PLOS digital health最新文献

{"title":"Digital technology as a tool to provide social support to individuals with cancer in low- and middle-income countries: A scoping review.","authors":"Hallie Dau, Fazila Kassam, Beth A Payne, Hana Miller, Gina Ogilvie","doi":"10.1371/journal.pdig.0000609","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000609","url":null,"abstract":"<p><p>Cancer is a rising cause of morbidity and mortality in low- and middle-income countries (LMICs). Individuals diagnosed with cancer in LMICs often have limited access to cancer prevention, diagnosis, and treatment services. Digital technologies, such as the Internet and mobile phones, could be used to provide support to individuals with cancer in a more accessible way. The goal of this scoping review is to understand how digital technology is being utilized by individuals with cancer for social support in LMICs. Four electronic databases were searched up to June 2024 to identify studies that reported on the use of digital technology for cancer social support in LMICs. Articles were included if they were published in English, included adults diagnosed with any type of cancer, and reported the use of digital technology for social support. Study characteristics, population demographics, and technological interventions reported were extracted. In all, 15 articles from 12 studies were included in the scoping review. Only four countries utilized digital technology for social support: China, Iran, Kenya, and Serbia. The most common cancer type reported was breast. Online health communities, Internet-based resources, mobile applications, and telecommunication were the four digital technologies reported. Overall, the articles demonstrated that the use of digital technology for social support can be beneficial for individuals diagnosed with cancer in LMICs. We found that digital technology may improve quality of life, reduce anxiety and depression, and allow individuals to connect with other individuals diagnosed with cancer. We concluded that there is a limited understanding of how digital technology can be used to support individuals with cancer in LMICs. Future research is needed to explore how digital technology can be utilized by underrepresented regions to offer avenues of support for regionally common cancer types such as cervical.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000609"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999895","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":"Addressing data management and analysis challenges in viral genomics: The Swiss HIV cohort study viral next generation sequencing database.","authors":"Marius Zeeb, Paul Frischknecht, Suraj Balakrishna, Lisa Jörimann, Jasmin Tschumi, Levente Zsichla, Sandra E Chaudron, Bashkim Jaha, Kathrin Neumann, Christine Leemann, Michael Huber, Karoline Leuzinger, Huldrych F Günthard, Karin J Metzner, Roger D Kouyos","doi":"10.1371/journal.pdig.0000825","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000825","url":null,"abstract":"<p><p>Numerous HIV related outcomes can be determined on the viral genome, for example, resistance associated mutations, population transmission dynamics, viral heritability traits, or time since infection. Viral sequences of people with HIV (PWH) are therefore essential for therapeutic and research purposes. While in the first three decades of the HIV pandemic viral genomes were mainly sequenced using Sanger sequencing, the last decade has seen a shift towards next-generation sequencing (NGS) as the preferred method. NGS can achieve near full length genome sequence coverage and simultaneously, it accurately encapsulates the within-host diversity by characterizing HIV subpopulations. NGS opens new avenues for HIV research, but it also presents challenges concerning data management and analysis. We therefore set up the Swiss HIV Cohort Study Viral NGS Database (SHCND) to address key issues in the handling of NGS data including high loads of raw- and processed NGS data, data storage solutions, downstream application of sophisticated bioinformatic tools, high-performance computing resources, and reproducibility. The database is nested within the Swiss HIV Cohort Study (SHCS) and the Zurich Primary HIV Infection Cohort Study (ZPHI), which together enrolled 21,876 PWH since 1988 and include a biobank dating back to the early nineties. Since its initiation in 2018, the SHCND accumulated NGS sequences (plasma and proviral origin) of 5,178 unique PWH. We here describe the design, set-up, and use of this NGS database. Overall, the SHCND has contributed to several research projects on HIV pathogenesis, treatment, drug resistance, and molecular epidemiology, and has thereby become a central part of HIV-genomics research in Switzerland.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000825"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013989","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":"Empowering sustainable futures: The role of digital citizenship for health in healthcare and environmental resilience.","authors":"Sohayla Eldeeb, Salman Fitrat Khan, Lynn-Everdene Bufu Akisarl, Charlotte Anne-Marie Helena Thibault, Maryam Sherif, El Ghali Ouali Alami, Alfredo Lorenzo Recio Sablay, Leonardo Bolstad Reo Aoki","doi":"10.1371/journal.pdig.0000828","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000828","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000828"},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061541","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":"A BLE based turnkey indoor positioning system for mobility assessment in aging-in-place settings.","authors":"Haixin Wang, Guha Ganesh, Michael Zon, Oishee Ghosh, Henry Siu, Qiyin Fang","doi":"10.1371/journal.pdig.0000774","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000774","url":null,"abstract":"<p><p>Indoor positioning systems (IPS) can be used to measure mobility at home, which is an important indicator for health and wellbeing. In this work, we designed and developed a Bluetooth Low Energy (BLE) based IPS that identifies individual users; does not require floorplans; and allows the end-users to perform on-site install/setup. Additionally, a dynamic calibration process is implemented to learn room boundaries based on the distribution of the BLE signal strength. The functionality and performance of IPS system were validated in two residential home settings. Raw and filtered relative signal strength indicators (RSSI) and variability of RSSI were measured during testing. Room detection was determined by comparing a user input location (ground truth) with the IPS detected location for over 300 positions. The IPS produced a 96% accuracy of correctly detecting room location when using RSSI and the additional motion sensors. The use of PIR motion and ultrasonic sensors information provided improved validity when compared with existing indoor positioning systems. The ease of use and modular design of this IPS makes it a good choice for implementation in larger scale smart healthcare monitoring systems.