PLOS digital health最新文献

{"title":"Patient and clinician perceptions of telehealth in musculoskeletal physiotherapy services - A systematic review of the evidence-base.","authors":"Anthony Smith, Sue Innes","doi":"10.1371/journal.pdig.0000789","DOIUrl":"10.1371/journal.pdig.0000789","url":null,"abstract":"<p><p>Telehealth has been at the forefront of healthcare delivery since the Covid-19 pandemic with a prompt shift in transition from face-to-face delivery to remote contact. This critical review aims to understand patient and clinician views of telehealth adoption regarding effectiveness and satisfaction within musculoskeletal (MSK) physiotherapy services. A systematic process was used to search for evidence within 6 databases (CINAHL, PyscINFO, Medline, AMED, EMCARE, EMBASE) utilising clear inclusion and exclusion criteria in August 2024. Articles published in English between 2019-2024 were searched, a total of 394 articles were identified and 10 articles were included in the review. Methodological quality was evaluated using the CASP, JBI and QuADS tools. Findings were evaluated via consensus and showed clear patient and clinician satisfaction with positive themes of reduced travel, reduced physical burden, flexibility/accessibility and negative themes of reduced physical contact, computer literacy and privacy infringements. Quality analysis identified non-response bias, sampling bias, and participants mix as risks to overall validity. Telehealth has shown to be an effective and transformative model of healthcare delivery for musculoskeletal services, especially in improving access and convenience for patients. Implications for practice suggest a need for a hybrid model of care, enhanced training, and improved data security. Future research should focus on satisfaction within condition specific musculoskeletal health, overall cost-effectiveness, health equity, and the integration of advanced technologies to ensure telehealth can be a sustainable and inclusive part of the healthcare landscape moving forwards.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000789"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11957330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756238","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":"Effectiveness and user experience of a virtual reality intervention in a cohort of patients with chronic musculoskeletal pain syndromes.","authors":"Tiffany Prétat, Pedro Ming Azevedo, Chris Lovejoy, Thomas Hügle","doi":"10.1371/journal.pdig.0000788","DOIUrl":"10.1371/journal.pdig.0000788","url":null,"abstract":"<p><p>Chronic musculoskeletal pain (CMP) syndromes, including fibromyalgia, present diverse physical and psychological symptoms often resistant to pharmacological treatment. To retrospectively evaluate the effectiveness and user experience of Virtual Reality (VR) in reducing pain and anxiety in CMP patients and identify predictors of positive response. Data from 91 CMP patients in a 2-week interdisciplinary pain program were analyzed (78% met fibromyalgia criteria). Pain and anxiety were assessed using Numerical Rating Scales (NRS 0-10) before and after VR. Follow-up interviews were conducted after one month. An unsupervised machine learning model explored response patterns. VR led to a moderate but significant short-term reduction in anxiety and pain (median NRS -1.0, p < 0.001). A reduction of ≥3 NRS points occurred in 25% (anxiety) and 14% (pain). High baseline anxiety (NRS ≥ 7) correlated with greater pain reduction (median -2.0, p = 0.01). After one month, half of the patients reported sustained benefits. Catastrophizing and benzodiazepine use were linked to improved anxiety outcomes. Machine learning identified a most responsive cluster, characterized by patients with nociplastic pain, alexithymia, and anxiety. VR provided moderate short- and mid-term benefits for anxiety and pain in CMP patients, particularly in those with nociplastic pain and high baseline anxiety.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000788"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11957290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756284","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":"The behavioural and cognitive impacts of digital educational interventions in the emergency department: A systematic review.","authors":"Sophie Cleff, Shubhang Sreeranga, Ibtisam Mahmoud, Abdullatif Hassan, Laury Gueyie Noutiamo, Elie Fadel, Jennifer Turnbull, Esli Osmanlliu","doi":"10.1371/journal.pdig.0000772","DOIUrl":"10.1371/journal.pdig.0000772","url":null,"abstract":"<p><p>Ensuring patients and their caregivers understand the health information they receive is an important part of every clinical visit. Digital educational interventions like video discharge instructions, follow-up text messaging, or interactive web-based modules (WBMs) have the potential to improve information retention and influence behaviour. This study aims to systematically evaluate the impact of these interventions on patient and