Computer methods and programs in biomedicine update最新文献

{"title":"Insight into the treatment strategy on pneumonia transmission with asymptotic carrier stage using fractional order modeling approach","authors":"Shewafera Wondimagegnhu Teklu ,&nbsp;Belela Samuel Kotola","doi":"10.1016/j.cmpbup.2024.100134","DOIUrl":"10.1016/j.cmpbup.2024.100134","url":null,"abstract":"<div><p>Pneumonia remains a significant global health concern, claiming millions of lives annually. This study introduces a novel approach by developing and analyzing a Caputo fractional order pneumonia infection model that incorporates pneumonia asymptomatic carriers. Through a qualitative lens, we establish the existence and uniqueness of model solutions by applying the well-known Picard–Lindelöf criteria. Employing a next-generation approach, we compute the model's basic reproduction number, determine equilibrium points, and probe their stabilities. The main objective of this study is to investigate the transmission dynamics of pneumonia infection with a focus on asymptomatic carriers using fractional order modeling. Our findings reveal innovative outcomes as we showcase numerical simulations, providing a practical verification of the qualitative results. Notably, we explore the fractional order model solutions in-depth, examining the influence of specific model parameters and fractional orders on the dynamics of pneumonia disease transmission. The significant contributions of this study lie in advancing the theoretical foundation of infectious disease modeling, particularly in the context of pneumonia. Through rigorous analysis and numerical simulations, we provide valuable insights into the behavior of the proposed fractional order model. These findings hold practical implications for understanding and managing pneumonia transmission in real-world scenarios. Our study serves as a vital resource for researchers, policymakers, and healthcare practitioners involved in combating and preventing the spread of pneumonia, ultimately contributing to global efforts in public health.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000016/pdfft?md5=6871bd31c7f672357b1ea9c42a2eec1a&pid=1-s2.0-S2666990024000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395642","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}

{"title":"Why do youths initiate to smoke? A data mining analysis on tobacco advertising, peer, and family factors for Indonesian youths","authors":"Enny Rachmani ,&nbsp;Sri Handayani ,&nbsp;Kriswiharsi Kun Saptorini ,&nbsp;Nurjanah ,&nbsp;Dian Kusuma ,&nbsp;Abdillah Ahsan ,&nbsp;Edi Jaya Kusuma ,&nbsp;Suleman Atique ,&nbsp;Jumanto Jumanto","doi":"10.1016/j.cmpbup.2024.100168","DOIUrl":"10.1016/j.cmpbup.2024.100168","url":null,"abstract":"<div><div>Global Youth Tobacco Survey (GYTS), Indonesia showed that 60,9 % of students noticed cigarette advertisements or promotions in outdoor media. Our study aimed to understand the impact of outdoor tobacco advertising and peer and family association with Youth's smoking behavior.</div><div>This study deployed a cross-sectional approach to explore factors related to youth smoking behavior, such as peers, family, and tobacco advertising. The GYTS questionnaire was adapted as the instrument and distributed to 400 students from 20 high schools to observe smoking behavior. The chosen schools based on the previous study whose classify school in hot-spot and non hot-spot area. This study applied a data mining approach with a decision tree to generate the models.</div><div>This study generates a decision tree model that describes the peer factor as the key to introducing Youth to smoking. The model also reveals that youth in the non-hotspot advertising area are not likely to develop Youth to smoke. The model has a performance classification of 77.5 % This study found that youth with smoking fathers are more likely to start smoking earlier, youth whose both parents are smokers, and mothers who are smokers have a confidence level of 100 % to smoke. Further research is warranted to investigate rural districts to explore any regional and socioeconomic variations.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"6 ","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423928","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}

{"title":"Precision medicine: Beyond AI","authors":"Marco Filetti ,&nbsp;Manuela Petti ,&nbsp;Lorenzo Farina","doi":"10.1016/j.cmpbup.2024.100157","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100157","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000247/pdfft?md5=2afe713191963f1a17ebab8dcb2d42e0&pid=1-s2.0-S2666990024000247-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141067833","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}