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000774"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051804","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":"Correction: Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings.","authors":"Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones","doi":"10.1371/journal.pdig.0000841","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000841","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1371/journal.pdig.0000491.].</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000841"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030670","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":"A comparative study of explainability methods for whole slide classification of lymph node metastases using vision transformers.","authors":"Jens Rahnfeld, Mehdi Naouar, Gabriel Kalweit, Joschka Boedecker, Estelle Dubruc, Maria Kalweit","doi":"10.1371/journal.pdig.0000792","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000792","url":null,"abstract":"<p><p>Recent advancements in deep learning have shown promise in enhancing the performance of medical image analysis. In pathology, automated whole slide imaging has transformed clinical workflows by streamlining routine tasks and diagnostic and prognostic support. However, the lack of transparency of deep learning models, often described as black boxes, poses a significant barrier to their clinical adoption. This study evaluates various explainability methods for Vision Transformers, assessing their effectiveness in explaining the rationale behind their classification predictions on histopathological images. Using a Vision Transformer trained on the publicly available CAMELYON16 dataset comprising of 399 whole slide images of lymph node metastases of patients with breast cancer, we conducted a comparative analysis of a diverse range of state-of-the-art techniques for generating explanations through heatmaps, including Attention Rollout, Integrated Gradients, RISE, and ViT-Shapley. Our findings reveal that Attention Rollout and Integrated Gradients are prone to artifacts, while RISE and particularly ViT-Shapley generate more reliable and interpretable heatmaps. ViT-Shapley also demonstrated faster runtime and superior performance in insertion and deletion metrics. These results suggest that integrating ViT-Shapley-based heatmaps into pathology reports could enhance trust and scalability in clinical workflows, facilitating the adoption of explainable artificial intelligence in pathology.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000792"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030668","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":"Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis.","authors":"Andrew J Codlin, Luan N Q Vo, Thang P Dao, Rachel J Forse, Ha T M Dang, Lan H Nguyen, Hoa B Nguyen, Luong V Dinh, Kristi Sidney Annerstedt, Johan Lundin, Knut Lönnroth","doi":"10.1371/journal.pdig.0000813","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000813","url":null,"abstract":"<p><p>New versions of computer-aided detection (CAD) software for chest X-ray (CXR) interpretation during tuberculosis (TB) screening are regularly released which purport to have incremental performance gains. No studies have independently assessed differences in software performance between the World Health Organization recommended INSIGHT CXR software (Lunit, South Korea). A well-characterized Digital Imaging and Communications in Medicine (DICOM) test library was compiled using data from a community-based TB screening initiative in Ho Chi Minh City, Viet Nam. The performance of Lunit CAD software versions 3.1.0.0 and 3.9.0.1 (newer version) were compared by measuring the area under the receiver operating characteristic curve (AUC), stratified by key clinical and demographic variables and using Xpert MTB/RIF Ultra (Ultra) test results as the reference standard. Median abnormality scores were compared using the Wilcoxon signed-rank test and performance characteristics were compared at clinically-relevant cut-off thresholds (e.g., 90% sensitivity) between the versions. The DICOM test library contained 2,708 participants, of whom 10.3% had a Mycobacterium tuberculosis (MTB) positive Ultra test result. The newer software version had a significantly higher AUC than its predecessor (AUC 0.76 vs 0.78, p = 0.029), and performed significantly better among people with a past history of TB (AUC 0.67 vs 0.73, p = 0.003), older individuals (0.75 vs 0.77, p = 0.040) and males (0.73 vs 0.76, p = 0.008). When using an cut-off threshold optimized for the older software version, the newer software was significantly less accurate than its predecessors. However, when the cut-off threshold was re-calibrated, there were no significant differences in sensitivity and specificity between the software versions. Although INSIGHT CXR v3.9.0.1 has some significantly improved performance characteristics compared to its predecessor, further studies should assess how these performance differences translate into real-world improvements during TB screening. As new CAD software versions are rolled out, cut-off thresholds must be re-calibrated to ensure the continued accuracy of CXR interpretation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000813"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047813","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":"Association