检索结果-内蒙古大学图书馆

Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study

引用

The Lancet Digital Health 2025年第4期7卷 e264-e274页

作者： Mayourian, Joshua Asztalos, Ivor B El-Bokl, Amr Lukyanenko, Platon Kobayashi, Ryan L La Cava, William G Ghelani, Sunil J Vetter, Victoria L Triedman, John K Department of Cardiology Boston Children's Hospital Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital Department of Pediatrics Harvard Medical School BostonMA United States Division of Pediatric Cardiology Perelman School of Medicine at the University of Pennsylvania Children's Hospital of Philadelphia PhiladelphiaPA United States

Background: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of LVSD in the general adult population, it has yet to be applied comprehensively across congenital heart disease lesions. Methods: We trained a convolutional neural network on paired ECG–echocardiograms (≤2 days apart) across the lifespan of a wide range of congenital heart disease lesions to detect left ventricular ejection fraction (LVEF) of 40% or less. Model performance was evaluated on single ECG–echocardiogram pairs per patient at Boston Children's Hospital (Boston, MA, USA) and externally at the Children's Hospital of Philadelphia (Philadelphia, PA, USA) using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves. Findings: The training cohort comprised 124 265 ECG–echocardiogram pairs (49 158 patients;median age 10·5 years [IQR 3·5–16·8];3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%). Test groups included internal testing (21 068 patients;median age 10·9 years [IQR 3·7–17·0];3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%) and external validation (42 984 patients;median age 10·8 years [IQR 4·9–15·0];1313 [1·7%] of 76 400 ECG–echocardiogram pairs with LVEF ≤40%) cohorts. High model performance was achieved during internal testing (AUROC 0·95, AUPRC 0·33) and external validation (AUROC 0·96, AUPRC 0·25) for a wide range of congenital heart disease lesions. Patients with LVEF greater than 40% by echocardiogram who were deemed high risk by AI-ECG were more likely to have future dysfunction compared with low-risk patients (hazard ratio 12·1 [95% CI 8·4–17·3];p © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

xML-workFlow: an end-to-end explainable scikit-learn workflow for rapid biomedical experimentation

arXiv

引用

arXiv 2025年

作者： Tran, Khoa A. Pearson, John V. Waddell, Nicola Medical Genomics Group Cancer Program QIMR Berghofer Medical Research Institute Brisbane Australia Genome Informatics Group Cancer Program QIMR Berghofer Medical Research Institute Brisbane Australia

Motivation Building and iterating machine learning models is often a resource-intensive process. In biomedical research, scientific codebases can lack scalability and are not easily transferable to work beyond what they were intended. xML-workFlow addresses this issue by providing a rapid, robust, and traceable end-to-end workflow that can be adapted to any ML project with minimal code rewriting. Results We show a practical, end-to-end workflow that integrates scikit-learn, MLflow, and SHAP. This template significantly reduces the time and effort required to build and iterate on ML models, addressing the common challenges of scalability and reproducibility in biomedical research. Adapting our template may save bioinformaticians time in development and enables biomedical researchers to deploy ML projects. © 2025, CC BY-NC-SA.

关键词： XML

来源：评论

学校读者我要写书评

暂无评论

Improving the quality of communicating with dementia patients: A virtual reality-based simulated communication approach

引用

British Journal of Educational Technology 2025年第1期56卷 167-189页

作者： Lin, Hui-Chen Huang, Hsin Tsai, Chia-Kuang Chang, Shao-Chen School of Nursing College of Nursing Taipei Medical University Taipei Taiwan Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Nursing Wan Fang Hospital Taipei Medical University Taipei Taiwan School of Nursing National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Neurology Tri Service General Hospital National Defense Medical Center Taipei Taiwan International Bachelor Program in Informatics and the Department of Information Communication Yuan Ze University Taoyuan Taiwan

