Frontiers in digital health最新文献

{"title":"Designing knowledge-driven digitalization: novel recommendations for digitally supported multi-professional collaboration.","authors":"Oliver Meindl, Sarah Peuten, Xena Striebel, Henner Gimpel, Christoph Ostgathe, Werner Schneider, Tobias Steigleder","doi":"10.3389/fdgth.2025.1511973","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1511973","url":null,"abstract":"<p><strong>Introduction: </strong>Palliative care is based on the principle of multi-professional collaboration, which integrates diverse competencies and perspectives to provide holistic care and support for patients and their relatives. In palliative care teams, there is an intensive exchange of information and knowledge; however, current documentation and hospital information systems often fall short of meeting the specific demands for effective collaboration and dynamic communication in this field.</p><p><strong>Methods: </strong>This action design research study is based on the three-and-a-half-year interdisciplinary research project PALLADiUM and aims to demonstrate the added value of knowledge-driven digitalization.</p><p><strong>Results and discussion: </strong>Our study provides novel recommendations for digitally supported multi-professional collaboration tailored to the specific requirements of palliative care and similar fields. Based on the analytical distinction between 'information' and 'knowledge,' we present design recommendations for co-creative, knowledge-driven development processes and multi-professional collaboration support systems. We further illustrate how these recommendations have been implemented into a functional technical demonstrator and outline how our results could impact future digitalization initiatives in healthcare.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1511973"},"PeriodicalIF":3.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12009921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052030","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":"Effect of the transpulmonary pressure on the lungs' vibroacoustic response: a first numerical perspective.","authors":"Arife Uzundurukan, Sébastien Poncet, Daria Camilla Boffito, Philippe Micheau","doi":"10.3389/fdgth.2025.1434578","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1434578","url":null,"abstract":"<p><p>In the high-stakes environment of intensive care units (ICUs), managing transpulmonary pressure is crucial for providing breathing assistance to intubated patients, particularly when combining this intervention with respiratory therapy, such as high-frequency chest compression (HFCC). Despite the complexity of lung tissues, a computed tomography-based finite element model (CT-FEM), guided by Biot's theory, can be employed to numerically predict their vibroacoustic behavior at low frequencies, where the properties of the lungs align with the theory's principles. In this work, one aims to develop an analytical model of the lungs for two different levels of transpulmonary pressure-10 cm H₂O (inflated lungs) and 20 cm H₂O (healthy lungs)-to examine the poroviscoelastic behavior of the lungs and evaluate the generated analytical model using a CT-FEM of the human thorax like a digital twin of the human thorax. Biot's theory was utilized to predict the complex-valued shear wave speed, as well as the fast and slow compression wave speeds, across a frequency range between 5 and 100 Hz. The analytically computed values were tested using a previously validated CT-FEM of the human thorax to compare respiratory therapy outcomes for intubated patients under different transpulmonary pressure levels. Besides the frequency response function of the thorax, the kinetic energy density and the strain energy density were compared for these pressure levels. The CT-FEM demonstrated that all peak points fall within the range of 20-45 Hz; therefore, this range might be considered in ICUs settings. Kinetic energy density was nearly 2.2 times higher, and strain energy density was 1.46-1.26 times higher at the first and last peaks, respectively; therefore, inflated lungs experienced greater effects than healthy ones under the same respiratory therapy conditions. Overall, this study highlights how different transpulmonary pressures affect HFCC therapy, offering insights into gentle and effective conditions for intubated patients in ICUs while revealing the lungs' 3D responses by integrating analytically predicted shear wave speed, fast and slow compression wave speeds.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1434578"},"PeriodicalIF":3.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029910","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":"Recovery in personality disorders: the development and preliminary testing of a novel natural language processing model to identify recovery in mental health electronic records.","authors":"Giouliana Kadra-Scalzo, Jaya Chaturvedi, Oliver Dale, Richard D Hayes, Lifang Li, Shaza Mahmood, Jonathan Monk-Cunliffe, Angus Roberts, Paul Moran","doi":"10.3389/fdgth.2025.1544781","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1544781","url":null,"abstract":"<p><strong>Introduction: </strong>The concept of recovery is of great importance in mental health as it emphasizes improvements in quality of life and functioning alongside the traditional focus on symptomatic remission. Yet, investigating non-symptomatic recovery in the field of personality disorders has been particularly challenging due to complexities in capturing the occurrence of recovery. Electronic health records (EHRs) provide a robust platform from which episodes of recovery can be detected. However, much of the relevant information may be embedded in free-text clinical notes, requiring the development of appropriate tools to extract these data.