IEEE Journal of Biomedical and Health Informatics最新文献_第3页

In Silico Modeling and Validation of the Effect of Calcium-Activated Potassium Current on Ventricular Repolarization in Failing Myocytes. 钙激活钾电流对衰竭肌细胞心室复极化影响的硅学建模和验证

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-08 DOI: 10.1109/JBHI.2024.3495027

Marta Gomez, Jesus Carro, Esther Pueyo, Alba Perez, Aida Olivan, Violeta Monasterio

{"title":"In Silico Modeling and Validation of the Effect of Calcium-Activated Potassium Current on Ventricular Repolarization in Failing Myocytes.","authors":"Marta Gomez, Jesus Carro, Esther Pueyo, Alba Perez, Aida Olivan, Violeta Monasterio","doi":"10.1109/JBHI.2024.3495027","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3495027","url":null,"abstract":"Objective: The pathophysiological role of the small conductance calcium-activated potassium (SK) channels in human ventricular myocytes remains unclear. Experimental studies have reported upregulation of in pathological states, potentially contributing to ventricular arrhythmias. In heart failure (HF) patients, the upregulation of SK channels could be an adaptive physiological response to shorten the action potential duration (APD) under conditions of reduced repolarization reserve. In this work, we aimed at uncovering the contribution of SK channels to ventricular repolarization in failing myocytes.Methods: We extended an in silico electrophysiological model of human ventricular failing myocytes by including a representation of the SK channel activity. To calibrate the maximal SK current conductance (G SK), we simulated action potentials (APs) at different pacing frequencies and matched the changes in AP duration induced by SK channel inhibition or activation to available experimental data.Results: The optimal value obtained for GSK was 4.288 μ S/ μF in mid-myocardial cells, and 6.4 μS/ μF for endocardial and epicardial cells. The simulated SK block-induced effects were consistent with experimental evidence. 1-D simulations of a transmural ventricular fiber indicated that SK channel block may prolong the QT interval and increase the transmural dispersion of repolarization, potentially increasing the risk of arrhythmia in HF.Conclusion: Our results highlight the importance of considering the SK channels to improve the characterization of HF-induced ventricular remodeling. Simulations across various scenarios in 0-D and 1-D scales suggest that pharmacological SK channel inhibition could lead to adverse effects in failing ventricles.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603719","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

M-NET: Transforming Single Nucleotide Variations into Patient Feature Images for the Prediction of Prostate Cancer Metastasis and Identification of Significant Pathways. M-NET：将单核苷酸变异转化为患者特征图像，以预测前列腺癌转移并识别重要途径。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-07 DOI: 10.1109/JBHI.2024.3493618

Li Zhou, Jie Li, Weilong Tan

{"title":"M-NET: Transforming Single Nucleotide Variations into Patient Feature Images for the Prediction of Prostate Cancer Metastasis and Identification of Significant Pathways.","authors":"Li Zhou, Jie Li, Weilong Tan","doi":"10.1109/JBHI.2024.3493618","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3493618","url":null,"abstract":"High-performance prediction of prostate cancer metastasis based on single nucleotide variations remains a challenge. Therefore, we developed a novel biologically informed deep learning framework, named M-NET, for the prediction of prostate cancer metastasis. Within the framework, we transformed single nucleotide variations into patient feature images that are optimal for fitting convolutional neural networks. Moreover, we identified significant pathways associated with the metastatic status. The experimental results showed that M-NET significantly outperformed other comparison methods based on single nucleotide variations, achieving improvements in accuracy, precision, recall, F1-score, area under the receiver operating characteristics curve, and area under the precision-recall curve by 6.3%, 8.4%, 5.1%, 0.070, 0.041, and 0.026, respectively. Furthermore, M-NET identified some important pathways associated with the metastatic status, such as signaling by the hedgehog pathway. In summary, compared with other comparative methods, M-NET exhibited a better performance in the prediction of prostate cancer metastasis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603794","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

Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning. 利用双向降维和原型学习对高维脑电图图谱表示进行增量分类

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-07 DOI: 10.1109/JBHI.2024.3491096