Dementia patients may have language barriers and decreased comprehension ability. Their family caregivers can feel frustrated when communicating with them. Poor communication hinders family caregivers from obtaining accurate health information about patients, and may also increase their emotional burden, affecting patient care quality. The present study developed a virtual reality-based simulated communication training (VRSCT) system and applied it to a training course for family caregivers of dementia patients. It allowed family caregivers to simulate real-world situations in a VR environment, experience the daily communication barriers and stress with dementia patients, and apply their acquired knowledge and skills to solve related problems. This study used a randomised control experimental design with mixed analysis methods. A total of 63 family caregivers were recruited and randomly divided into the experimental group (N = 32) learning with the VRSCT system to interact with virtual dementia patients and practice communication skills, and the control group (N = 31) using the traditional role-playing method for practice. Quantitative data were analysed to determine participants' knowledge of dementia care, attitudes, communication confidence and skills. In addition, the qualitative method was used to analyse the participants' discussion records. The results showed that by using the VRSCT approach, participants significantly improved their knowledge of dementia care, attitudes, communication confidence and communication skills compared to the control group. In addition, participants reported that through the real-time feedback of the VRSCT system, they could recognise their previous incorrect communication approach. As a result, they adjusted their communication strategies and increased their self-confidence. Practitioner notes What is already known about this topic Situational simulation helps learners improve their communication skills in a safe environment. Virt

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

From Density to Void: Why Brain Networks Fail to Reveal Complex Higher-Order Structures

arXiv

引用

arXiv 2025年

作者： Chung, Moo K. El-Yaagoubi, Anass B. Qiu, Anqi Ombao, Hernando Department of Biostatistics and Medical Informatics University of Wisconsin Madison United States Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Health Technology and Informatics Hong Kong

In brain network analysis using resting-state fMRI, there is growing interest in modeling higher-order interactions beyond simple pairwise connectivity via persistent homology. Despite the promise of these advanced topological tools, robust and consistently observed higher-order interactions over time remain elusive. In this study, we investigate why conventional analyses often fail to reveal complex higher-order structures—such as interactions involving four or more nodes—and explore whether such interactions truly exist in functional brain networks. We utilize a simplicial complex framework often used in persistent homology to address this question. © 2025, CC BY-NC-SA.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

Actigraphy-derived circadian rhythms, sleep-wake patterns, and physical activity across clinical stages and pathophysiological subgroups in young people presenting for mental health care

引用

Journal of Psychiatric Research 2025年 186卷 396-406页

作者： Carpenter, Joanne S. Crouse, Jacob J. Park, Shin Ho Shin, Mirim Tonini, Emiliana Guo, Wei Merikangas, Kathleen R. Iorfino, Frank Leroux, Andrew Nichles, Alissa Zmicerevska, Natalia Scott, Jan Scott, Elizabeth M. Hickie, Ian B. Youth Mental Health and Technology Team Brain & Mind Centre The University of Sydney NSW Australia Genetic Epidemiology Branch Intramural Research Program National Institute of Mental Health Bethesda United States Department of Biostatistics and Informatics University of Colorado Anschutz Medical Campus Aurora CO United States Institute of Neuroscience University of Newcastle Newcastle United Kingdom

Staging models for youth mental health aim to locate clinical presentations on a spectrum from at-risk states to persistent disorder and predict future illness trajectories. Our previous publications on trans-diagnostic staging proposed three pathophysiological subgroups of major mood or psychotic disorders in youth (‘hyperarousal-anxious depression’, ‘circadian-bipolar spectrum’, ‘neurodevelopmental-psychosis’). This study aims to investigate differences in objective measures of 24hr sleep-wake patterns, circadian rhythms, and physical activity across clinical stages and pathophysiological subgroups. Actigraphy data (median: 13 days) was collected from 497 youth presenting for mental health care (21.6 ± 4.7 years, 37% male) and 88 controls (24.1 ± 3.8 years, 44% male). Actigraphy estimates were compared across groups using analysis of covariance adjusting for age and sex. Compared with controls or earlier clinical stages, later clinical stages were significantly associated with longer sleep duration(η² = 0.04), later sleep midpoint(η² = 0.02), lower sleep regularity(η² = 0.02), lower relative amplitude of the rest-activity cycle(η² = 0.05), higher interdaily stability(η² = 0.03), lower total activity(η² = 0.08) and less moderate-vigorous physical activity(η² = 0.06). Compared to controls, the ‘circadian-bipolar spectrum’ subgroup had later sleep midpoint(η² = 0.02), and higher interdaily stability(η² = 0.03);the ‘neurodevelopmental-psychosis’ subgroup had longer sleep duration(η² = 0.02), and lower total activity(η² = 0.03);and the ‘hyperarousal-anxious depression’ subtype had later sleep midpoint(η² = 0.02), and lower sleep regularity(η² = 0.02). The findings suggest differences in sleep-wake and rest-activity patterns according to clinical stage and proposed illness trajectory subtypes. The cross-sectional associations of

