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

A flexible multi-sensor device enabling handheld sensing of heart sounds by untrained users.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-17 DOI: 10.1109/JBHI.2025.3551882

Andrew McDonald, Maximilian Nussbaumer, Nirmani Rathnayake, Richard Steeds, Anurag Agarwal

引用次数: 0

Pre-operative Overall Survival Prediction of Diffuse Glioma Enhanced by Longitudinal Data.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-14 DOI: 10.1109/JBHI.2025.3550937

Zhenyu Tang, Jiannan Li, Jingliang Cheng, Zhi-Cheng Li, Zhenyu Zhang, Jing Yan

{"title":"Pre-operative Overall Survival Prediction of Diffuse Glioma Enhanced by Longitudinal Data.","authors":"Zhenyu Tang, Jiannan Li, Jingliang Cheng, Zhi-Cheng Li, Zhenyu Zhang, Jing Yan","doi":"10.1109/JBHI.2025.3550937","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3550937","url":null,"abstract":"Many pre-operative overall survival (OS) prediction methods have been proposed to assist personalized treatment of diffuse glioma for better prognosis. Most of them utilize pre-operative data, while post-operative data, which contains essential prognosis-related information (e.g., surgical outcomes and lesion evolution) is neglected, hindering prediction accuracy. However, incorporating post-operative data could make OS prediction inapplicable at pre-operative stage, affecting clinical utility. To address this contradiction, in this paper, we propose an effective framework that leverages longitudinal data (pre- and post-operative data) to enhance pre-operative OS prediction. Specifically, two OS prediction networks are built in a knowledge distillation framework. One is the teacher network trained with longitudinal data, and the other is the student network relying solely on pre-operative data. Distillation of deep features is conducted to align the performance of the student network with that of the teacher network. Moreover, mass effect and its distillation are adopted to incorporate lesion evolution information, further enhancing prediction performance. Based on our framework, the student network can leverage essential post-operative information without compromising its applicability at pre-operative stage. Experiments on both in-house and public datasets demonstrate that the student network outperforms all state-of-the-art methods under evaluation with statistical significance. Further ablation study reveals that distillation of mass effect and deep features play positive roles in OS prediction. Moreover, new prognosis-related factors are discovered by comparing the student network with and without distillation. Codes are available at https://github.com/LiJiannan2000/OSPred.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630067","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

Contrastive Learning with Transformer to Predict the Chronicity of Children with Immune Thrombocytopenia.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-14 DOI: 10.1109/JBHI.2025.3551365

Yuntian Wang, Yongqiang Tang, Jingyao Ma, Zhenping Chen, Chang Cui, Mingda Li, Runhui Wu, Wensheng Zhang

{"title":"Contrastive Learning with Transformer to Predict the Chronicity of Children with Immune Thrombocytopenia.","authors":"Yuntian Wang, Yongqiang Tang, Jingyao Ma, Zhenping Chen, Chang Cui, Mingda Li, Runhui Wu, Wensheng Zhang","doi":"10.1109/JBHI.2025.3551365","DOIUrl":"10.1109/JBHI.2025.3551365","url":null,"abstract":"Immune thrombocytopenia (ITP) is a typically self-limiting and immune-mediated bleeding disorder in children. Approximately 20% of children with ITP experience chronicity, leading to reduced quality of life and increased treatment burden. The accurate prediction of chronicity would enable clinicians to make personalized treatment plans at an early stage. However, due to the self-limiting nature of ITP and the scarcity of available children patients, the data presents two prominent issues: small data and imbalanced class, which are unfavorable for effectively training a deep learning model. To handle these issues concurrently, we proposed a novel method that integrates contrastive learning with the Transformer. First, we adopt the FT-Transformer as our backbone, which allows our model to flexibly process heterogeneous tabular data. Second, we amplify and balance the original data via random masking and oversampling, respectively. Lastly, we build contrastive pairs according to the latent representations generated by the FT-Transformer encoder, such that the amplified and oversampled synthetic data can be utilized thoroughly. The experimental results on real-world ITP children data show that our proposal outperforms the state-of-the-art methods, and demonstrate the significant advantages of dealing with insufficient and imbalanced problems.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630042","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

EvolveFNN: An interpretable framework for early detection using longitudinal electronic health record data.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-14 DOI: 10.1109/JBHI.2025.3551312