caregiver cognition and behaviour, as well as identify the characteristics of successful interventions and observe how success is measured. In December of 2022, a systematic literature search was conducted in several databases (Cochrane, Embase, MEDLINE (Ovid), Web of Science, ClinicalTrials.gov, and Google Scholar) for randomized controlled trials (RCTs) published between 2012 and 2022. In 2024, an identical search was performed for articled published between 2022 and 2024. Studies testing patient- and caregiver-facing digital educational interventions in the emergency department for behavioural and cognitive outcomes were included. Data from 35 eligible studies encompassing 12,410 participants were analyzed and assessed for bias using the Cochrane RoB2.0 tool. Video was used in 22 studies (63%), making it the most common modality. Seventy-three percent (16/22) of these studies reported statistically significant improvements in their primary outcomes. Text messaging was used in eight studies, with two (25%) reporting significant improvement in their primary outcomes. WBMs and apps were used in seven studies, 71% (5/7) of which reported statistically significant improvements in primary outcomes. Statistically significant improvements in cognitive outcomes were reported in 64% (18/28) of applicable studies, compared with 17% (4/23) for behavioural outcomes. The results suggest that digital educational interventions can positively impact cognitive outcomes in the emergency department. Video, WBM, and app modalities appear particularly effective. However, digital educational interventions may not yet effectively change behaviour. Establishing guidelines for evaluating the quality of digital educational interventions, and the formal adoption of existing reporting guidelines, could improve study quality and consistency in this emerging field. Registration The study is registered with PROSPERO ID #CRD42023338771.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000772"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11942422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733530","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 flexible framework for minimal biomarker signature discovery from clinical omics studies without library size normalisation.","authors":"Daniel Rawlinson, Chenxi Zhou, Myrsini Kaforou, Kim-Anh Lê Cao, Lachlan J M Coin","doi":"10.1371/journal.pdig.0000780","DOIUrl":"10.1371/journal.pdig.0000780","url":null,"abstract":"<p><p>Application of transcriptomics, proteomics and metabolomics technologies to clinical cohorts has uncovered a variety of signatures for predicting disease. Many of these signatures require the full 'omics data for evaluation on unseen samples, either explicitly or implicitly through library size normalisation. Translation to low-cost point-of-care tests requires development of signatures which measure as few analytes as possible without relying on direct measurement of library size. To achieve this, we have developed a feature selection method (Forward Selection-Partial Least Squares) which generates minimal disease signatures from high-dimensional omics datasets with applicability to continuous, binary or multi-class outcomes. Through extensive benchmarking, we show that FS-PLS has comparable performance to commonly used signature discovery methods while delivering signatures which are an order of magnitude smaller. We show that FS-PLS can be used to select features predictive of library size, and that these features can be used to normalize unseen samples, meaning that the features in the complete model can be measured in isolation for making new predictions. By enabling discovery of small, high-performance signatures, FS-PLS addresses an important impediment for the further development of precision medical care.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000780"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11942414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733527","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":"Nomophobia and Psychological distress among the Saudi Population.","authors":"Noara Alhusseini, Jamil Alghanem, Salsabil Haque, Samanta Mohammed Shahin, Mohammad Alsaeed, Wael Kalou, Adel Kalou, Sara Alnasser, Majed Ramadan, Khadijah Ateq","doi":"10.1371/journal.pdig.0000779","DOIUrl":"10.1371/journal.pdig.0000779","url":null,"abstract":"<p><strong>Introduction: </strong>Smartphones have become a defining feature of the 21st century, fundamentally transforming the way we live and interact. However, the pervasive use and growing dependence on these devices have led to increasing concerns about their impact on mental health. The rise of smartphone addiction, often manifesting as anxiety, irritability, and a feeling of melancholy, has contributed to the rapid increase in nomophobia, a term describing the fear of being without a mobile device. This phenomenon is increasingly linked to psychological distress as our reliance on smartphones continues to deepen.