Dongxu Liu, Qichuan Ding, Chenyu Tong, Jinshuo Ai, Fei Wang

{"title":"Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.","authors":"Dongxu Liu, Qichuan Ding, Chenyu Tong, Jinshuo Ai, Fei Wang","doi":"10.1109/JBHI.2024.3491096","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3491096","url":null,"abstract":"In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality of SPD matrices introduces too much computational burden to hinder the real-time applications of such BCI, especially in handling dynamic tasks, like incremental learning. Directly reducing the dimensionality of SPD matrices with conventional dimensionality reduction (DR) methods will alter the fundamental properties of SPD matrices. Moreover, current DR methods for incremental learning always necessitate retaining old data to update their representations under new mapping. To this end, a bidirectional two-dimensional principal component analysis for SPD manifold (B2DPCA-SPD) is proposed to reduce the dimensionality of SPD matrices, in such way that the reduced matrices remain on SPD manifold. Afterwards, the B2DPCA-SPD is extended to adapt to incremental learning tasks without saving old data. The incremental B2DPCA-SPD can be seamlessly integrated with the matrix-formed growing neural gas network (MF-GNG) to achieve an incremental EEG classification, where the new low-dimensional representations of the prototypes in old classifiers can be easily recalculated with the updated projection matrix. Extensive experiments are conducted on two public datasets to perform the EEG classification. The results demonstrate that our method significantly reduces computation time by 38.53% and 35.96%, and outperforms conventional methods in classification accuracy by 4.21% to 19.59%.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603671","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

Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data 罗生门集探索有助于为医学数据提供可信解释

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI: 10.1109/JBHI.2024.3443069

Katarzyna Kobylińska;Mateusz Krzyziński;Rafał Machowicz;Mariusz Adamek;Przemysław Biecek

{"title":"Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data","authors":"Katarzyna Kobylińska;Mateusz Krzyziński;Rafał Machowicz;Mariusz Adamek;Przemysław Biecek","doi":"10.1109/JBHI.2024.3443069","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3443069","url":null,"abstract":"The machine learning modeling process conventionally results in selecting a single model that maximizes a selected performance metric. However, this approach leads to abandoning a more profound analysis of slightly inferior models. Particularly in medical and healthcare studies, where the objective extends beyond predictions to generation of valuable insights, relying solely on a single model can result in misleading or incomplete conclusions. This problem is particularly pertinent when dealing with a set of models known as \u0000<italic>Rashomon set\u0000, with performance close to maximum one. Such a set can be numerous and may contain models describing the data in a different way, which calls for comprehensive analysis. This paper introduces a novel method to explore models in the Rashomon set, extending the conventional modeling approach. We propose the \u0000<monospace>Rashomon_DETECT</monospace>\u0000 algorithm to detect models with different behavior. It is based on recent developments in the eXplainable Artificial Intelligence (XAI) field. To quantify differences in variable effects among models, we introduce the Profile Disparity Index (PDI) based on measures from functional data analysis. To illustrate the effectiveness of our approach, we showcase its application on real-world medical problem: predicting survival among hemophagocytic lymphohistiocytosis (HLH) patients – a foundational case study. Additionally, we benchmark our approach on other medical data sets, demonstrating its versatility and utility in various contexts. If differently behaving models are detected in the Rashomon set, their combined analysis leads to more trustworthy conclusions, which is of vital importance for high-stakes applications such as medical applications.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"28 11","pages":"6454-6465"},"PeriodicalIF":6.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595931","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 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 : 2024-11-06 DOI: 10.1109/JBHI.2024.3472131

引用次数: 0

Guest Editorial: Trustworthy Machine Learning for Health Informatics 特邀社论：健康信息学中值得信赖的机器学习

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI: 10.1109/JBHI.2024.3472368

Luyang Luo;Daguang Xu;Jing Qin;Yueming Jin;Hao Chen

引用次数: 0

Hierarchical graph representation learning with multi-granularity features for anti-cancer drug response prediction. 利用多粒度特征进行分层图表示学习以预测抗癌药物反应