Yufeng Zhang, Emily Wittrup, Matthew Hodgman, Michael Mathis, Kayvan Najarian

{"title":"EvolveFNN: An interpretable framework for early detection using longitudinal electronic health record data.","authors":"Yufeng Zhang, Emily Wittrup, Matthew Hodgman, Michael Mathis, Kayvan Najarian","doi":"10.1109/JBHI.2025.3551312","DOIUrl":"10.1109/JBHI.2025.3551312","url":null,"abstract":"The extensive adoption of artificial intelligence in clinical decision support systems requires greater model interpretability. Hence, we introduce EvolveFNN, an interpretable model based on the recurrent neural network that merges fuzzy logic principles with recurrent units. This model is designed to train precise and understandable models using high-dimensional longitudinal electronic health records data. Through supervised learning, our method allows the identification of variable encoding functions and significant rules. To demonstrate performance and capabilities in classification and rule discovery, we first test our method on a simulated dataset. The proposed methods achieve the best model performance compared to other methods, and the rules learned are almost identical to what we used to generate the synthetic data. Furthermore, we showcase a pilot application that proves its potential in the early detection of cardiac event onset. Our proposed algorithm obtains a comparable model performance to vanilla GRU models and remains relatively stable when the prediction window size changes. Examining the rules generated by our proposed model, we find that the extracted rules not only align with clinical practices and existing literature but also provide potential risk factors not explored in the population. The additional experiments on the MIMIC-III benchmark dataset show the algorithm's generalizability. In conclusion, our proposed approach can effectively train accurate, interpretable, and reliable models using large longitudinal electronic health records, offering clinicians valuable insights. Source code is available at https://github.com/kayvanlabs/EvolveFNN.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630048","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

Learning the Difference of Few-Shot Food Data Using Multivariate Knowledge-Guided Variational Autoencoder.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-12 DOI: 10.1109/JBHI.2025.3550347

Yi Zhang, Sheng Huang, Mingjian Hong, Dan Yang

{"title":"Learning the Difference of Few-Shot Food Data Using Multivariate Knowledge-Guided Variational Autoencoder.","authors":"Yi Zhang, Sheng Huang, Mingjian Hong, Dan Yang","doi":"10.1109/JBHI.2025.3550347","DOIUrl":"10.1109/JBHI.2025.3550347","url":null,"abstract":"Recent advancements in food image recognition have underscored its importance in dietary monitoring, which promotes a healthy lifestyle and aids in the prevention of diseases such as diabetes and obesity. While mainstream food recognition methods excel in scenarios with large-scale annotated datasets, they falter in few-shot regimes where data is limited. This paper addresses this challenge by introducing a variational generative method, the Multivariate Knowledge-guided Variational AutoEncoder (MK-VAE), for few-shot food recognition. MK-VAE leverages handcrafted features and semantic embeddings as multivariate prior knowledge to strengthen feature learning and feature generation in different phases. Specifically, we design a lightweight and flexible feature distillation module that distills handcrafted features to enhance the feature learning network for capturing the salient visual information in few-shot samples. During the feature generation phase, we utilize a variational autoencoder to learn the difference distribution of food data and explicitly boost the latent representation with category-level semantic embeddings to pull homogeneous features closer together while pushing inhomogeneous features apart. Experimental results demonstrate that our proposed MK-VAE significantly outperforms state-of-the-art few-shot food recognition methods in both 5-way 1-shot and 5-way 5-shot settings on three widely-used benchmark datasets: Food-101, VIREO Food-172, and UECFood-256.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614780","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 Compact Quiet Sleep Estimator Based on Cardiorespiratory and Video Motion Features for Maturation Analysis in NICU.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-12 DOI: 10.1109/JBHI.2025.3550805

