Pub Date : 2026-01-01Epub Date: 2026-02-17DOI: 10.1007/s13721-025-00724-w
Kassidy Crockett, Autumn Langer, Tyler Cook, Emily P Hendryx Lyons
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.
{"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":"https://doi.org/10.1007/s13721-025-00724-w","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"15 1","pages":"51"},"PeriodicalIF":2.0,"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}
Pub Date : 2024-01-01Epub Date: 2024-10-07DOI: 10.1007/s13721-024-00490-1
Rupert Ironside-Smith, Beryl Noë, Stuart M Allen, Shannon Costello, Liam D Turner
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.
{"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":"<p><p>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.</p>","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}
Pub Date : 2023-11-14DOI: 10.1007/s13721-023-00433-2
Abinash Mishra, U. Srinivasulu Reddy, A. Venkataswamy Reddy
{"title":"An improved cost-sensitive approach toward the selection of wart treatment methods","authors":"Abinash Mishra, U. Srinivasulu Reddy, A. Venkataswamy Reddy","doi":"10.1007/s13721-023-00433-2","DOIUrl":"https://doi.org/10.1007/s13721-023-00433-2","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"77 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134900602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 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
{"title":"Automatic classification of depressive users on Twitter including temporal analysis","authors":"Luis Roberto García-Noguez, Saúl Tovar-Arriaga, Wilfrido Jacobo Paredes-García, Juan Manuel Ramos-Arreguín, Marco Antonio Aceves-Fernandez","doi":"10.1007/s13721-023-00434-1","DOIUrl":"https://doi.org/10.1007/s13721-023-00434-1","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":" 47","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135240777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1007/s13721-023-00432-3
Saba Raoof Syed, Saleem Durai M A
{"title":"A diagnosis model for detection and classification of diabetic retinopathy using deep learning","authors":"Saba Raoof Syed, Saleem Durai M A","doi":"10.1007/s13721-023-00432-3","DOIUrl":"https://doi.org/10.1007/s13721-023-00432-3","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-23DOI: 10.1007/s13721-023-00429-y
V. Vidhya Rajalakshmi, Jayaprakash Chinnappan
{"title":"Analysis of cortisol mechanism to predict common genes between PCOS and its co-morbidities","authors":"V. Vidhya Rajalakshmi, Jayaprakash Chinnappan","doi":"10.1007/s13721-023-00429-y","DOIUrl":"https://doi.org/10.1007/s13721-023-00429-y","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1007/s13721-023-00431-4
Sohaib Asif, None Qurrat-ul-Ain, Muhammad Awais, Saif Ur Rehman Khan
{"title":"IR-CNN: Inception residual network for detecting kidney abnormalities from CT images","authors":"Sohaib Asif, None Qurrat-ul-Ain, Muhammad Awais, Saif Ur Rehman Khan","doi":"10.1007/s13721-023-00431-4","DOIUrl":"https://doi.org/10.1007/s13721-023-00431-4","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-09DOI: 10.1007/s13721-023-00428-z
Asmita Dixit, Manish Kumar Thakur
{"title":"Advancements and emerging trends in brain tumor classification using MRI: a systematic review","authors":"Asmita Dixit, Manish Kumar Thakur","doi":"10.1007/s13721-023-00428-z","DOIUrl":"https://doi.org/10.1007/s13721-023-00428-z","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"23 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84623079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1007/s13721-023-00430-5
Yoshiyasu Takefuji
{"title":"COVID-19 lag time and case fatality rate calculation tool, as well as a tool to identify when policymakers made mistakes","authors":"Yoshiyasu Takefuji","doi":"10.1007/s13721-023-00430-5","DOIUrl":"https://doi.org/10.1007/s13721-023-00430-5","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"51 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82255341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1007/s13721-023-00426-1
Junfeng Ma
{"title":"Using nonlinear analysis and neural network to classify bipolar I disorder electroencephalogram signals from normal electroencephalograms","authors":"Junfeng Ma","doi":"10.1007/s13721-023-00426-1","DOIUrl":"https://doi.org/10.1007/s13721-023-00426-1","url":null,"abstract":"","PeriodicalId":44876,"journal":{"name":"Network Modeling and Analysis in Health Informatics and Bioinformatics","volume":"29 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74643558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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