Technology and Health Care最新文献_第7页

Leveraging personal health records for early heart failure risk prediction through AI-driven modeling. 通过人工智能驱动的建模，利用个人健康记录进行早期心力衰竭风险预测。

IF 1.3 4区医学

Technology and Health Care Pub Date : 2026-06-17 DOI: 10.1177/09287329261455443

Behzad Sobhani, Amir Jamshidnezhad, Ebrahim Heidari Sardabi

{"title":"Leveraging personal health records for early heart failure risk prediction through AI-driven modeling.","authors":"Behzad Sobhani, Amir Jamshidnezhad, Ebrahim Heidari Sardabi","doi":"10.1177/09287329261455443","DOIUrl":"https://doi.org/10.1177/09287329261455443","url":null,"abstract":"BackgroundCardiovascular disease, especially heart failure, is a substantial global health issue. By integrating PHR with machine learning, early disease detection could be made possible.ObjectiveIn this study, an attempt was made to develop and fine-tune an AI model that would forecast the likelihood of heart failure based on patient data in PHRs.MethodsData from 1025 patients and 12 clinical/demographic criteria were used. An untuned multilayer perceptron (MLP) with two hidden layers (10 and 5 neurons) was first trained (1000 epochs). Then, using the same dataset, we performed systematic hyperparameter tuning (grid search with 5-fold cross-validation) for Logistic Regression, Random Forest, SVM, and an enhanced MLP. Performance metrics included accuracy, precision, recall, F1-score, MCC, and ROC-AUC with 95% confidence intervals.ResultsThe original untuned MLP gave a mean accuracy of 0.7244 (±0.0245) and mean ROC-AUC of 0.724 (±0.038). After tuning, Random Forest achieved the highest performance (AUC = 0.959, 95% CI 0.924-0.986; accuracy = 0.890). The tuned MLP reached AUC = 0.830 (CI 0.763-0.893), outperforming the untuned version and showing comparable performance to Logistic Regression (AUC = 0.824) and SVM (AUC = 0.842).ConclusionThese results suggest potential use of AI models to anticipate the risk of heart failure from a subject's medical history and provide an avenue toward scalable and personal medicine, resulting in improved early prevention and treatment of cardiovascular disease.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261455443"},"PeriodicalIF":1.3,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leadership styles and work outcome in healthcare sector: Insights from bibliometric analysis. 医疗保健部门的领导风格和工作成果：来自文献计量分析的见解。

IF 1.8 4区医学

Technology and Health Care Pub Date : 2026-06-11 DOI: 10.1177/09287329261449717

Udin Udin

{"title":"Leadership styles and work outcome in healthcare sector: Insights from bibliometric analysis.","authors":"Udin Udin","doi":"10.1177/09287329261449717","DOIUrl":"https://doi.org/10.1177/09287329261449717","url":null,"abstract":"BackgroundEffective leadership is crucial for delivering high-quality healthcare, optimizing employee performance, and improving patient outcomes.ObjectiveThis study conducts a bibliometric analysis to examine leadership styles in the healthcare sector, identifying key research trends, influential works, and their impact on work outcomes.MethodsUsing Scopus-indexed publications (2010-2024), this study analyzes eighty-three relevant documents with VOSviewer.ResultsThe findings reveal that various leadership styles, such as transformational, transactional, authentic, collaborative, inclusive, servant, and paternalistic leadership, have a significant impact on both individual and organizational outcomes. Furthermore, transformational leadership is the most studied and influential style, promoting employee engagement, innovation, and better patient care. Transactional leadership ensures structure and efficiency but offers limited long-term benefits. Servant leadership fosters trust, collaboration, and ethical decision-making, while authentic leadership enhances transparency and psychological safety. Additionally, the study highlights a growing focus on healthcare leadership, with Medicine and Business, Management & Accounting as the dominant disciplines.ConclusionThis bibliometric study highlights the critical role of leadership styles in shaping work outcomes in the healthcare sector. Transformational leadership emerges as the most influential, fostering innovation, engagement, and improved patient care.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261449717"},"PeriodicalIF":1.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From data to prevention: A systematic review of artificial intelligence applications in sports injury prediction. 从数据到预防：人工智能在运动损伤预测中的应用综述。