关键词： Actigraphy Bipolar disorder Circadian rhythms Clinical stage Depression Mood disorders Physical activity Sleep

来源：评论

学校读者我要写书评

暂无评论

SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal medical Image Segmentation Model for Highly Constrained Environments

SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Med...

引用

International Challenge on Segment Anything in medical Images on Laptop held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9783031818530

medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： medical imaging

来源：评论

学校读者我要写书评

暂无评论

Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

引用

2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Fetal Health Risk Classification Using Important Feature Selection and Cart Model on Cardiotocography Data

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Mitotic Figures in Glioma Tissues Using Deep Learning

Automated Detection of Mitotic Figures in Glioma Tissues Usi...

引用

IEEE International Symposium on Biomedical Imaging

作者： Avery Wang Yangming Ou Department of Radiology Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston MA US The Seven Hills School Cincinnati Ohio US

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Deep learning approaches have shown significant promise in advancing automated detection in digital pathology. In this study, we present a comprehensive investigation of methods for accurately detecting mitotic activity in regions of interest extracted from H&E stained glioma tissue images. Our approach involves training a Vision Transformer (ViT) from scratch while also evaluating alternative models, including ResNet, Swin Transformer, DenseNet, and VGG architectures, supplemented by various transfer learning strategies using ImageNet pretrained weights. Central to our work is the Glioma-MDC 2025 challenge, which serves as a benchmark for developing robust algorithms that detect and classify mitotic figures in glioma tissues. Experimental results, assessed via Accuracy, Precision, Recall, and F1 Score through cross-validation, demonstrate that our transformer based models achieve a highest F1 score of 0.982 on training data and an external test score of 0.969 during the challenge's validation phase. These findings suggest that our carefully curated approaches can significantly enhance automated mitosis detection, potentially leading to more accurate glioma grading in clinical settings.

关键词： Deep learning Training Pathology Computer vision Accuracy Translation Transfer learning Training data Transformers Reliability

来源：评论

学校读者我要写书评

暂无评论

Identifying Task Groupings for Multi-Task Learning Using Pointwise V-Usable Information

SSRN

引用

SSRN 2025年

作者： Li, Yingya Miller, Timothy Bethard, Steven Savova, Guergana Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School 401 Park Drive BostonMA02115 United States College of Information Science The University of Arizona TucsonAZ United States

ObjectiveEven in the era of Large Language Models (LLMs) which are claimed to be solutions for many tasks, fine-tuning language models remains a core methodology used in deployment for a variety of reasons – computational efficiency and performance maximization among them. Fine-tuning could be single-task or multi-task joint learning where the tasks support each other thus boosting their performance. The success of multi-task learning can depend heavily on which tasks are grouped together. Naively grouping all tasks or a random set of tasks can result in negative transfer, with the multi-task models performing worse than single-task models. Though many efforts have been made to identify task groupings and to measure the relatedness among different tasks, it remains a challenging research topic to define a metric to identify the best task grouping out of a pool of many potential task combinations. We propose such *** propose a metric of task relatedness based on task difficulty measured by pointwise V-usable information (PVI). PVI is a recently proposed metric to estimate how much usable information a dataset contains given a model. We hypothesize that tasks with not statistically different PVI estimates are similar enough to benefit from the joint learning process. We conduct comprehensive experiments to evaluate the feasibility of this metric for task grouping on 15 NLP datasets in the general, biomedical, and clinical domains. We compare the results of the joint learners against single learners, existing baseline methods, and recent large language models, including Llama 2 and *** results show that by grouping tasks with similar PVI estimates, the joint learners yielded competitive results with fewer total parameters, with consistent performance across *** domain-specific tasks, finetuned models may remain a preferable option, and the PVI-based method of grouping tasks for multi-task learning could be particularly benefi

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：