Computer methods and programs in biomedicine update最新文献

{"title":"Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models","authors":"Sandeep Reddy ,&nbsp;Supriya Roy ,&nbsp;Kay Weng Choy ,&nbsp;Sourav Sharma ,&nbsp;Karen M Dwyer ,&nbsp;Chaitanya Manapragada ,&nbsp;Zane Miller ,&nbsp;Joy Cheon ,&nbsp;Bahareh Nakisa","doi":"10.1016/j.cmpbup.2024.100160","DOIUrl":"10.1016/j.cmpbup.2024.100160","url":null,"abstract":"<div><h3>Background</h3><p>Chronic kidney disease (CKD) poses a major global public health burden, with over 700 million affected. Early identification of those in whom the disease is likely to progress enables timely therapeutic interventions to delay advancement to kidney failure.</p></div><div><h3>Methods</h3><p>This study developed explainable machine learning models leveraging pathology data to accurately predict CKD trajectory, targeting improved prognostic capability even in early stages using limited datasets. Key variables used in this study include age, gender, most recent estimated glomerular filtration rate (eGFR), mean eGFR, and eGFR slope over time prior to the incidence of kidney failure. Supervised classification modelling techniques included decision tree and random forest algorithms selected for interpretability. Internal validation on an Australian tertiary centre cohort (<em>n</em> = 706; 353 with kidney failure and 353 without) achieved exceptional predictive accuracy. To address the inherent class imbalance, centroid-cluster-based under-sampling was applied to the Australian dataset. For external validation, the model was applied to a dataset (<em>n</em> = 597 adults) sourced from a Japanese CKD registry. Transfer learning was subsequently employed by fine-tuning machine learning models on 15 % of the external dataset (<em>n</em> = 89) before evaluating the remaining 508 patients.</p></div><div><h3>Results</h3><p>Internal validation achieved exceptional predictive accuracy, with the area under the receiver operating characteristic curve (ROC-AUC) reaching 0.94 and 0.98 on the binary task of predicting kidney failure for decision tree and random forest, respectively. External validation demonstrated performant results with an ROC-AUC of 0.88 for the decision tree and 0.93 for the random forest model. Decision tree model analysis revealed the most recent eGFR and eGFR slope as the most informative variables for prediction in the Japanese cohort.</p></div><div><h3>Conclusion</h3><p>The research highlights the utility of deploying explainable machine learning techniques to forecast CKD trajectory even in the early stages utilising limited real-world datasets.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"6 ","pages":"Article 100160"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000272/pdfft?md5=990fdaf12f5d28d2cae65af47c229654&pid=1-s2.0-S2666990024000272-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012917","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":"DiGAN Breakthrough: Advancing diabetic data analysis with innovative GAN-based imbalance correction techniques","authors":"Puyang Zhao ,&nbsp;Xinhui Liu ,&nbsp;Zhiyi Yue ,&nbsp;Qianyu Zhao ,&nbsp;Xinzhi Liu ,&nbsp;Yuhui Deng ,&nbsp;Jingjin Wu","doi":"10.1016/j.cmpbup.2024.100152","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100152","url":null,"abstract":"<div><p>In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000193/pdfft?md5=465f3cbca9e1cb295e9d2d56ae5c71e1&pid=1-s2.0-S2666990024000193-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140552303","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":"Role-playing recovery in social virtual worlds: Adult use of child avatars as PTSD therapy","authors":"Donna Davis ,&nbsp;Stephen Alexanian","doi":"10.1016/j.cmpbup.2023.100129","DOIUrl":"10.1016/j.cmpbup.2023.100129","url":null,"abstract":"<div><p>A study of a community of people with disabilities in a virtual world sheds new light on an important issue of health literacy that has to date remained underreported in the current body of research. Participants revealed a community of individuals who are adults role-playing <em>via</em> child avatars as a coping and recovery mechanism for childhood trauma. One case follows the experience of a woman who role plays an adopted child of a caring adult while another attempts to recreate different ages of herself to unpack past trauma and find therapeutic healing. This phenomenon, as well as both its risks and opportunities, are examined with important considerations for the future of digital mental health support for people who have experienced abuse as children. Researchers, policy makers, and mental health professionals are encouraged to consider the role of social virtual worlds in the future of telemedicine for PTSD therapy.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266699002300037X/pdfft?md5=aee6f0c42be284ed2238561d6e7b28da&pid=1-s2.0-S266699002300037X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139301410","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":"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}