</p><p><strong>Methods: </strong>Using data from one of Europe's largest electronic health records databases [the Clinical Records Interactive Search (CRIS)], we developed and evaluated natural language processing (NLP) models for the identification of occupational and activities of daily living (ADL) recovery among individuals diagnosed with personality disorder.</p><p><strong>Results: </strong>The models on ADL performed better (precision: 0.80; 95% CI: 0.73-0.84) than those on occupational recovery (precision: 0.62; 95%CI: 0.52-0.72). However, the models performed less acceptably in correctly identifying all those who recovered, generally missing at least 50% of the population of those who had recovered.</p><p><strong>Conclusion: </strong>It is feasible to develop NLP models for the identification of recovery domains for individuals with a diagnosis of personality disorder. Future research needs to improve the efficiency of pre-processing strategies to handle long clinical documents.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1544781"},"PeriodicalIF":3.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144064869","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 doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare.","authors":"Paulo Santos, Isabel Nazaré","doi":"10.3389/fdgth.2025.1588479","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1588479","url":null,"abstract":"<p><p>Artificial intelligence (AI) 's rapid integration into healthcare transforms medical decision-making, preventive strategies, and patient engagement. AI-driven technologies, including real-time health monitoring and predictive analytics, offer new personalized preventive care possibilities. However, concerns regarding ethical implications, data security, and equitable access remain unresolved. This paper addresses the critical gap in AI integration in preventive healthcare, highlighting statistical evidence of its impact. It also explores the intersection of AI, preventive medicine, and ethical challenges in future healthcare, envisioning the evolving roles of physicians and patients in an AI-integrated ecosystem. A fictional case study projected for 2040, illustrating an entirely digitized, AI-supported healthcare system, frames the discussion about digital health technologies, privacy regulations, and AI's ethical implications in the future of preventive medicine. Digital health interventions powered by AI will facilitate real-time preventive strategies, strengthen patient autonomy, and enhance precision medicine. However, algorithmic bias, data privacy, and healthcare equity challenges must be addressed to ensure AI fosters inclusivity rather than exacerbating disparities. Regulatory frameworks, such as GDPR, provide foundational protections, but further adaptations are required to govern AI's expanding role in medicine. This digital-assisted preventive medicine has the potential to redefine patient-provider interactions, enhance healthcare efficiency, and promote proactive health management. However, achieving this vision requires a multidisciplinary approach involving health professionals, policymakers, and technology developers. Future research should focus on regulatory strategies, digital literacy, and ethical AI implementation to balance innovation with equity, ensuring that digital healthcare remains patient-centered and inclusive.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1588479"},"PeriodicalIF":3.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051706","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":"Applying and validating a quality management system for in-house developed medical software.","authors":"Vera Lagerburg, Michelle van den Boorn, Reinier F Crane, Koen Welvaars, Jaap M Groen","doi":"10.3389/fdgth.2025.1461107","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1461107","url":null,"abstract":"<p><strong>Introduction: </strong>The legislation regarding in-house development of medical devices has changed substantially with the introduction of the Medical Device Regulation (MDR) in 2021. Practical guidelines regarding the implementation of a quality management system for in-house developed medical software are scarce. In this article, we describe our experience with fulfilling the requirements of the MDR for an in-house developed prediction model, qualified as medical software.</p><p><strong>Methods and materials: </strong>Our quality management system (QMS) is based on the ISO13485:2016. It is a workflow consisting of elements subdivided in subelements, which consist of procedures, work instructions and/or formats. Within the data science team procedures regarding the process and documentation of software development were already in place. The existing procedures and documentation were compared with the procedures of the QMS and where possible, integrated into the workflow. The gap between the existing procedures regarding software development and the procedures of the QMS was defined. Existing documentation and procedures were used as much as possible. If there was a gap, additional documentation was written.</p><p><strong>Results: </strong>The majority of the (sub)elements was considered to be applicable for our software development project beforehand. Only in 6 out of 32 cases (19%), the (sub)element was deemed not applicable. For 32% of the applicable elements the documentation of the data scientists team was sufficient and additional information was not needed. For 23% the documentation was incomplete and we decided to add relevant information to fulfil the requirements of the MDR and for 45% the documentation was completely lacking and the standard formats were used.