{"title":"Erratum regarding missing declaration of competing interest statements in previously published articles","authors":"Authors","doi":"10.1016/j.cmpbup.2023.100128","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100128","url":null,"abstract":"<div><p>Abstract</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990023000368/pdfft?md5=4e34b54b0fb88eed5e22e823484e045e&pid=1-s2.0-S2666990023000368-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141294373","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}

{"title":"Isolation and abuse: The intersection of Covid19 and domestic violence","authors":"Sidra Waseem Khan ,&nbsp;Hafsah Arshed Ali Khan ,&nbsp;Dawn Clarke","doi":"10.1016/j.cmpbup.2024.100149","DOIUrl":"10.1016/j.cmpbup.2024.100149","url":null,"abstract":"<div><p>Amid the global lockdowns, the surge in domestic violence cases has been one of the distressing consequences of the Covid19 pandemic [<span>1</span>]. Isolation, stress, and economic distress amongst other factors have all contributed to an increase in this form of abuse. Women have been subjected to discrimination and abuse for around 2700 years, and a clear example of such discrimination can be seen in the form of laws operating in 753 BCE that allowed the disciplining of wives [<span>2</span>]. The matter of domestic abuse started receiving recognition in the 1970s when it became a compulsion on all the certified hospitals by the Joint Commission on Accreditation of Health Care Organizations to refer patients of domestic abuse to authorities after treating them [<span>3</span>].</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000168/pdfft?md5=0263ba1128c8e267657bbc317fe3b81e&pid=1-s2.0-S2666990024000168-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140274269","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}

{"title":"Advancing clinical decision support: The role of artificial intelligence across six domains","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy ,&nbsp;Usman Iqbal","doi":"10.1016/j.cmpbup.2024.100142","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100142","url":null,"abstract":"<div><h3>Background</h3><p>Artificial Intelligence (AI) is a transformative force in clinical decision support (CDS) systems within healthcare. Its emergence, fuelled by the growing volume and diversity of healthcare data, offers significant potential in patient care, diagnosis, treatment, and health management. This study systematically reviews AI's role in enhancing CDS across six domains, underscoring its impact on patient outcomes and healthcare efficiency.</p></div><div><h3>Methods</h3><p>A four-step systematic review was conducted, involving a comprehensive literature search, application of inclusion and exclusion criteria, data extraction and synthesis, and analysis. Sources included PubMed, Embase, and Google Scholar, with papers published in English since 2019. Selected studies focused on AI's application in CDS, with 32 papers ultimately reviewed.</p></div><div><h3>Results</h3><p>The review identified six AI CDS domains: Data-Driven Insights and Analytics, Diagnostic and Predictive Modelling, Treatment Optimisation and Personalised Medicine, Patient Monitoring and Telehealth Integration, Workflow and Administrative Efficiency, and Knowledge Management and Decision Support. Each domain is crucial in improving various aspects of CDS, from enhancing diagnostic accuracy to optimising resource management. AI's capabilities in EHR analysis, predictive analytics, personalised treatment, and telehealth demonstrate its critical role in advancing healthcare.</p></div><div><h3>Discussion</h3><p>AI significantly enhances healthcare by improving diagnostic precision, predictive capabilities, and administrative efficiency. It facilitates personalised medicine, remote monitoring, and evidence-based decision-making. However, challenges such as data privacy, ethical considerations, and integration with existing systems persist. This requires collaboration among technologists, healthcare professionals, and policymakers.</p></div><div><h3>Conclusion</h3><p>AI is revolutionising healthcare by enhancing CDS in several domains, contributing to more efficient, effective, and patient-centric care. However, it should complement, not replace, human expertise. Future directions include ethical AI development, continuous professional development for healthcare personnel, and collaborative efforts to address challenges. This approach ensures AI's potential is fully harnessed, leading to a synergistic blend of technology and human care.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000090/pdfft?md5=aaaa8b38d130717ba82fc96ec2dea81f&pid=1-s2.0-S2666990024000090-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139908302","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}