</p><p><strong>Aim: </strong>This study seeks to determine the prevalence of nomophobia and psychological distress symptoms and their relationship among the adult population of Saudi Arabia.</p><p><strong>Method: </strong>A cross-sectional survey was done among the adult population of Saudi Arabia, including Saudis and non-Saudis. An online validated survey was distributed via social media channels. SAS 9.4. was used for data analysis. Frequencies and percentages were used to display the prevalence, and the chi-square test was used for associations. A p-value <0.05 was used to determine significance.</p><p><strong>Result: </strong>A total of 704 Saudi and non-Saudi adults completed the questionnaire. The mean nomophobia score among all participants indicated a moderate level at 73.71, while the mean psychological distress score reflected a mild disorder at 22.08. Saudis reported a statistically higher significant mean nomophobia score than non-Saudis (p-value <0.0001). Participants residing in the Eastern region were significantly more prone to nomophobia (p-value 0.0003), and to psychological distress (p-value 0.004).</p><p><strong>Conclusion: </strong>The study reveals that men and Saudi nationals are particularly affected by nomophobia, likely due to their higher reliance on smartphones. Saudi nationality, educational attainment, and residing in the Eastern region of Saudi Arabia are considered predictors for nomophobia and psychological distress.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000779"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11942369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733529","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":"Classifying the features of digital mental health interventions to inform the development of a patient decision aid.","authors":"Gemma Bradley, Lucia Rehackova, Kayleigh Devereaux, Tor Alexander Bruce, Victoria Nunn, Liam Gilfellon, Scott Burrows, Alisdair Cameron, Rose Watson, Katie Rumney, Darren Flynn","doi":"10.1371/journal.pdig.0000752","DOIUrl":"10.1371/journal.pdig.0000752","url":null,"abstract":"<p><p>Digital mental health interventions (DMHIs) are a potential scalable solution to improve access to psychological support and therapies. DMHIs vary in terms of their features such as delivery systems (Websites or Apps) and function (information, monitoring, decision support or therapy) that are sensitive to the needs and preferences of users. A decision aid is warranted to empower people to make an informed preference-based choice of DMHIs. We conducted a review of features of DMHIs to embed within a patient decision aid to support shared decision-making. DMHIs, with evidence of availability in the United Kingdom (UK) at the time of the review, were identified from interactive meetings with a multi-disciplinary steering group, an online survey and interviews with adults with lived experience of using DMHIs in the UK. Eligible DMHIs targeted users age ≥16 years with a mental health condition(s), delivered through a digital system. A previous classification system for DMHIs was extended to eight dimensions (Target population; System; Function; Time; Facilitation; Duration and Intensity; and Research Evidence) to guide data extraction and synthesis of findings. Twenty four DMHIs were included in the review. More than half (n = 13, 54%) targeted people living with low mood, anxiety or depression and were primarily delivered via systems such as Apps or websites (or both). Most DMHIs offered one-way transmission of information (n = 21, 88%). Ten (42%) also had two-way communication (e.g., with a healthcare provider). Eighteen (75%) had a function of therapy, with seven and five DMHIs providing monitoring and decision support functions respectively. Most DMHIs were capable of being self-guided (n = 18,75%). Cost and access were primarily free, with some free via referral from the UK NHS or through corporate subscription for employees (n = 11). Eight (33%) DMHIs had evidence of effectiveness from randomised controlled trials. Six statements were developed to elicit user preferences on features of DMHIs: Target Population; Function; Time and Facilitation; System; Cost and Access; and Research Evidence. Preference elicitation statements have been embedded into a prototype decision aid for DMHIs, which will be subjected to acceptability and usability testing.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000752"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11942417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733528","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":"Measuring the influence of depressive symptoms on engagement, adherence, and weight loss in an eHealth intervention.","authors":"Lex Hurley, Nisha G O'Shea, Julianne Power, Christopher Sciamanna, Deborah F Tate","doi":"10.1371/journal.pdig.0000766","DOIUrl":"10.1371/journal.pdig.0000766","url":null,"abstract":"<p><strong>Background: </strong>Digital behavior change interventions (eHealth, mHealth) are known to be capable of promoting clinically significant weight loss among some participants. However, these programs can struggle with declining engagement and adherence over time, which can hamper their effectiveness. This analysis examines the extent that depression symptoms may negatively influence engagement, adherence, and 6 month weight change in an eHealth intervention.