Network Modeling and Analysis in Health Informatics and Bioinformatics最新文献

Comparing subset selection methods in multi-lead electrocardiogram data. 多导联心电图数据子集选择方法的比较。

IF 2.4

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2026-01-01 Epub Date: 2026-02-17 DOI: 10.1007/s13721-025-00724-w

Kassidy Crockett, Autumn Langer, Tyler Cook, Emily P Hendryx Lyons

{"title":"Comparing subset selection methods in multi-lead electrocardiogram data.","authors":"Kassidy Crockett, Autumn Langer, Tyler Cook, Emily P Hendryx Lyons","doi":"10.1007/s13721-025-00724-w","DOIUrl":"10.1007/s13721-025-00724-w","url":null,"abstract":"Heart health complications are often diagnosed through the presence of anomalous heartbeat morphologies in the electrocardiogram (ECG). The automated summarization of ECG data can aid clinicians in promoting a more comprehensive, time-sensitive assessment. Given that lead availability may vary in different health-monitoring settings, however, we investigate subset selection in single-lead ECG, 12-lead ECG, vectorcardiogram (VCG), and VCG magnitude representations using data from the St. Petersburg INCART 12-lead Arrhythmia Database. Subsets for each data representation are found using seven different algorithms that can be used to form CUR matrix decompositions, three of which use oversampling from reduced representations of the data. The QR-based discrete empirical interpolation method (Q-DEIM) with 12-lead data yields the highest class detection results among the non-oversampling algorithms. The extended DEIM (E-DEIM) oversampling algorithm performs the best overall with a lower-rank representation of the VCG magnitude data, offering potential computational savings along with its improved class detection. The results of this work provide insight into the summarization of different ECG representations with the goal that such summaries can in turn be used in subsequent models or presented directly to clinicians for improved patient outcomes.","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"15 1","pages":"51"},"PeriodicalIF":2.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12913273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motif discovery in hospital ward vital signs observation networks. 医院病房生命体征观测网络中的动机发现。

IF 2

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2024-01-01 Epub Date: 2024-10-07 DOI: 10.1007/s13721-024-00490-1

Rupert Ironside-Smith, Beryl Noë, Stuart M Allen, Shannon Costello, Liam D Turner

{"title":"Motif discovery in hospital ward vital signs observation networks.","authors":"Rupert Ironside-Smith, Beryl Noë, Stuart M Allen, Shannon Costello, Liam D Turner","doi":"10.1007/s13721-024-00490-1","DOIUrl":"10.1007/s13721-024-00490-1","url":null,"abstract":"Vital signs observations are regular measurements used by healthcare staff to track a patient's overall health status on hospital wards. We look at the potential in re-purposing aggregated and anonymised hospital data sources surrounding vital signs recording to provide new insights into how care is managed and delivered on wards. In this paper, we conduct a retrospective longitudinal observational study of 770,720 individual vital signs recordings across 20 hospital wards in South Wales (UK) and present a network modelling framework to explore and extract behavioural patterns via analysis of the resulting network structures at a global and local level. Self-loop edges, dyad, triad, and tetrad subgraphs were extracted and evaluated against a null model to determine individual statistical significance, and then combined into ward-level feature vectors to provide the means for determining notable behaviours across wards. Modelling data as a static network, by aggregating all vital sign observation data points, resulted in high uniformity but with the loss of important information which was better captured when modelling the static-temporal network, highlighting time's crucial role as a network element. Wards mostly followed expected patterns, with chains or stand-alone supplementary observations by clinical staff. However, observation sequences that deviate from this are revealed in five identified motif subgraphs and 6 anti-motif subgraphs. External ward characteristics also showed minimal impact on the relative abundance of subgraphs, indicating a 'superfamily' phenomena that has been similarly seen in complex networks in other domains. Overall, the results show that network modelling effectively captured and exposed behaviours within vital signs observation data, and demonstrated uniformity across hospital wards in managing this practice.","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"13 1","pages":"55"},"PeriodicalIF":2.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved cost-sensitive approach toward the selection of wart treatment methods 一个改进的成本敏感的方法，以选择疣治疗方法

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-11-14 DOI: 10.1007/s13721-023-00433-2

Abinash Mishra, U. Srinivasulu Reddy, A. Venkataswamy Reddy

引用次数: 0

Automatic classification of depressive users on Twitter including temporal analysis 对Twitter上抑郁用户的自动分类，包括时间分析

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-11-09 DOI: 10.1007/s13721-023-00434-1

Luis Roberto García-Noguez, Saúl Tovar-Arriaga, Wilfrido Jacobo Paredes-García, Juan Manuel Ramos-Arreguín, Marco Antonio Aceves-Fernandez

引用次数: 0

A diagnosis model for detection and classification of diabetic retinopathy using deep learning 基于深度学习的糖尿病视网膜病变检测与分类诊断模型

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-10-13 DOI: 10.1007/s13721-023-00432-3

Saba Raoof Syed, Saleem Durai M A

引用次数: 0

Analysis of cortisol mechanism to predict common genes between PCOS and its co-morbidities 皮质醇预测多囊卵巢综合征及其合并症共同基因的机制分析

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-09-23 DOI: 10.1007/s13721-023-00429-y

V. Vidhya Rajalakshmi, Jayaprakash Chinnappan

引用次数: 0

IR-CNN: Inception residual network for detecting kidney abnormalities from CT images IR-CNN:基于CT图像检测肾脏异常的初始残差网络

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-09-20 DOI: 10.1007/s13721-023-00431-4

Sohaib Asif, None Qurrat-ul-Ain, Muhammad Awais, Saif Ur Rehman Khan

引用次数: 0

Advancements and emerging trends in brain tumor classification using MRI: a systematic review MRI在脑肿瘤分类中的进展和新趋势:系统回顾

IF 2.3

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-09-09 DOI: 10.1007/s13721-023-00428-z

Asmita Dixit, Manish Kumar Thakur

引用次数: 0

COVID-19 lag time and case fatality rate calculation tool, as well as a tool to identify when policymakers made mistakes COVID-19滞后时间和病死率计算工具，以及识别政策制定者何时犯错的工具

IF 2.3

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-09-05 DOI: 10.1007/s13721-023-00430-5

Yoshiyasu Takefuji

引用次数: 0

Using nonlinear analysis and neural network to classify bipolar I disorder electroencephalogram signals from normal electroencephalograms 应用非线性分析和神经网络对双相I型障碍脑电图信号与正常脑电图进行分类

IF 2.3

Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-09-05 DOI: 10.1007/s13721-023-00426-1

Junfeng Ma

引用次数: 0