</p><p><strong>Conclusion: </strong>We showed in this article that it is possible to use a QMS developed with physical medical products in mind for medical software and thus comply with applicable legislation and regulations. This can be done without too much effort when there is already some structured form of software development methodology in place.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1461107"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999547","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 COVID-19 pandemic and the worldwide online interest in telepsychiatry: an infodemiological study from 2004 to 2022.","authors":"Rowalt Alibudbud","doi":"10.3389/fdgth.2025.1425684","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1425684","url":null,"abstract":"<p><strong>Introduction: </strong>Studies call for the further assessment and understanding of public interests and concerns about telepsychiatry, especially during the COVID-19 pandemic. Since telepsychiatry services are accessed through the Internet, this study analyzed online searches and queries to determine telepsychiatry-related interests and concerns over time. The findings can inform the development and customization of online telepsychiatry resources and services, enabling a more effective response to public needs.</p><p><strong>Materials and methods: </strong>This study determined public concerns and interests in telepsychiatry using data from Google Trends and Wikipedia from 2004 to 2022. These platforms were selected for their large global market share. After describing the data, bootstrap for independent sample tests of search volumes and Wikipedia page views before and during the COVID-19 pandemic.</p><p><strong>Results: </strong>The highest interest in telepsychiatry was observed in high-income countries. Search volumes for telepsychiatry increased, while Wikipedia page views decreased during the COVID-19 pandemic. The top and rising queries that can be incorporated into telepsychiatry websites include telepsychiatry concepts, jobs, services, costs, and locations.</p><p><strong>Discussion: </strong>The findings support that the use of the Internet for telepsychiatry information increased compared to previous years, especially during the start of the COVID-19 pandemic. There may also be a higher interest in telepsychiatry among high-income nations compared to low and middle-income countries. Furthermore, the study also supports that digital information should be tailored to respond to public needs and expectations by incorporating telepsychiatry-related concepts, jobs, services, costs, and locations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1425684"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11998030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030226","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":"Mapping the research landscape of mHealth and technology in pediatric chronic illness: a bibliometric study.","authors":"Esther Rodríguez-Jiménez, Javier Martín-Ávila, Selene Valero-Moreno, Marián Pérez-Marín","doi":"10.3389/fdgth.2025.1540362","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1540362","url":null,"abstract":"<p><strong>Introduction: </strong>The presence of a chronic disease during adolescence has been linked to an increased risk of developing psychosocial problems and a greater likelihood of experiencing difficulties during the transition to adulthood. In this regard, research has been conducted on the development of applications or programs based on new technologies to address the potential complications associated with self-management and coping with chronic diseases in adolescents.</p><p><strong>Method: </strong>The objective of the present study was to conduct a bibliometric analysis of the existing literature on the use of new technologies to enhance self-management and coping with chronic diseases during adolescence. This four-staged bibliometric analysis was conducted using the following software programs: HisCite, Bibexcel, Pajek, and VOSviewer. The programs were utilized for the extraction of data and the subsequent construction of graphs, which facilitate the visualization of existing networks between disparate authors, institutions, and terms.</p><p><strong>Results: </strong>The screening results shortlisted a total of 157 articles from an initial 207. The further analysis of these records indicated that the United States was the most prominent nation in terms of scientific output in the domain of digital applications designed to enhance self-management and coping in chronic diseases during adolescence.</p><p><strong>Discussion: </strong>The digital intervention in this specific population is primarily associated with the development of the mHealth app, along with the physical and psychological consequences that illness can entail. This research can serve as a reference for future bibliometric studies or scientific investigations in this field.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1540362"},"PeriodicalIF":3.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11994655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060750","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":"Mobile health (mHealth) technologies for fall prevention among older adults in low-middle income countries: bibliometrics, network analysis and integrative review.","authors":"Michael Joseph Dino, Ladda Thiamwong, Rui Xie, Ma Kristina Malacas, Rommel Hernandez, Patrick Tracy Balbin, Joseph Carlo Vital, Jenica Ana Rivero, Vivien Wu Xi","doi":"10.3389/fdgth.2025.1559570","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1559570","url":null,"abstract":"<p><strong>Introduction: </strong>mHealth technologies offer promising solutions to reduce the incidence of falls among older adults. Unfortunately, publications on their application to Low-Middle Income Countries (LMIC) settings have not been collectively examined.