</p><p><strong>Methods: </strong>Structural equation modeling is applied to test the effects of baseline depression symptoms on weight change outcomes, mediated through latent constructs of engagement and adherence, respectively. These constructs were highly correlated within this dataset and necessitated two separate models to be tested. Engagement was indicated by 6 month sums of website logins, user-created goals, visiting various webpages, and posts on the online discussion boards. Adherence was indicated by 6 month sums of weeks exercise goals met, days weight logged, and days of complete dietary tracking.</p><p><strong>Results: </strong>Depression symptoms showed no direct association with weight change (p's ≥ 0.6), but were negatively associated with both constructs of engagement and adherence (p's < 0.001), which in turn were negatively associated with weight change in both models (p's < 0.001). It was determined depression symptoms had a positive indirect association with weight change fully mediated through these variables, meaning less weight loss or possible weight gain (p < 0.001).</p><p><strong>Discussion: </strong>This analysis shows that depression symptoms had a significant, undesirable effect on weight loss outcomes within this eHealth intervention, fully mediated through measured participant engagement and adherence. Further research is needed to test these constructs within a longitudinal model to better understand their dynamic interrelationships, and consider means to address depression in future digital interventions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000766"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11936179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712464","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 of an artificial intelligence-enhanced warfarin interaction checker platform.","authors":"Monther Abdolmohsin Alsultan, Mohammed Alabdulmuhsin, Deema AlBunyan","doi":"10.1371/journal.pdig.0000756","DOIUrl":"10.1371/journal.pdig.0000756","url":null,"abstract":"<p><p>Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism, which has many advantages but also some disadvantages including narrow therapeutic window, vast drug interactions (and wide variability with foods/herbs), as well as unpredictability of pharmacodynamics and/or kinetics. Complicating factors can present as challenges for the outpatient clinicians trying to strike that balance due to the potential consequences of over or under dose anticoagulation with associated increased risk of bleeding and/or thromboembolic events, respectively. Because warfarin interactions can drastically affect therapeutic outcomes, patient to healthcare provider communication regarding such potential drug-drug or diet-warfarin interactions is crucial for compliance with the medication and achieving successful treatment. Furthermore, language barriers cause low patient satisfaction scores and poor quality/safety health care. In fact, the advancement and improvements in healthcare technology promise artificial intelligence (AI) as one of ideal options to optimize delivery of health care. The goal of this study is to develop Warfa-Check, a bilingual AI-based web app that matches both speakers of Arabic and English. The application helps users recognize potential warfarin-associated drug interactions with a simple user interface that accepts text, picture or voice commands. Warfa-Check, developed with Python and Flask as well as OpenAI's GPT-4 API with natural language processing tools trained to correctly interpret outbound warfarin interactions. Multiple validation methods and beta testing have been done to ensure that the app is data-driven, as well color coded alerts for interaction severity provide clear feedback to end-users. This easy-to-use application helps patients identify drug interactions in both English and Arabic. Warfa-Check represents a valuable avenue for improving the safety of our residents, simplifying medication management in high-risk individuals and streamlining workflow. Future development plans are to develop into other anticoagulants, and integrate with Electronic Health Records (EHRs).