主ai支持方案。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-13 DOI: 10.1109/JBHI.2025.3579722

Linlin He, Siyuan Rao, Kexin Tian, Yuyuan Liu, Jue Wang, Shuanggen Liu, Xiuhua Lu

{"title":"A Post-Quantum Blockchain and Autonomous AI-Enabled Scheme for Secure Healthcare Information Exchange.","authors":"Linlin He, Siyuan Rao, Kexin Tian, Yuyuan Liu, Jue Wang, Shuanggen Liu, Xiuhua Lu","doi":"10.1109/JBHI.2025.3579722","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3579722","url":null,"abstract":"Secure healthcare information exchange (HIE) is critical to improving medical services, enabling data interoperability, and ensuring patient privacy. However, the increasing threat posed by quantum computing challenges the reliability of conventional cryptographic mechanisms. To address this, we propose a post-quantum secure healthcare data-sharing scheme that combines the Extended Merkle Signature Scheme (XMSS) and consortium blockchain technology to guarantee the integrity, authenticity, and traceability of electronic medical records (EMRs). Furthermore, the scheme incorporates autonomous artificial intelligence (AI) to assist healthcare professionals in generating accurate and intelligent diagnostic reports, enhancing clinical decision-making. We theoretically analyze the scheme's security in the random oracle model, demonstrating that it effectively resists various threats. Performance evaluation shows that the scheme is particularly suitable for HIE scenarios as it reduces about 49% in total computational overheads and 36% in blockchain storage compared to other schemes.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289430","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

LWAH-Net: Light weight Attention-Driven Hybrid Network for Polyp Segmentation in Endoscopic Images. LWAH-Net：用于内镜图像息肉分割的轻量级注意驱动混合网络。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-12 DOI: 10.1109/JBHI.2025.3579348

Malik Abdul Manan, Jinchao Feng, Syed Muhammad Ali Imran, Shahzad Ahmad, Abdul Raheem

{"title":"LWAH-Net: Light weight Attention-Driven Hybrid Network for Polyp Segmentation in Endoscopic Images.","authors":"Malik Abdul Manan, Jinchao Feng, Syed Muhammad Ali Imran, Shahzad Ahmad, Abdul Raheem","doi":"10.1109/JBHI.2025.3579348","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3579348","url":null,"abstract":"Polyp segmentation is vital for the early detection and diagnosis of colorectal cancer, challenges such as variability in polyp morphology, low contrast, and imaging artifacts demand advanced segmentation solutions. LWAH-Net is a light-weight, attention-driven hybrid network combining CNN and transformer-based attention modules to effectively capture local and global contextual features. The architecture includes booster encoders for multiscale feature extraction, attention-based bottleneck for attentiondriven global feature modeling, transformer attention-based residual connection and a combined loss function employing Dice, Jaccard, and surface losses to enhance boundary accuracy. With only 0.82 million parameters, LWAH-Net achieved state-of-the-art performance across five datasets. It attains Dice scores ranging from 78.8% (ETIS dataset) to 93.8% (CVC-ClinicDB dataset) and mean Intersection over Union (mIoU) scores ranging from 70.4% to 90.1%, surpassing existing models in accuracy and computational efficiency. The model demonstrates excellent generalization on diverse datasets, highlighting its adaptability for clinical applications in resource-constrained environments. LWAH-Net is a robust and efficient tool that is a new addition for real-time diagnostic systems for polyp segmentation. https://github.com/manansandila/LWAH-Net.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144283744","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

DRLSurv: Disentangled Representation Learning for Cancer Survival Prediction by Mining Multimodal Consistency and Complementarity. DRLSurv：基于多模态一致性和互补性挖掘的癌症生存预测解纠缠表示学习。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-11 DOI: 10.1109/JBHI.2025.3578859

Ying Xu, Yi Shi, Honglei Liu, Ao Li, Anli Zhang, Minghui Wang

{"title":"DRLSurv: Disentangled Representation Learning for Cancer Survival Prediction by Mining Multimodal Consistency and Complementarity.","authors":"Ying Xu, Yi Shi, Honglei Liu, Ao Li, Anli Zhang, Minghui Wang","doi":"10.1109/JBHI.2025.3578859","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3578859","url":null,"abstract":"Accurate cancer survival prediction is crucial in devising optimal treatment plans and offering individualized care to improve clinical outcomes. Recent researches confirm that integrating heterogenous cancer data such as histopathological images and genomic data, can enhance our understanding of cancer progression and provides a multimodal perspective on patient survival chances. However, existing methods often over-look the fundamental aspects of multimodal data, i.e., consistency and complementarity, which in consequence significantly hinder advancements in cancer survival prediction. To address this issue, we represent DRLSurv, a novel multimodal deep learning method that leverages disentangled representation learning for precise cancer survival prediction. Through dedicated deep encoding networks, DRLSurv decomposes each modality into modality-invariant and modality-specific representations, which are mapped to common and unique feature subspaces for simultaneously mining the distinct aspects of cancer multimodal data. Moreover, our method innovatively introduces a subspace-based proximity contrastive loss and re-disentanglement loss, thus ensuring the successful decomposition of consistent and complementary information while maintaining the multimodal fidelity during the learning of disentangled representations. Both quantitative analyses and visual assessments on different datasets validate the superiority of DRLSurv over existing survival prediction approaches, demonstrating its powerful capability to exploit enriched survival-related information from cancer multimodal data. Therefore, DRLSurv not only offers a unified and comprehensive deep learning framework for advancing multimodal survival predictions, but also provides valuable insights for cancer prognosis and survival analysis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274661","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