Houda Jebbari, Sandie Cabon, Patrick Pladys, Guy Carrault, Fabienne Poree

{"title":"A Compact Quiet Sleep Estimator Based on Cardiorespiratory and Video Motion Features for Maturation Analysis in NICU.","authors":"Houda Jebbari, Sandie Cabon, Patrick Pladys, Guy Carrault, Fabienne Poree","doi":"10.1109/JBHI.2025.3550805","DOIUrl":"10.1109/JBHI.2025.3550805","url":null,"abstract":"Monitoring sleep of premature infants is a vital aspect of clinical care, as it can reveal potential future pathologies and health issues. This study presents a novel approach to automatically estimate and track Quiet Sleep (QS) in 33 newborns using ECG, respiration, and video motion features. Using an annotated dataset from 15 neonates (10 preterm, 5 full-term) encompassing 127.2 hours, a comprehensive feature extraction and selection process was employed. Three classifiers (Random Forest, Logistic Regression, K-Nearest Neighbors) were evaluated to develop a QS estimation model. A compact and interpretable model was selected, achieving a balanced accuracy of 84.67.5%. The robustness of the model was further enhanced by incorporating a switching mechanism between models using only ECG and respiration when video data was unavailable. The study further explored the evolution of QS during hospitalization using a large dataset with 18 newborns (16 preterm and 2 term) and 1396.6 hours of data. It highlighted an increase in QS duration and mean interval duration with post-menstrual age. The results offer valuable insights into the developmental progress of healthy preterm infants and underscore the potential of continuous, non-invasive monitoring in neonatal intensive care units.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614776","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

BreastCancerNet: Flask-Enabled Attention-Driven Hybrid Dual DNN Framework for Real-Time Breast Cancer Prediction.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-12 DOI: 10.1109/JBHI.2025.3550564

Allam Jaya Prakash, Kiran Kumar Patro, Palash Ingle, Jeevana Jyothi Pujari, Sidheswar Routray, Rutvij H Jhaveri

{"title":"BreastCancerNet: Flask-Enabled Attention-Driven Hybrid Dual DNN Framework for Real-Time Breast Cancer Prediction.","authors":"Allam Jaya Prakash, Kiran Kumar Patro, Palash Ingle, Jeevana Jyothi Pujari, Sidheswar Routray, Rutvij H Jhaveri","doi":"10.1109/JBHI.2025.3550564","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3550564","url":null,"abstract":"Breast cancer is the most prevalent cancer among women and poses a significant global health challenge due to its association with uncontrolled cell proliferation. Artificial intelligence (AI) integration into medical practice has shown promise in boosting diagnosis accuracy and treatment protocol optimisation, thus contributing to improved survival rates globally. This paper presents a comprehensive analysis utilizing the Wisconsin Breast Cancer dataset, comprising data from 569 patients and 30 attributes. We propose BreastCancerNet, a hybrid AI architecture that leverages dual deep neural networks (DNNs) coupled with an attention mechanism to enhance breast cancer diagnosis. The proposed framework integrates two distinct DNNs (DNN-I and DNN-II) to extract diverse feature representations from the dataset, which are then concatenated for comprehensive analysis. An attention mechanism is employed to prioritize critical features, thereby improving the model's focus on essential characteristics of the input data. The final classification is performed using a support vector machine (SVM), achieving an impressive accuracy rate of 99.42% in differentiating between malignant and benign cases. Furthermore, we introduce a user-centric web application that facilitates real-time breast cancer detection by allowing users to input new attributes. This intuitive web interface fosters interactive engagement with the predictive algorithm, potentially enhancing breast cancer screening and treatment outcomes.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614777","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

ExplainMIX: Explaining Drug Response Prediction in Directed Graph Neural Networks with Multi-Omics Fusion.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-11 DOI: 10.1109/JBHI.2025.3550353

