DIGITAL HEALTH最新文献

{"title":"Exploring the potential of telehealth in-flight medical emergencies.","authors":"Yikeun Kim, Sung Chul Bae, Yoo-Seong Song","doi":"10.1177/20552076251326666","DOIUrl":"https://doi.org/10.1177/20552076251326666","url":null,"abstract":"<p><p>In-flight medical emergencies occur at an average of 127 incidents per one million passengers, without of physicians present at 41.1%. In response, telehealth can play a crucial role in swiftly addressing these emergencies. Adequate internet speed and appropriate latency are necessary for this purpose, alongside the importance of documenting such emergencies to enhance the efficiency of medical services. In-flight telehealth directly benefits passengers, airlines, and volunteered medical professionals. Furthermore, it presents an opportunity for innovative business models, offering new prospects for insurance companies and telecommunications providers.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251326666"},"PeriodicalIF":2.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11907535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent wearable devices with audio collection capabilities to assess chronic obstructive pulmonary disease severity.","authors":"Chunbo Zhang, Kunyao Yu, Zhe Jin, Yingcong Bao, Cheng Zhang, Jiping Liao, Guangfa Wang","doi":"10.1177/20552076251320730","DOIUrl":"https://doi.org/10.1177/20552076251320730","url":null,"abstract":"<p><strong>Background: </strong>Intelligent wearable devices have potential for chronic obstructive pulmonary disease (COPD) monitoring, but the effectiveness of combining cough and blowing sounds for disease assessment is unclear.</p><p><strong>Objective: </strong>The objective was to assess COPD severity via physiological parameters and audio data collected by a smartwatch.</p><p><strong>Methods: </strong>COPD patients underwent lung function tests, electrocardiograms, blood gas analysis, and 6-min walk tests. The patients' peripheral arterial oxygen saturation (SpO₂), heart rate variability (HRV), heart rate (HR), and respiratory rate (RR) were continuously monitored via a smartwatch for 7-14 days, and voluntary cough and forceful blowing sounds were recorded twice daily. The HR, SpO₂, and RR were categorized into all-day, sleep, and wake periods and summarized using the mean, standard deviation, median, 25th percentile, 75th percentile and percent variation. The correlations among lung function, physiological parameters, and audio data were analyzed to develop a model for predicting COPD severity.</p><p><strong>Results: </strong>Twenty-nine stable patients, with a mean age of 67.0 ± 5.8 years, were enrolled, and 89.7% were male. HR, HRV, RR, cough sounds, and blowing sounds were significantly correlated with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, with cough sounds showing the highest correlation (r = 0.7617, <i>p </i>< .001). Cough sounds also had the strongest correlation with the mean 6-minute walking distance (r = 0.6847, <i>p </i>< .001), whereas blowing sounds had the strongest correlation with the Body mass index, airflow Obstruction, Dyspnea, and Exercise capacity index (r = -0.6749, <i>p </i>< .001). A logistic regression model using the RR and blowing sounds as key predictors achieved accuracies of 0.77-0.89 in determining the GOLD grade, with a Cohen's kappa coefficient of 0.6757.</p><p><strong>Conclusions: </strong>Audio data were more strongly correlated with lung function in COPD patients than were physiological parameters. A smartwatch with audio collection capabilities effectively assessed COPD severity.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT05551169.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251320730"},"PeriodicalIF":2.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11907614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an interactive biosensing application for assessing finger dexterity.","authors":"Michal Greenberg Abrahami, Yehuda Warszawer, Alon Kalron, Emanuel Shirbint, Maria Didikin, Anat Achiron","doi":"10.1177/20552076241297734","DOIUrl":"10.1177/20552076241297734","url":null,"abstract":"<p><strong>Objective: </strong>Accurate finger function assessment is crucial for monitoring the performance of daily hand activities. However, specialized digital applications are lacking for evaluating various finger tasks. This study aims to develop a custom digital biosensing application to assess finger dexterity.</p><p><strong>Methods: </strong>We developed a digital biosensing application compatible with smartphones and tablets that enables 3-min testing of finger dexterity, measuring velocity and accuracy for each finger and each movement orientation. Data were collected for the dominant hand from a large cohort of healthy volunteers to establish population norms values.</p><p><strong>Results: </strong>The construction of the application involved a comprehensive, multi-stage process designed to ensure functionality, user-friendliness, and cross-platform compatibility using the Flutter framework by Google with specific adaptations for Android and iOS. To evaluate the application and construct population norms, 318 healthy subjects, 197 females and 121 males, mean ± age 37.7 ± 13.5 years, were tested. Velocity was faster for the vertical and horizontal tests than all other tests and fastest for finger 2, while the pinch test was the slowest for all fingers. Deviation from any required test orientation was more evident for the circle test and mainly for finger 5, while the vertical and horizontal orientations were the most unerring. Analysis of finger dexterity by age disclosed better performance in the younger age group (<35 years); no effect of gender for both velocity and deviation was observed.