PLOS digital health最新文献

{"title":"Development of a digital, self-guided return-to-work toolkit for stroke survivors and employers using intervention mapping.","authors":"Kristelle Craven, Jain Holmes, Jade Kettlewell, Kathryn Radford","doi":"10.1371/journal.pdig.0000971","DOIUrl":"10.1371/journal.pdig.0000971","url":null,"abstract":"<p><p>Stroke incidence is rising among working-age adults in high-income countries. Employers often lack knowledge and skills to support return-to-work post-stroke. In the United Kingdom, nearly 40% of stroke survivors stop working. Vocational rehabilitation is rarely accessible, and self-guided resources often lack tools to support practical application. This study developed a self-guided return-to-work toolkit for stroke survivors and employers. Steps 1-4 of the six-step Intervention Mapping approach were followed. Intervention goal, content, and design were informed by three online workshops with employers (n = 12) and meetings with an advisory group (n = 20), including stroke charity and trade union representatives, stroke survivors, healthcare professionals, and experts in human resources and vocational rehabilitation. Theory-based pretesting (task-based usability review, advisory group discussions) was shaped by prototype review with advisory group members, including employers (n = 4), stroke survivors (n = 7), and healthcare professionals (n = 4). Framework analysis was used to structure feedback related to acceptability, ease of use/learnability, accessibility, inclusivity, perceived usefulness, and technical or environmental issues. No personal data were analysed. The toolkit aims to empower stroke survivors and employers to plan and manage a sustainable return-to-work post-stroke. It exists as two Xerte eLearning packages, with accessibility features such as screen reader compatibility and keyboard navigation. The toolkit contains theory- and evidence-based content for a) stroke survivors and b) employers, and includes downloadable PDF tools. Stroke survivor-focused content provides guidance on identifying and disclosing support needs to employers. Employer-focused content guides employers in increasing and maintaining understanding of stroke survivors' work abilities, and implementing and monitoring tailored reasonable adjustments. Pretesting indicated the toolkit is comprehensive, empowering, and fosters open communication, offering key information and practical tools. Minor refinements and technical improvements were suggested. This toolkit addresses a gap in return-to-work guidance in the United Kingdom. Refinement, testing, and evaluation in real-world settings are needed.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000971"},"PeriodicalIF":7.7,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12327610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144796343","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":"Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict individuals at risk of microvascular disease.","authors":"Viswanathan Baskar, Mani Arun Vignesh, Sumanth C Raman, Arun Jijo, Bhavadharini Balaji, Nico Steckhan, Lena Maria Klara Roth, Moneeza K Siddiqui, Saravanan Jebarani, Ranjit Unnikrishnan, Viswanathan Mohan, Ranjit Mohan Anjana","doi":"10.1371/journal.pdig.0000702","DOIUrl":"10.1371/journal.pdig.0000702","url":null,"abstract":"<p><strong>Background: </strong>Previous research has identified four distinct endotypes of type 2 diabetes in Asian Indians, which include Severe Insulin Deficient Diabetes (SIDD), Combined Insulin Resistant and Deficient Diabetes (CIRDD), Insulin Resistance and Obese Diabetes (IROD), and Mild Age-related Diabetes (MARD). DIANA (Diabetes Novel Subgroup Assessment) is an online precision medicine tool that can predict endotype membership of type 2 diabetes and individual risk for retinopathy and nephropathy.</p><p><strong>Methodology: </strong>The DIANA tool determines subgroup membership using a machine learning model (support vector machine) on T2D subgroups in the Asian Indian population. We used a support vector machine (SVM) model to classify type 2 diabetes patient endotypes, and the model is trained based on k-fold cross-validation. Its performance was compared with an algorithm determined based on conditional pre-determined cut-offs and weights for each clinical feature [age at diagnosis, BMI, waist, HbA1c, Serum Triglycerides, HDL-Cholesterol, (C-peptide fasting, C-peptide stimulated) - optional. This study employed local interpretable model-agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP) to demystify the endotype prediction model. A random forest model was built to assess an individual's risk for nephropathy and retinopathy based on individual risk algorithms.</p><p><strong>Findings: </strong>The SVM model has relatively high accuracy, specificity, sensitivity, and precision values compared to conditional pre-determined cut-offs 98% vs 63.6%, 99.8% vs 88%, 98.5% vs 65.1%, and 98.7% vs 63.4%. Clinician face value validation of the prediction by the SVM model reported an accuracy, specificity, sensitivity and precision compared to conditional pre-determined cut-offs 97% vs 85%, 95.3% vs 63%, 95.8% vs 73%, and 98.9% vs 66.9%. Additionally, our study demonstrated the impact of features on ML models through LIME and SHAP analyses. The accuracy of the random forest risk prediction model for nephropathy and retinopathy was 89.6% (p < 0.05) and 78.4% (p < 0.05), respectively.