Ying Xiang, Xiaodi Li, Qian Gao, Junfeng Xia, Zhenyu Yue

{"title":"ExplainMIX: Explaining Drug Response Prediction in Directed Graph Neural Networks with Multi-Omics Fusion.","authors":"Ying Xiang, Xiaodi Li, Qian Gao, Junfeng Xia, Zhenyu Yue","doi":"10.1109/JBHI.2025.3550353","DOIUrl":"10.1109/JBHI.2025.3550353","url":null,"abstract":"The intricacies of cancer present formidable challenges in achieving effective treatments. Despite extensive research in computational methods for drug response prediction, achieving personalized treatment insights remains challenging. Emerging solutions combine multiple omics data, leveraging graph neural networks to integrate molecular interactions into the reasoning process. However, effectively modeling and harnessing this information, as well as gaining the trust of clinical professionals remain complex. This paper introduces ExplainMIX, a pioneering approach that utilizes directed graph neural networks to predict drug responses with interpretability. ExplainMIX adeptly captures intricate structures and features within directed heterogeneous graphs, leveraging diverse data modalities such as genomics, proteomics, and metabolomics. ExplainMIX goes beyond prediction by generating transparent and interpretable explanations. Incorporating edge-level, metapath, and graph structure information, it provides meaningful insights into factors influencing drug response, supporting clinicians and researchers in the development of targeted therapies. Empirical results validate the efficacy of ExplainMIX in prediction and interpretation tasks by constructing a quantitative evaluation ground truth. This approach aims to contribute to precision medicine research by addressing challenges in interpretable personalized drug response prediction within the landscape of cancer. The dataset and source code of ExplainMIX are publicly available at https://github.com/AhauBioinformatics/ExplainMIX.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604711","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

Identifying Disease-Gene Associations by Topological and Biological Feature-based Data Augmentation and Graph Neural Networks.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-11 DOI: 10.1109/JBHI.2025.3549509

Yuan Zhang, Juan Wang, Jiajie Xing, Xiaomin Chen

{"title":"Identifying Disease-Gene Associations by Topological and Biological Feature-based Data Augmentation and Graph Neural Networks.","authors":"Yuan Zhang, Juan Wang, Jiajie Xing, Xiaomin Chen","doi":"10.1109/JBHI.2025.3549509","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3549509","url":null,"abstract":"Predicting gene-disease associations is essential for understanding disease pathogenesis and determining therapeutic targets. While prior methods have integrated diverse biological information to make predictions, they still encounter several challenges. First, incomplete and sparse gene-disease association data constrain model performance. Second, integrating heterogeneous data sources is not straightforward. To address these challenges, we propose a novel method, DAVGAE, which combines data augmentation, Variational Graph Auto-Encoders (VGAE), and attention mechanisms. DAVGAE integrates both the biological and topological features of genes and diseases to address challenges such as data sparsity and heterogeneity. By leveraging these features, it calculates cosine similarity scores for gene-disease pairs and applies a novel data augmentation strategy to enhance association data by selecting gene-disease associations with higher similarity scores. Using a four-layer Graph Neural Network (GNN) encoder, DAVGAE effectively learns robust and discriminative representations for genes and diseases within the association network. Finally, an inner product decoder predicts association scores for all gene-disease pairs. Comprehensive experiments on three gene-disease association datasets reveal that DAVGAE outperforms baseline models in predicting gene-disease associations. DAVGAE is freely available at https://github.com/imustu/DAVGAE.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604712","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 Trusted Medical Image Zero-watermarking Scheme Based On DCNN and Hyperchaotic System.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-11 DOI: 10.1109/JBHI.2025.3550324

Ruotong Xiang, Gang Liu, Min Dang, Quan Wangn, Rong Pan

{"title":"A Trusted Medical Image Zero-watermarking Scheme Based On DCNN and Hyperchaotic System.","authors":"Ruotong Xiang, Gang Liu, Min Dang, Quan Wangn, Rong Pan","doi":"10.1109/JBHI.2025.3550324","DOIUrl":"10.1109/JBHI.2025.3550324","url":null,"abstract":"The zero-watermarking methods provide a means of lossless, which was adopted to protect medical image copyright requiring high integrity. However, most existing studies have only focused on robustness and there has been little discussion about the analysis and experiment on discriminability. Therefore, this paper proposes a trusted robust zero-watermarking scheme for medical images based on Deep convolution neural network (DCNN) and the hyperchaotic encryption system. Firstly, the medical image is converted into several feature map matrices by the specific convolution layer of DCNN. Then, a stable Gram matrix is obtained by calculating the colinear correlation between different channels in feature map matrices. Finally, the Gram matrixes of the medical image and the feature map matrixes of the watermark image are fused by the trained DCNN to generate the zero-watermark. Meanwhile, we propose two feature evaluation criteria for finding differentiated eigenvalues. The eigenvalue is used as the explicit key to encrypt the generated zero-watermark by Lorenz hyperchaotic encryption, which enhances security and discriminability. The experimental results show that the proposed scheme can resist common image attacks and geometric attacks, and is distinguishable in experiments, being applicable for the copyright protection of medical images.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604708","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