</p><p><strong>Conclusions: </strong>The developed digital application allows immediate evaluation of finger dexterity. The established population norms can provide a comparative standard for assessing patients with disorders like multiple sclerosis, sensory neuropathy, or stroke.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076241297734"},"PeriodicalIF":2.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing physical activity through a relational artificial intelligence chatbot: A feasibility and usability study.","authors":"Yoo Jung Oh, Kai-Hui Liang, Diane Dagyong Kim, Xuanming Zhang, Zhou Yu, Yoshimi Fukuoka, Jingwen Zhang","doi":"10.1177/20552076251324445","DOIUrl":"10.1177/20552076251324445","url":null,"abstract":"<p><strong>Objective: </strong>This study presents a pilot randomized controlled trial to assess the usability, feasibility, and initial efficacy of a mobile app-based relational artificial intelligence (AI) chatbot (Exerbot) intervention for increasing physical activity behavior.</p><p><strong>Methods: </strong>The study was conducted over a 1-week period, during which participants were randomized to either converse with a baseline chatbot without relational capacity (control group) or a relational chatbot using social relational communication strategies. Objectively measured physical activity data were collected using smartphone pedometers.</p><p><strong>Results: </strong>The study was feasible in enrolling a sample of 36 participants and with a 94% retention rate after 1 week. Daily engagement rate with the AI chatbot reached over 88% across the groups. Findings revealed that the control group experienced a significant decrease in steps on the final day, whereas the group interacting with the relational chatbot maintained their step counts throughout the study period. Importantly, individuals who engaged with the relational chatbot reported a stronger social bond with the chatbot compared to those in the control group.</p><p><strong>Conclusions: </strong>Leveraging AI chatbot and the relationship-building capabilities of AI holds promise in the development of cost-effective, accessible, and sustainable behavior change interventions. This approach may benefit individuals with limited access to conventional in-person behavior interventions.</p><p><strong>Clinical trial registrations: </strong>ClinicalTrials.gov; NCT05794308; https://clinicaltrials.gov/ct2/show/NCT05794308.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251324445"},"PeriodicalIF":2.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile apps for cancer patients: Identifying positive impacts and concerns.","authors":"Wei Leidong, Michelle Monachino, Don Lloyd-Williams, Thi Le Ha Nguyen, Brayal Dsouza, Joaquim Paulo Moreira","doi":"10.1177/20552076241305707","DOIUrl":"10.1177/20552076241305707","url":null,"abstract":"<p><strong>Background: </strong>Mobile health is being increasingly considered as a strategy to deliver healthcare to people with chronic diseases. This stands particularly true for cancer management where treatment is being progressively administered at home, requiring more involvement, education, and changes in behavior from patients. This article aims to identify the main axes of intervention for behavioral change of mHealth in cancer management and its relative impacts, as well as identify recent evidence on user preferences for optimal engagement in mHealth-based behavioral change strategies.</p><p><strong>Methodological approach: </strong>A literature search was carried out in the Databases PubMed and Cochrane during the period October-December 2023. The search retrieved 505 initial entries narrowed down to 21 articles included in this commentary.</p><p><strong>Results: </strong>Evidence is available on Mobile apps for cancer management being used to successfully promote behavioral changes in the areas of treatment adherence, symptoms self-management, communication with healthcare professionals, and holistic well-being in cancer patients. These are activities traditionally relevant in healthcare management interventions and contribute to further developing the relevance of the field of Digital Health in healthcare management.</p><p><strong>Relevance to clinical practice: </strong>The article contributes to a practical understanding of how Mobile interventions are being applied to promote higher self-care, a better emotional status, lesser adverse impacts, and, ultimately, increased survival rates for cancer patients. Several cancer patients' preferences were identified for the promotion of user engagement related to app design, available features, interoperability, and app creation process, as well as advanced healthcare management intervention. Preferences were found to be different for adolescents and young adult cancer patients when compared to other cohort groups.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076241305707"},"PeriodicalIF":2.