</p><p><strong>Conclusion: </strong>We conclude that, DIANA is an accurate, clinically explainable AI tool that clinicians can use to make informed decisions on risk assessment and provide precision management to individuals with new-onset type 2 diabetes.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000702"},"PeriodicalIF":7.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12324136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790967","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":"Longitudinal image-based prediction of surgical intervention in infants with hydronephrosis using deep learning: Is a single ultrasound enough?","authors":"Adree Khondker, Stanley Bryan Z Hua, Jethro C C Kwong, Kunj Sheth, Daniel Alvarez, Kyla N Velaer, John Weaver, Alice Xiang, Gregory E Tasian, Armando J Lorenzo, Anna Goldenberg, Mandy Rickard, Lauren Erdman","doi":"10.1371/journal.pdig.0000939","DOIUrl":"10.1371/journal.pdig.0000939","url":null,"abstract":"<p><p>The potential of deep learning to predict renal obstruction using kidney ultrasound images has been demonstrated. However, these image-based classifiers have incorporated information using only single-visit ultrasounds. Here, we developed machine learning (ML) models incorporating ultrasounds from multiple clinic visits for hydronephrosis to generate a hydronephrosis severity index score to discriminate patients into high versus low risk for needing pyeloplasty and compare these against models trained with single clinic visit data. We included patients followed for hydronephrosis from three institutions. The outcome of interest was low risk versus high risk of obstructive hydronephrosis requiring pyeloplasty. The model was trained on data from Toronto, ON and validated on an internal holdout set, and tested on an internal prospective set and two external institutions. We developed models trained with single ultrasound (single-visit) and multi-visit models using average prediction, convolutional pooling, long-short term memory and temporal shift models. We compared model performance by area under the receiver-operator-characteristic (AUROC) and area under the precision-recall-curve (AUPRC). A total of 794 patients were included (603 SickKids, 102 Stanford, and 89 CHOP) with a pyeloplasty rate of 12%, 5%, and 67%, respectively. There was no significant difference in developing single-visit US models using the first ultrasound vs. the latest ultrasound. Comparing single-visit vs. multi-visit models, all multi-visit models fail to produce AUROC or AUPRC significantly greater than single-visit models. We developed ML models for hydronephrosis that incorporate multi-visit inference across multiple institutions but did not demonstrate superiority over single-visit inference. These results imply that the single-visit models would be sufficient in aiding accurate risk stratification from single, early ultrasound images.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000939"},"PeriodicalIF":7.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144786152","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":"Healthy futures online: A rights-based perspective on digital health literacy for children and youth.","authors":"Jia Lin, Maria Fernanda Vargas Herrera, Alice Sofia Rubio, Taty Diego, Marie-Eve Turcotte, Alexanne Dumas, Malika Saher, Bertrand Lebouché, Esli Osmanlliu","doi":"10.1371/journal.pdig.0000946","DOIUrl":"10.1371/journal.pdig.0000946","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000946"},"PeriodicalIF":7.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144786151","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 in clinical diagnostics: Is overreliance eroding clinical expertise?","authors":"Fawad A Khan","doi":"10.1371/journal.pdig.0000959","DOIUrl":"10.1371/journal.pdig.0000959","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000959"},"PeriodicalIF":7.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144786146","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":"Postoperative complication management: How do large language models measure up to human expertise?","authors":"Sophie-Caroline Schwarzkopf, Jean-Paul Bereuter, Mark Enrik Geissler, Jürgen Weitz, Marius Distler, Fiona R Kolbinger","doi":"10.1371/journal.pdig.0000933","DOIUrl":"10.1371/journal.pdig.0000933","url":null,"abstract":"<p><p>Managing postoperative complications is an essential part of surgical care and largely depends on the medical team's experience. Large Language Models (LLMs) have demonstrated immense potential in supporting medical professionals. To evaluate the potential of LLMs in surgical patient care, we compared the performance of three state-of-the-art LLMs in managing postoperative complications to that of a panel of medical professionals based on six postsurgical patient cases. Six realistic postoperative patient cases were queried using GPT-3, GPT-4, and Gemini-Advanced and presented to human surgical caregivers. Humans and LLMs provided a triage assessment, an initial suspected diagnosis, and an acute management