of the child opportunity index with in-hospital mortality and persistence of organ dysfunction at one week after onset of Phoenix Sepsis among children admitted to the pediatric intensive care unit with suspected infection.","authors":"Ronald Moore, Daniela Chanci, Stephanie R Brown, Michael J Ripple, Natalie R Bishop, Jocelyn Grunwell, Rishikesan Kamaleswaran","doi":"10.1371/journal.pdig.0000763","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000763","url":null,"abstract":"<p><p>The social determinants of health (SDoH) are fundamental factors that contribute to overall health and health-related outcomes. Children living in lower socioeconomic areas have a higher risk of critical illness and worse outcomes compared to children living in more socioeconomically advantaged areas. In this work, we determine whether the Child Opportunity Index (COI 3.0), a multi-dimensional child-specific indicator of neighborhood environment, is associated with in-hospital mortality or persistence of a Phoenix Sepsis Score  ≥  2 at one week following Phoenix Sepsis onset in children admitted to pediatric intensive care units (PICUs) with suspected infection. We performed a retrospective cohort analysis of 63,824 patients with suspected or confirmed infection admission diagnosis in two PICUs in Atlanta, Georgia with a Georgia residential address that could be geocoded and linked to a census tract. The primary outcome was the composite of in-hospital mortality or persistence of a Phoenix Sepsis Score  ≥  2 at one week following Phoenix Sepsis onset. Model performance measures of interest were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Models developed with electronic medical record (EMR) data using Egleston (EG) or Scottish Rite (SR) as the training site achieved AUROCs of 0.81-0.84 (95% CI range: 0.8-0.85) and 0.82-0.82 (95% CI range: 0.81-0.83) and AUPRCs of 0.59-0.68 (95% CI range: 0.58-0.69) and 0.62-0.64 (95% CI range: 0.61-0.65) respectively. Despite significant differences in COI 3.0 characteristics and overall in-hospital mortality of children with Phoenix suspected infection between the EG and SR PICUs, the addition of COI 3.0 did not improve the overall model performance metrics. While children admitted to both PICUs were more often from COI 3.0 neighborhoods in the lowest two quintiles, these neighborhood features had less of an impact on the model's predictive performance compared to patient physiologic and biologic features available in the EMR.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000763"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052911","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":"AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers.","authors":"Gregor Donabauer, Anca Rath, Aila Caplunik-Pratsch, Anja Eichner, Jürgen Fritsch, Martin Kieninger, Susanne Gaube, Wulf Schneider-Brachert, Udo Kruschwitz, Bärbel Kieninger","doi":"10.1371/journal.pdig.0000821","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000821","url":null,"abstract":"<p><p>The isolation of affected patients and intensified infection control measures are used to prevent nosocomial transmission of vancomycin-resistant enterococci (VRE), but early detection of VRE carriers is needed. However, there are still no standard screening criteria for VRE, which poses a significant threat to patient safety. Our study aimed to develop and evaluate an artificial intelligence (AI)-based approach for identifying and predicting of at-risk patients who could assist infection prevention and control staff through a human-in-the-loop approach. We used data from 8,372 patients, combining more than 125,000 movements within our hospital with patient-related information to create time-dependent graph sequences and applied graph neural networks (GNNs) to classify patients as VRE carriers or noncarriers. Our model achieves a macro F1 score of 0.880 on the task (sensitivity of 0.808, specificity of 0.942). The parameters with the strongest impact on the prediction are the codes for clinical diagnosis (ICD) and operations/procedures (OPS), which are integrated as high-dimensional patient node features in our model. We demonstrate that modeling a \"living\" hospital with a GNN is a promising approach for the early detection of potential VRE carriers. This proves that AI-based tools combining heterogeneous information types can predict VRE carriage with high sensitivity and could therefore serve as a promising basis for future automated infection prevention control systems. Such systems could help enhance patient safety and proactively reduce nosocomial transmission events through targeted, cost-efficient interventions. Moreover, they could enable a more effective approach to managing antimicrobial resistance.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000821"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065354","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":"Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.","authors":"Vitaly Lorman, L Charles Bailey, Xing Song, Suchitra Rao, Mady Hornig, Levon Utidjian, Hanieh Razzaghi, Asuncion Mejias, John Erik Leikauf, Seuli Bose Brill, Andrea Allen, H Timothy Bunnell, Cara Reedy, Abu Saleh Mohammad Mosa, Benjamin D Horne, Carol Reynolds Geary, Cynthia H Chuang, David A Williams, Dimitri A Christakis, Elizabeth A Chrischilles, Eneida A Mendonca, Lindsay G Cowell, Lisa McCorkell, Mei Liu, Mollie R Cummins, Ravi Jhaveri, Saul Blecker, Christopher B Forrest","doi":"10.1371/journal.pdig.0000747","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000747","url":null,"abstract":"<p><p>Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients' clinical histories to then identify groups of patients with similar presentations. The results indicate that cardiorespiratory presentations are most common (present in 54% of patients) followed by subphenotypes marked (in decreasing order of frequency) by musculoskeletal pain, neuropsychiatric conditions, gastrointestinal symptoms, headache, and fatigue.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 4","pages":"e0000747"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058558","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}