Dynamic Instance-level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction. 颅内脑电图信号的动态实例级图学习网络用于癫痫发作预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-11 DOI: 10.1109/JBHI.2025.3578627

Qi Lian, Yueming Wang, Yu Qi

{"title":"Dynamic Instance-level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction.","authors":"Qi Lian, Yueming Wang, Yu Qi","doi":"10.1109/JBHI.2025.3578627","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3578627","url":null,"abstract":"Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN's capability to learn interpretable and diverse connections.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274662","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

A Multi-Resolution Hybrid CNN-Transformer Network With Scale-Guided Attention for Medical Image Segmentation. 基于尺度导向的多分辨率CNN-Transformer混合网络医学图像分割。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-11 DOI: 10.1109/JBHI.2025.3578625

Shujin Zhu, Yue Li, Xiubin Dai, Tianyi Mao, Lei Wei, Yidan Yan

{"title":"A Multi-Resolution Hybrid CNN-Transformer Network With Scale-Guided Attention for Medical Image Segmentation.","authors":"Shujin Zhu, Yue Li, Xiubin Dai, Tianyi Mao, Lei Wei, Yidan Yan","doi":"10.1109/JBHI.2025.3578625","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3578625","url":null,"abstract":"Medical image segmentation remains a challenging task due to the intricate nature of anatomical structures and the wide range of target sizes. In this paper, we propose a novel U -shaped segmentation network that integrates CNN and Transformer architectures to address these challenges. Specifically, our network architecture consists of three main components. In the encoder, we integrate an attention-guided multi-scale feature extraction module with a dual-path downsampling block to learn hierarchical features. The decoder employs an advanced feature aggregation and fusion module that effectively models inter-dependencies across different hierarchical levels. For the bottleneck, we explore multi-scale feature activation and multi-layer context Transformer modules to facilitate high-level semantic feature learning and global context modeling. Additionally, we implement a multi-resolution input-output strategy throughout the network to enrich feature representations and ensure fine-grained segmentation outputs across different scales. The experimental results on diverse multi-modal medical image datasets (ultrasound, gastrointestinal polyp, MR, and CT images) demonstrate that our approach can achieve superior performance over state-of-the-art methods in both quantitative measurements and qualitative assessments. The code is available at https://github.com/zsj0577/MSAGHNet.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274660","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

Noise-Aware Epileptic Seizure Prediction Network via Self-Attention Feature Alignment. 基于自注意特征对齐的噪声感知癫痫发作预测网络。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-11 DOI: 10.1109/JBHI.2025.3579229

Qiulei Dong, Zhixi Wang, Mengyu Gao

{"title":"Noise-Aware Epileptic Seizure Prediction Network via Self-Attention Feature Alignment.","authors":"Qiulei Dong, Zhixi Wang, Mengyu Gao","doi":"10.1109/JBHI.2025.3579229","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3579229","url":null,"abstract":"Recently, deep neural networks have been extensively used to extract features from EEG data for epileptic seizure prediction in the epilepsy diagnosis community. Many existing works in literature either use the ultimate-layer feature or aggregate multi-layer features via straightforward concatenation or element-wise addition, but they do not pay a special attention to the contextual consistency between these features as well as the involved noise in these features. To address the above problem, we propose a Noise-aware epileptic seizure prediction network via Self-attention Feature Alignment, called NSFA-Net. The NSFA-Net consists of two modules: a self-attention backbone module to extract multi-layer features from the input EEG data, and a time-frequency feature alignment module to align these features for maintaining the contextual consistency. In addition, during the training process, a noise-aware regularizer is introduced to alleviate the negative influence of noise that is generally inevitable in EEG data. The average sensitivities of the proposed method on the CHB-MIT and Kaggle datasets are 98.68% and 93.57% respectively, and the average false prediction rates are 0.038/h and 0.060/h respectively. These experimental results show the superiority of the proposed method to some state-of-the-art methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274663","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

SkipDAEformer: A High-Precision Representation Learning Method for Removing Random Mixed Noise in MCG Signals. SkipDAEformer：一种去除MCG信号随机混合噪声的高精度表示学习方法。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-11 DOI: 10.1109/JBHI.2025.3579060