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using machine learning to identify frequent attendance at accident and emergency services in Lanarkshire.","authors":"Fergus Reid, S Josephine Pravinkumar, Roma Maguire, Ashleigh Main, Haruno McCartney, Lewis Winters, Feng Dong","doi":"10.1177/20552076251315293","DOIUrl":"10.1177/20552076251315293","url":null,"abstract":"<p><strong>Background: </strong>Frequent attenders to accident and emergency (A&E) services pose complex challenges for healthcare providers, often driven by critical clinical needs. Machine learning (ML) offers potential for predictive approaches to managing frequent attendance, yet its application in this area is limited. Existing studies often focus on specific populations or models, raising concerns about generalisability. Identifying risk factors for frequent attendance and high resource use is crucial for effective prevention strategies.</p><p><strong>Objectives: </strong>This research aims to evaluate the strengths and weaknesses of ML approaches in predicting frequent A&E attendance in NHS Lanarkshire, Scotland, identify associated risk factors and compare findings with existing research to uncover commonalities and differences.</p><p><strong>Method: </strong>Health and social care data were collected from 17,437 A&E patients in NHS Lanarkshire (2021-2022), including clinical, social and demographic information. Five classification models were tested: multinomial logistic regression (LR), random forests (RF), support vector machine (SVM) classifier, k-nearest neighbours (k-NN) and multi-layer perceptron (MLP) classifier. Models were evaluated using a confusion matrix and metrics such as precision, recall, F1 and area under the curve. Shapley values were used to identify risk factors.</p><p><strong>Results: </strong>MLP achieved the highest F1 score (0.75), followed by k-NN, RF and SVM (0.72 each), and LR (0.70). Key health conditions and risk factors consistently predicted frequent attendance across models, with some variation highlighting dataset-specific characteristics.</p><p><strong>Conclusions: </strong>This study underscores the utility of combining ML models to enhance prediction accuracy and identify risk factors. Findings align with existing research but reveal unique insights specific to the dataset and methodology.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251315293"},"PeriodicalIF":2.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence (ChatGPT) ready to evaluate ECG in real life? Not yet!","authors":"Volkan Çamkıran, Hüseyin Tunç, Batool Achmar, Tuğçe Simay Ürker, İlhan Kutlu, Akin Torun","doi":"10.1177/20552076251325279","DOIUrl":"10.1177/20552076251325279","url":null,"abstract":"<p><strong>Objective: </strong>This study aims at evaluating if ChatGPT-based artificial intelligence (AI) models are effective in interpreting electrocardiograms (ECGs) and determine their accuracy as compared to those of cardiologists. The purpose is therefore to explore if ChatGPT can be employed for clinical setting, particularly where there are no available cardiologists.</p><p><strong>Methods: </strong>A total of 107 ECG cases classified according to difficulty (simple, intermediate, complex) were analyzed using three AI models (GPT-ECGReader, GPT-ECGAnalyzer, GPT-ECGInterpreter) and compared with the performance of two cardiologists. The statistical analysis was conducted using chi-square and Fisher exact tests using scikit-learn library in Python 3.8.</p><p><strong>Results: </strong>Cardiologists demonstrated superior accuracy (92.52%) compared to ChatGPT-based models (GPT-ECGReader: 57.94%, GPT-ECGInterpreter: 62.62%, GPT-ECGAnalyzer: 62.62%). Statistically significant differences were observed between cardiologists and AI models (<i>p</i> < 0.05). ChatGPT models exhibited enhanced performance with female patients; however, the differences found were not statistically significant. Cardiologists significantly outperformed AI models across all difficulty levels. When it comes to diagnosing patients with arrhythmia (A) and cardiac structural disease ECG patterns, cardiologists gave the best results though there was no statistical difference between them and AI models in diagnosing people with normal (N) ECG patterns.</p><p><strong>Conclusions: </strong>ChatGPT-based models have potential in ECG interpretation; however, they currently lack adequate reliability beyond oversight from a doctor. Additionally, further studies that would improve the accuracy of these models, especially in intricate diagnoses are needed.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251325279"},"PeriodicalIF":2.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11898233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The application of artificial intelligence in insomnia, anxiety, and depression: A bibliometric analysis.","authors":"Enshi Lu, Di Zhang, Mingguang Han, Shihua Wang, Liyun He","doi":"10.1177/20552076251324456","DOIUrl":"10.1177/20552076251324456","url":null,"abstract":"<p><strong>Background: </strong>Mental health issues like insomnia, anxiety, and depression have increased significantly. Artificial intelligence (AI) has shown promise in diagnosing and providing personalized treatment.</p><p><strong>Objective: </strong>This study aims to systematically review the application of AI in addressing insomnia, anxiety, and depression, identifying key research hotspots, and forecasting future trends through bibliometric analysis.