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI: 10.1109/JBHI.2024.3492806

Wei Peng, Jiangzhen Lin, Wei Dai, Ning Yu, Jianxin Wang

{"title":"Hierarchical graph representation learning with multi-granularity features for anti-cancer drug response prediction.","authors":"Wei Peng, Jiangzhen Lin, Wei Dai, Ning Yu, Jianxin Wang","doi":"10.1109/JBHI.2024.3492806","DOIUrl":"10.1109/JBHI.2024.3492806","url":null,"abstract":"Patients with the same type of cancer often respond differently to identical drug treatments due to unique genomic traits. Accurately predicting a patient's response to drug is crucial in guiding treatment decisions, alleviating patient suffering, and improving cancer prognosis. Current computational methods utilize deep learning models trained on extensive drug screening data to predict anti-cancer drug responses based on features of cell lines and drugs. However, the interaction between cell lines and drugs is a complex biological process involving interactions across various levels, from internal cellular and drug structures to the external interactions among different molecules.To address this complexity, we propose a novel Hierarchical graph representation Learning with Multi-Granularity features (HLMG) algorithm for predicting anti-cancer drug responses. The HLMG algorithm combines features at two granularities: the overall gene expression and pathway substructures of cell lines, and the overall molecular fingerprints and substructures of drugs. Subsequently, it constructs a heterogeneous graph including cell lines, drugs, known cell line-drug responses, and the associations between similar cell lines and similar drugs. Through a graph convolutional network model, the HLMG learns the final cell line and drug representations by aggregating features of their multi-level neighbor in the heterogeneous graph. The multi-level neighbors consist of the node self, directly related drugs/cell lines, and indirectly related similar drugs/cell lines. Finally, a linear correlation coefficient decoder is employed to reconstruct the cell line-drug correlation matrix to predict anti-cancer drug responses. Our model was tested on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) databases. Results indicate that HLMG outperforms other state-of-the-art methods in accurately predicting anti-cancer drug responses.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590593","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》作者须知

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI: 10.1109/JBHI.2024.3472135

引用次数: 0

Guest Editorial: Metaverse for Healthcare Trends, Challenges, and Solutions 特邀社论：医疗保健领域的 Metaverse 趋势、挑战和解决方案

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI: 10.1109/JBHI.2024.3472388

Weizheng Wang;Zhuotao Lian;Kapal Dev;Shan Jiang

引用次数: 0

Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation 用于半监督医学图像分割的不确定性全局对比学习框架

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI: 10.1109/JBHI.2024.3492540

Hengyang Liu;Pengcheng Ren;Yang Yuan;Chengyun Song;Fen Luo

{"title":"Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation","authors":"Hengyang Liu;Pengcheng Ren;Yang Yuan;Chengyun Song;Fen Luo","doi":"10.1109/JBHI.2024.3492540","DOIUrl":"10.1109/JBHI.2024.3492540","url":null,"abstract":"In semi-supervised medical image segmentation, the issue of fuzzy boundaries for segmented objects arises. With limited labeled data and the interaction of boundaries from different segmented objects, classifying segmentation boundaries becomes challenging. To mitigate this issue, we propose an uncertainty global contrastive learning (UGCL) framework. Specifically, we propose a patch filtering method and a classification entropy filtering method to provide reliable pseudo-labels for unlabelled data, while separating fuzzy boundaries and high-entropy pixel points as unreliable points. Considering that unreliable regions contain rich complementary information, we introduce an uncertainty global contrast learning method to distinguish these challenging unreliable regions, enhancing intra-class compactness and inter-class separability at the global data level. Within our optimization framework, we also integrate consistency regularization techniques and select unreliable points as targets for consistency. As demonstrated, the contrastive learning and consistency regularization applied to uncertain points enable us to glean valuable semantic information from unreliable data, which enhances segmentation accuracy. We evaluate our method on two publicly available medical image datasets and compare it with other state-of-the-art semi-supervised medical image segmentation methods, and a series of experimental results show that our method has achieved substantial improvements.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 1","pages":"433-442"},"PeriodicalIF":6.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590644","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