</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000756"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702424","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":"Evaluating fairness of machine learning prediction of prolonged wait times in Emergency Department with Interpretable eXtreme gradient boosting.","authors":"Hao Wang, Nethra Sambamoorthi, Nathan Hoot, David Bryant, Usha Sambamoorthi","doi":"10.1371/journal.pdig.0000751","DOIUrl":"10.1371/journal.pdig.0000751","url":null,"abstract":"<p><p>It is essential to evaluate performance and assess quality before applying artificial intelligence (AI) and machine learning (ML) models to clinical practice. This study utilized ML to predict patient wait times in the Emergency Department (ED), determine model performance accuracies, and conduct fairness evaluations to further assess ethnic disparities in using ML for wait time prediction among different patient populations in the ED. This retrospective observational study included adult patients (age ≥18 years) in the ED (n=173,856 visits) who were assigned an Emergency Severity Index (ESI) level of 3 at triage. Prolonged wait time was defined as waiting time ≥30 minutes. We employed extreme gradient boosting (XGBoost) for predicting prolonged wait times. Model performance was assessed with accuracy, recall, precision, F1 score, and false negative rate (FNR). To perform the global and local interpretation of feature importance, we utilized Shapley additive explanations (SHAP) to interpret the output from the XGBoost model. Fairness in ML models were evaluated across sensitive attributes (sex, race and ethnicity, and insurance status) at both subgroup and individual levels. We found that nearly half (48.43%, 84,195) of ED patient visits demonstrated prolonged ED wait times. XGBoost model exhibited moderate accuracy performance (AUROC=0.81). When fairness was evaluated with FNRs, unfairness existed across different sensitive attributes (male vs. female, Hispanic vs. Non-Hispanic White, and patients with insurances vs. without insurance). The predicted FNRs were lower among females, Hispanics, and patients without insurance compared to their counterparts. Therefore, XGBoost model demonstrated acceptable performance in predicting prolonged wait times in ED visits. However, disparities arise in predicting patients with different sex, race and ethnicity, and insurance status. To enhance the utility of ML model predictions in clinical practice, conducting performance assessments and fairness evaluations are crucial.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000751"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672092","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":"Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.","authors":"Zasim Azhar Siddiqui, Maryam Pathan, Sabina Nduaguba, Traci LeMasters, Virginia G Scott, Usha Sambamoorthi, Jay S Patel","doi":"10.1371/journal.pdig.0000765","DOIUrl":"10.1371/journal.pdig.0000765","url":null,"abstract":"<p><strong>Background: </strong>The use of social media platforms in health research is increasing, yet their application in studying rare diseases is limited. Hodgkin's lymphoma (HL) is a rare malignancy with a high incidence in young adults. This study evaluates the feasibility of using social media data to study the disease and treatment characteristics of HL.</p><p><strong>Methods: </strong>We utilized the X (formerly Twitter) API v2 developer portal to download posts (formerly tweets) from January 2010 to October 2022. Annotation guidelines were developed from literature and a manual review of limited posts was performed to identify the class and attributes (characteristics) of HL discussed on X, and create a gold standard dataset. This dataset was subsequently employed to train, test, and validate a Named Entity Recognition (NER) Natural Language Processing (NLP) application.</p><p><strong>Results: </strong>After data preparation, 80,811 posts were collected: 500 for annotation guideline development, 2,000 for NLP application development, and the remaining 78,311 for deploying the application. We identified nine classes related to HL, such as HL classification, etiopathology, stages and progression, and treatment. The treatment class and HL stages and progression were the most frequently discussed, with 20,013 (25.56%) posts mentioning HL's treatments and 17,177 (21.93%) mentioning HL stages and progression. The model exhibited robust performance, achieving 86% accuracy and an 87% F1 score. The etiopathology class demonstrated excellent performance, with 93% accuracy and a 95% F1 score.</p><p><strong>Discussion: </strong>The NLP application displayed high efficacy in extracting and characterizing HL-related information from social media posts, as evidenced by the high F1 score. Nonetheless, the data presented limitations in distinguishing between patients, providers, and caregivers and in establishing the temporal relationships between classes and attributes. Further research is necessary to bridge these gaps.</p><p><strong>Conclusion: </strong>Our study demonstrated potential of using social media as a valuable preliminary research source for understanding the characteristics of rare diseases such as Hodgkin's Lymphoma.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000765"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11922232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665635","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}