</p><p><strong>Methods: </strong>We analyzed a total of 875 articles from the Web of Science Core Collection (2000-2024) using bibliometric tools such as VOSviewer and CiteSpace. These tools were used to map research trends, highlight international collaboration, and examine the contributions of leading countries, institutions, and authors in the field.</p><p><strong>Results: </strong>The United States and China lead the field in terms of research output and collaborations. Key research areas include \"neural networks,\" \"machine learning,\" \"deep learning,\" and \"human-robot interaction,\" particularly in relation to personalized treatment approaches. However, challenges around data privacy, ethical concerns, and the interpretability of AI models need to be addressed.</p><p><strong>Conclusions: </strong>This study highlights the growing role of AI in mental health research and identifies future priorities, such as improving data quality, addressing ethical challenges, and integrating AI more seamlessly into clinical practice. These advancements will be crucial in addressing the global mental health crisis.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251324456"},"PeriodicalIF":2.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of large language models in healthcare: A bibliometric analysis.","authors":"Lanping Zhang, Qing Zhao, Dandan Zhang, Meijuan Song, Yu Zhang, Xiufen Wang","doi":"10.1177/20552076251324444","DOIUrl":"10.1177/20552076251324444","url":null,"abstract":"<p><strong>Objective: </strong>The objective is to provide an overview of the application of large language models (LLMs) in healthcare by employing a bibliometric analysis methodology.</p><p><strong>Method: </strong>We performed a comprehensive search for peer-reviewed English-language articles using PubMed and Web of Science. The selected articles were subsequently clustered and analyzed textually, with a focus on lexical co-occurrences, country-level and inter-author collaborations, and other relevant factors. This textual analysis produced high-level concept maps that illustrate specific terms and their interconnections.</p><p><strong>Findings: </strong>Our final sample comprised 371 English-language journal articles. The study revealed a sharp rise in the number of publications related to the application of LLMs in healthcare. However, the development is geographically imbalanced, with a higher concentration of articles originating from developed countries like the United States, Italy, and Germany, which also exhibit strong inter-country collaboration. LLMs are applied across various specialties, with researchers investigating their use in medical education, diagnosis, treatment, administrative reporting, and enhancing doctor-patient communication. Nonetheless, significant concerns persist regarding the risks and ethical implications of LLMs, including the potential for gender and racial bias, as well as the lack of transparency in the training datasets, which can lead to inaccurate or misleading responses.</p><p><strong>Conclusion: </strong>While the application of LLMs in healthcare is promising, the widespread adoption of LLMs in practice requires further improvements in their standardization and accuracy. It is critical to establish clear accountability guidelines, develop a robust regulatory framework, and ensure that training datasets are based on evidence-based sources to minimize risk and ensure ethical and reliable use.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251324444"},"PeriodicalIF":2.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving machine learning algorithm for risk of early pressure injury prediction in admission patients using probability feature aggregation.","authors":"Shu-Chen Chang, Shu-Mei Lai, Mei-Wen Wu, Shou-Chuan Sun, Mei-Chu Chen, Chiao-Min Chen","doi":"10.1177/20552076251323300","DOIUrl":"10.1177/20552076251323300","url":null,"abstract":"<p><strong>Objective: </strong>Pressure injuries (PIs) pose a significant concern in hospital care, necessitating early and accurate prediction to mitigate adverse outcomes.</p><p><strong>Methods: </strong>The proposed approach receives multiple patients records, selects key features of discrete numerical based on their relevance to PIs, and trains a random forest (RF) machine learning (ML) algorithm to build a predictive model. Pairs of significant categorical features with high contributions to the prediction results are grouped, and the PI risk probability for each group is calculated. High-risk group probabilities are then added as new features to the original feature subset, generating a new feature subset to replace the original one, which is then used to retrain the RF model.</p><p><strong>Results: </strong>The proposed method achieved an accuracy of 83.44%, sensitivity of 84.59%, specificity of 83.42%, and an area under the curve of 0.84.</p><p><strong>Conclusion: </strong>The ML-based approach, coupled with feature aggregation, enhances predictive performance, aiding clinical teams in understanding crucial features and the model's decision-making process.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251323300"},"PeriodicalIF":2.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}