{"title":"Fostering digital health literacy to enhance trust and improve health outcomes","authors":"Kristine Sørensen","doi":"10.1016/j.cmpbup.2024.100140","DOIUrl":"10.1016/j.cmpbup.2024.100140","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000077/pdfft?md5=5fddb8d7de20b2508c53b5099afe8495&pid=1-s2.0-S2666990024000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139874509","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}

{"title":"Acknowledgments to our reviewers in 2023","authors":"","doi":"10.1016/j.cmpbup.2024.100138","DOIUrl":"10.1016/j.cmpbup.2024.100138","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000053/pdfft?md5=009e4e2d12849f38ea7767ad3627e409&pid=1-s2.0-S2666990024000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139639048","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}

{"title":"Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy","doi":"10.1016/j.cmpbup.2024.100141","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100141","url":null,"abstract":"<div><h3>Introduction</h3><p>Diabetes, a major cause of premature mortality and complications, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the prevention, diagnosis, and management of diabetes, highlighting the potential for personalised and proactive healthcare.</p></div><div><h3>Methods</h3><p>A structured four-step method was used, including extensive literature searches, specific inclusion and exclusion criteria, data extraction from selected studies focusing on AI's role in diabetes, and thorough analysis to identify specific domains and functions where AI contributes significantly.</p></div><div><h3>Results</h3><p>Through examining 43 experimental studies, AI has been identified as a transformative force across eight key domains in diabetes care: 1) Diabetes Management and Treatment, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancing Clinical Decision-Making, and 8) Patient Engagement and Self-Management. Each domain showcases AI's potential to revolutionize care, from personalizing treatment plans and improving diagnostic accuracy to enhancing patient engagement and predictive healthcare.</p></div><div><h3>Discussion</h3><p>AI's integration into diabetes care offers personalised, efficient, and proactive solutions. It enhances care accuracy, empowers patients, and provides better understanding of diabetes management. However, the successful implementation of AI requires continued research, data security, interdisciplinary collaboration, and a focus on patient-centered solutions. Education for healthcare professionals and regulatory frameworks are also crucial to address challenges like algorithmic bias and ethics.</p></div><div><h3>Conclusion and Recommendations</h3><p>AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring data security, fostering interdisciplinary collaboration, and prioritizing patient-centered solutions. Regular monitoring and evaluation are essential to adjust strategies and understand long-term impacts, ensuring AI's ethical and effective integration into healthcare.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000089/pdfft?md5=df60e0f94e7b010da1e695a8a5bf5d47&pid=1-s2.0-S2666990024000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749610","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}

{"title":"Numerical study on normal lung sounds in bronchial airways under different breathing intensities","authors":"Huiqiang Li ,&nbsp;Xiaozhao Li ,&nbsp;Juntao Feng","doi":"10.1016/j.cmpbup.2024.100154","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100154","url":null,"abstract":"<div><h3>Background</h3><p>Due to the complexity of airways and the limitation of experiments, the production mechanism of the lung sounds in airways has not been fully understood, which often confuses diagnosis.</p></div><div><h3>Method</h3><p>A 3D geometrical model of human airways (G5-G8) has been developed based on Weibel's model. Simulation on transient airflow and the noise production during exhalation under different breathing intensities (<em>Q</em> = 15, 30, 45, 60, 75, 90 L/min) has been carried out with Direct Noise Computation (DNC) and Ffowcs Williams-Hawkings (FW-H) method.</p></div><div><h3>Results</h3><p>(1) The junctions between airways are most likely to produce lung sounds, and the peak value is located in the junction between G7 and G6 at the middle of exhalation (about 0.75 s). (2) With the increase in breathing intensity, the average sound pressure level first increases, reaches the peak value at 70–75 L/min, and then drops. (3) Higher breathing intensity is helpful to produce the feature of wheezing, namely a comparatively higher sound pressure level in the range of 200–500 Hz. Moreover, this feature is prominent with the increase in breathing intensity.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000211/pdfft?md5=6b1cdf9b1b9d99f91f6def14fe7bffab&pid=1-s2.0-S2666990024000211-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140604780","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}