Ruizhe Wang, Zhanyi Liu, Jiaojiao Pang, Jie Sun, Min Xiang, Xiaolin Ning

{"title":"SkipDAEformer: A High-Precision Representation Learning Method for Removing Random Mixed Noise in MCG Signals.","authors":"Ruizhe Wang, Zhanyi Liu, Jiaojiao Pang, Jie Sun, Min Xiang, Xiaolin Ning","doi":"10.1109/JBHI.2025.3579060","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3579060","url":null,"abstract":"Automated analytical techniques for magnetocardiography (MCG) are essential for diagnosing and predicting cardiovascular diseases. Clinically acquired MCG signals are often contaminated by various types of noise, which negatively impact subsequent signal analysis. However, traditional methods have limitations in denoising long-term MCG signals with complex spatial structures. We propose a high-precision, robust representation learning method based on skip connection multi-scale feature fusion (SkipDAEformer) for effectively removing random mixed noise in MCG signals. SkipDAEformer integrates attention fusion mechanisms into a basic denoising autoencoder to extract and fuse critical temporal and spatial information from each feature map, thus enhancing the model's ability to capture long-range dependencies and spatial features in MCG signals. Meanwhile, we further supplement and refine the semantic information for the feature maps through a global feature fusion method. By fusing multi-scale features from different skip connections, SkipDAEformer can learn more comprehensive representations of MCG signals, enabling the effective separation of clean signals from noise. Experimental results demonstrate that SkipDAEformer outperforms existing methods in denoising performance, channel consistency, feature consistency, and generalization ability and can be extended to a self-supervised learning framework. In actual noise reduction and diagnostic classification tasks, SkipDAEformer shows superior clinical acceptability and diagnostic value, potentially advancing MCG data analysis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274664","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

Deep-learning-based Partial Volume Correction in 99mTc-TRODAT-1 SPECT for Parkinson's Disease: A Preliminary Study on Clinical Translation. 基于深度学习的帕金森病99mTc-TRODAT-1 SPECT部分体积校正：临床翻译的初步研究

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-10 DOI: 10.1109/JBHI.2025.3578526

Haiyan Wang, Bingjie Wang, Wenbo Huang, Yibin Liu, Yu Du, Guang-Uei Hung, Zhanli Hu, Greta S P Mok

{"title":"Deep-learning-based Partial Volume Correction in 99mTc-TRODAT-1 SPECT for Parkinson's Disease: A Preliminary Study on Clinical Translation.","authors":"Haiyan Wang, Bingjie Wang, Wenbo Huang, Yibin Liu, Yu Du, Guang-Uei Hung, Zhanli Hu, Greta S P Mok","doi":"10.1109/JBHI.2025.3578526","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3578526","url":null,"abstract":"99mTc-TRODAT-1 SPECT is effective for the early detection of Parkinson's disease (PD). However, SPECT images suffer from severe partial volume effect, which impairs tissue boundary clarity and subsequent quantification accuracy. This work proposes an anatomical prior- and segmentation-free deep learning (DL)-based partial volume correction (PVC) method using an attentionbased conditional generative adversarial network (Att-cGAN) for 99mTc-TRODAT-1 SPECT. A population of 454 digital brain phantoms modelling anatomical and 99mTc-TRODAT activity variations in different PD categories are used to generate realistic SPECT projections using the SIMIND Monte Carlo code, and then reconstructed using ordered subset expectation maximization algorithm. The dataset is split into 320, 44 and 90 used for training, validation, and testing. Att-cGAN, cGAN and U-Net are implemented based on simulated data, then directly tested on 100 retrospectively collected clinical 99mTc-TRODAT data, with same acquisition and reconstruction parameters as in simulations. Non-DL PVC methods of Van-Cittert and iterative Yang are implemented for comparison. Physical and clinical metrics, as well as a no-gold standard technique (NGST) are applied to evaluate different PVC methods in the absence of clinical ground truth. Att-cGAN yields superior PVC performance in simulations as compared to other methods in physical and clinical evaluations. NGST assessment is generally consistent with the clinical metric evaluation. For the clinical study, Att-cGAN also obtains better NGST result than others striatal compartments can be discriminated on DLbased processed images. DL-PVC method is feasible for clinical PD SPECT using highly realistic simulated data.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144266143","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

A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network. 基于图变分自编码器和强盗优化图神经网络的多种慢性疾病预测建模生成框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-10 DOI: 10.1109/JBHI.2025.3578532

Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan P Fisher-Hoch, Joseph B Mccormick

{"title":"A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network.","authors":"Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan P Fisher-Hoch, Joseph B Mccormick","doi":"10.1109/JBHI.2025.3578532","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3578532","url":null,"abstract":"Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort ($n = 1,592$) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144266142","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