plan, including initial diagnostic and therapeutic measures. Responses were compared based on medical contextual correctness, coherence, and completeness. In comparison to human caregivers, GPT-3 and GPT-4 possess considerable competencies in correctly identifying postoperative complications (humans: 76.3% vs. GPT-3: 75.0% vs. GPT-4: 96.7%, p = 0.47) as well as triaging patients accordingly (humans: 84.8% vs. GPT-3: 50% vs. GPT-4: 38.3%, p = 0.19). With regard to diagnostic and therapeutic management of postoperative complications, GPT-3 and GPT-4 provided comprehensive management plans. Gemini-Advanced often provided no diagnostic or therapeutic recommendations and censored its outputs. In summary, LLMs can accurately interpret postoperative care scenarios and provide comprehensive management recommendations. These results showcase the improvements in LLMs performance with regard to postoperative surgical use cases and provide evidence for their potential value to support and augment surgical routine care.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000933"},"PeriodicalIF":7.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12316209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144765881","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":"Telemonitoring starting in the emergency department as an alternative to acute hospital admission: A prospective pilot study focusing on patient preferences and first experience.","authors":"Noortje Zelis, Dewa Westerman, Anouk Schevers, Nicole V Eldik, Patricia M Stassen","doi":"10.1371/journal.pdig.0000962","DOIUrl":"10.1371/journal.pdig.0000962","url":null,"abstract":"<p><p>Telemonitoring at home may be used to reduce acute hospital admissions via the emergency department (ED), but experience in this setting is scarce. We performed a pilot study to investigate the perspectives and experiences of ED patients and care professionals with telemonitoring, started in the ED and used as potential an alternative to acute hospital admission. In this prospective pilot study, we asked medical ED patients for their perspectives on home monitoring. Suitability for homemonitoring was assessed by ED patients and care professionals. In a subset of patients, we started and evaluated telemonitoring. In total, 98 patients answered a questionnaire. The facilitators for telemonitoring as an alternative to hospital admission were: guaranteed admission if necessary (indicated by 96.9% of patients), possibility to contact the treatment team 24/7 (by 90.8%), and presence of someone to watch over the patient (by 72.4%). Main barriers for telemonitoring as an alternative care form were: need for treatment that could not be provided at home, feeling too severely ill, and judging it unsafe to return home. In total, 11.2% of ED patients indicated that hospital admission could be avoided using telemonitoring, while another 6.1% thought this might be possible. Professionals judged fewer patients capable of being sent home with telemonitoring (physicians: 7.2% and 6.1%, resp.; nurses: 10.4% and 4.2%, resp.). Agreement on the capability of patients to be sent home with telemonitoring between patients and professionals was slight-fair. All telemonitored patients were satisfied with the ease of use and comfort of the system, which gave most patients reassurance and was considered an alternative to admission. In conclusion, telemonitoring at home was seen as an alternative to admission in a substantial proportion of medical ED patients. Facilitators for telemonitoring indicated by patients were guaranteed admission if telemonitoring failed and the possibility to contact the treatment team 24/7, while indicated barriers were related to disease severity and lack of someone to watch over the patient. Telemonitoring in acute care may serve as a potential alternative to admissions if facilitators are met.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000962"},"PeriodicalIF":7.7,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12312925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762584","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":"Citizens' feedback on health service and the responses of health authorities of Bangladesh: An analysis of the Grievance Redress System.","authors":"Md Abdullah Saeed Khan, Md Toufiq Hassan Shawon, Atonu Rabbani","doi":"10.1371/journal.pdig.0000967","DOIUrl":"10.1371/journal.pdig.0000967","url":null,"abstract":"<p><p>Developing nations like Bangladesh face unique challenges in healthcare delivery system due to resource constraints, making it crucial to assess the responsiveness of healthcare authorities to citizen feedback. This study utilized data from the Ministry of Health and Family Welfare's (MOHFW) Grievance Redress System (GRS) in Bangladesh, collected from January 1st to August 8th, 2023. A total of 11,604 anonymous messages received from health service takers were retrieved and analyzed. Kaplan-Meier analysis, and log-rank tests were conducted to assess feedback response times. Feedback were mainly complaints (60.33%), followed by compliments (22.03%) and suggestions (14.48%). Complaints were primarily related to the health workforce, infrastructure, and service utilization. Responses included forwarding (67.02%) to the relevant department, closure (30.12%), resolution (2.55%), pending (0.05%), and overdue (0.25%). The median response time was 3.48 hours (IQR: 1.26 - 13.00). Kaplan-Meier analysis revealed that moderate and major complaints were significantly less likely to be resolved than minor complaints (p < 0.001). The status and responsiveness of the grievance redress process of the healthcare delivery system in Bangladesh highlighted in this study can be used to plan the enhancement of the redress system and, thereby, improve health service.