</p><p><strong>Methods: </strong>A triadic research design involving bibliometrics, network analysis, and model-based integrative review was conducted to process articles (<i>n</i> = 22) from 629 publications extracted from major databases using keywords related to mHealth, falls prevention, and LMIC. The web-based application Covidence and stand-alone VosViewer software were used to process data following previously published review standards.</p><p><strong>Results: </strong>Published articles in the field feature multidisciplinary authorships from multiple scholars in the domains of health and technology. Network analysis revealed the most prominent stakeholders and keyword clusters related to mHealth technology features and applications in healthcare. The papers predominantly focused on the development of mHealth technology, usability, and affordances and less on the physiologic and sociologic attributes of technology use. mHealth technologies in low and middle-income countries are mostly smartphone-based, static, and include features for home care settings with fall detection accuracy of 86%-99.62%. Mixed reality-based mobile applications have not yet been explored.</p><p><strong>Conclusion: </strong>Overall, key findings and information from the articles highlight a gradually advancing research domain. Outcomes reinforce the need to expand the focus of mHealth investigations to include emerging technologies, update current technology models, create a more human-centered technology design, test mHealth technologies in the clinical setting, and encourage continued cooperation between and among researchers from various fields and environments.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1559570"},"PeriodicalIF":3.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11985854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042465","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 large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate.","authors":"Myranda Uselton Shirk, Catherine Dang, Jaewoo Cho, Hanlin Chen, Lily Hofstetter, Jack Bijur, Claiborne Lucas, Andrew James, Ricardo-Torres Guzman, Andrea Hiller, Noah Alter, Amy Stone, Maria Powell, Matthew E Pontell","doi":"10.3389/fdgth.2025.1552746","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1552746","url":null,"abstract":"<p><strong>Background: </strong>Hypernasality, a hallmark of velopharyngeal insufficiency (VPI), is a speech disorder with significant psychosocial and functional implications. Conventional diagnostic methods rely heavily on specialized expertise and equipment, posing challenges in resource-limited settings. This study explores the application of OpenAI's Whisper model for automated hypernasality detection, offering a scalable and efficient alternative to traditional approaches.</p><p><strong>Methods: </strong>The Whisper model was adapted for binary classification by replacing its sequence-to-sequence decoder with a custom classification head. A dataset of 184 audio recordings, including 96 hypernasal (cases) and 88 non-hypernasal samples (controls), was used for training and evaluation. The Whisper model's performance was compared to traditional machine learning approaches, including support vector machines (SVM) and random forest (RF) classifiers.</p><p><strong>Results: </strong>The Whisper-based model effectively detected hypernasality in speech, achieving a test accuracy of 97% and an F1-score of 0.97. It significantly outperformed SVM and RF classifiers, which achieved accuracies of 88.1% and 85.7%, respectively. Whisper demonstrated robust performance across diverse recording conditions and required minimal training data, showcasing its scalability and efficiency for hypernasality detection.</p><p><strong>Conclusion: </strong>This study demonstrates the effectiveness of the Whisper-based model for hypernasality detection. By providing a reliable pretest probability, the Whisper model can serve as a triaging mechanism to prioritize patients for further evaluation, reducing diagnostic delays and optimizing resource allocation.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1552746"},"PeriodicalIF":3.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11986712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013632","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 call to action for adverse drug event (ADE) detection and prevention.","authors":"John McCue, C David Butler, Raymond C Love, Shelly Spiro, Roy Guharoy","doi":"10.3389/fdgth.2025.1507967","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1507967","url":null,"abstract":"<p><p>Injury from medication use, known as an adverse drug event (ADE) accounts for millions of emergency department visits globally and thousands of hospitalizations annually within the United States. Efforts to prevent and detect ADEs within healthcare systems are complicated by data quality, lack of data standardization, and actionable clinical decision support systems. United States Pharmacopeia (USP) proposes the use of an ADE value set, a standardized grouping of medical terms, to improve the identification, documentation, and use of ADE information in EHRs. Artificial Intelligence and Machine Learning capabilities would be further strengthened through the standardization of ADE data and information.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1507967"},"PeriodicalIF":3.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11986632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037164","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}