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000967"},"PeriodicalIF":7.7,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755319","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":"Feature representation in analysing childhood vaccination defaulter risk predictors: A scoping review of studies in low-resource settings.","authors":"Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Solomon Nyame, Dominic Asamoah, Kwaku Poku Asante, James Ben Hayfron-Acquah","doi":"10.1371/journal.pdig.0000965","DOIUrl":"10.1371/journal.pdig.0000965","url":null,"abstract":"<p><p>Childhood vaccination saves millions of lives yearly, yet over a million children in low-and middle-income countries die from vaccine-preventable diseases each year. Predicting childhood vaccination defaulter risk with analytical models requires understanding how to represent different individual demographics, community structures, and environmental factors that feed input data. This review explores features for analysing childhood vaccination defaulter risk in low-resource settings with a focus on feature encoding, engineering and representation. Articles published from 2018 to January 2025 were searched using PubMed, Google Scholar, ACM Digital Library, and references from the searched articles. Search was limited to low- and middle-income countries, focusing on African countries. We included studies that utilised either statistics or machine learning for analysis. Of the 4,174 articles retrieved, 55 were eligible, 41 were then excluded after full-text review, and 4 were added from references. Cross-cutting features included maternal education and health service utilisation. Novel features included community rates of poverty, maternal education and maternal unemployment. Variations in encoding strategies, engineering techniques and feature representation were marginal. Categorical data were mainly encoded as binary inputs, while features with high dimensionality like socio-economic status were condensed by using principal component analysis. A review of existing feature representations can serve as a feature construction reference to improve the exploitation of machine learning techniques within the context of childhood vaccination defaulter risk prediction. Future studies can exploit other representations different from binary encoding, like frequency encoding, to introduce elements of weighting into multi-categorical features.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000965"},"PeriodicalIF":7.7,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755320","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":"Topological data mapping of online hate speech, misinformation, and general mental health: A large language model based study.","authors":"Andrew William Alexander, Hongbin Wang","doi":"10.1371/journal.pdig.0000935","DOIUrl":"10.1371/journal.pdig.0000935","url":null,"abstract":"<p><p>The advent of social media has led to an increased concern over its potential to propagate hate speech and misinformation, which, in addition to contributing to prejudice and discrimination, has been suspected of playing a role in increasing social violence and crimes in the United States. While literature has shown the existence of an association between posting hate speech and misinformation online and certain personality traits of posters, the general relationship and relevance of online hate speech/misinformation in the context of overall psychological wellbeing of posters remain elusive. One difficulty lies in finding data analytics tools capable of adequately analyzing the massive amount of social media posts to uncover the underlying hidden links. Machine learning and large language models such as ChatGPT make such an analysis possible. In this study, we collected thousands of posts from carefully selected communities on the social media site Reddit. We then utilized OpenAI's GPT3 to derive embeddings of these posts, which are high-dimensional real-numbered vectors that presumably represent the hidden semantics of posts. We then performed various machine-learning classifications based on these embeddings in order to identify potential similarities between hate speech/misinformation speech patterns and those of various communities. Finally, a topological data analysis (TDA) was applied to the embeddings to obtain a visual map connecting online hate speech, misinformation, various psychiatric disorders, and general mental health.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000935"},"PeriodicalIF":7.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12306733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746405","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}