IF 1.8 4区医学

Technology and Health Care Pub Date : 2026-06-11 DOI: 10.1177/09287329261455797

Chu-Hsuan Lee, Ming-Hui Chang, Zheng-Hao Li

{"title":"From data to prevention: A systematic review of artificial intelligence applications in sports injury prediction.","authors":"Chu-Hsuan Lee, Ming-Hui Chang, Zheng-Hao Li","doi":"10.1177/09287329261455797","DOIUrl":"https://doi.org/10.1177/09287329261455797","url":null,"abstract":"PurposeArtificial intelligence (AI) shows considerable potential for sports injury prediction, yet a comprehensive methodological review of its empirical applications remains limited. This study aimed to systematically review the empirical literature on the use of AI and machine learning (ML) for sports injury prediction.MethodsFollowing the PRISMA 2020 guidelines, a systematic search was conducted across PubMed, IEEE Xplore, SPORTDiscus, Web of Science, and Scopus for literature published between January 2015 and March 2026. In addition to the structured database search, a small number of database-recommended articles identified through platform recommendation functions were also screened for eligibility. After a multi-stage screening process, 18 empirical studies were included in the final qualitative synthesis.ResultsRisk of bias assessment using PROBAST indicated that only one study (5.6%) had a low overall risk of bias, whereas most studies were judged to be high risk, primarily because of weaknesses in the analysis domain and reliance on internal validation. Across the included studies, AI-based models demonstrated potential for handling multidimensional training load, physiological, biomechanical, and psychological data; however, most prediction models relied exclusively on internal validation, limiting confidence in their generalizability.ConclusionAI demonstrates clear promise for sports injury prediction, but the current evidence base remains constrained by limited external validation, inconsistent reporting practices, and the continued overrepresentation of male cohorts. Future research should prioritize methodological rigor, broader geographic and demographic representation, standardized reporting, and explainable AI (XAI) approaches to enhance the trustworthiness and practical utility of these models for coaches and clinicians.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261455797"},"PeriodicalIF":1.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network analysis revealing research focus of the German Congress of Orthopedics and Trauma Surgery 2021. 网络分析揭示2021年德国骨科和创伤外科大会的研究重点。

IF 1.3 4区医学

Technology and Health Care Pub Date : 2026-06-05 DOI: 10.1177/09287329261450047

Jonas Roos, Andreas Christian Strauss, Sebastian Koob, Frank Sebastian Fröschen, Alberto Alfieri Zellner, Martin Gathen, Dieter Christian Wirtz, Tom Jansen, Robert Kaczmarczyk

{"title":"Network analysis revealing research focus of the German Congress of Orthopedics and Trauma Surgery 2021.","authors":"Jonas Roos, Andreas Christian Strauss, Sebastian Koob, Frank Sebastian Fröschen, Alberto Alfieri Zellner, Martin Gathen, Dieter Christian Wirtz, Tom Jansen, Robert Kaczmarczyk","doi":"10.1177/09287329261450047","DOIUrl":"10.1177/09287329261450047","url":null,"abstract":"BackgroundThe German congress for orthopedics and trauma surgery is the largest congress of this specialty in Europe. It reflects the latest scientific achievements and thus has a lasting impact on both, clinical practice and research. Understanding what is currently moving the specialist groups therefore helps to understand where orthopedics and trauma surgery is heading.MethodsContribution titles, topics, authors, and cities were extracted from the German Medical Science database (https://www.egms.de/) and using three distinct network analyses: an author network, a city network, and an entity network. Internal ratings were obtained from the congress committee for further elaboration.ResultsThe congress comprised 696 accepted papers, most of which were presented in German (72.41%) and in the form of lectures (57.61%), focusing on topics such as \"spine,\" \"basic research\" and \"polytrauma.\" The city network revealed geographical clusters, with Berlin, Munich and Dresden forming the most closely networked nodes. In the entity network, three major communities emerged, focusing on clinical care, fracture research and joint-related studies. Of the 1232 submissions, 85.80% received a majority favorable review, and 56.49% were accepted, with the acceptance rate increasing in proportion to the review score.ConclusionThe analysis of the DKOU 2021 revealed a broad scientific focus and research communities centered on clinical care, fracture studies and joint-related research. Transparent evaluation processes and global longitudinal comparisons are recommended to align research with the global burden of disease, taking into account the limitations of congress-based analyses and the influence of external factors such as the Covid-19 pandemic.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261450047"},"PeriodicalIF":1.3,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148165177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Engineering and experimental evaluation of a smart wireless glove for gamified upper limb rehabilitation in Parkinson's disease. 用于帕金森病游戏化上肢康复的智能无线手套的工程和实验评估。

IF 1.3 4区医学

Technology and Health Care Pub Date : 2026-05-01 Epub Date: 2026-04-24 DOI: 10.1177/09287329261438652

Kotryna Vaišnoraitė, Justinas Višinskas, Adriano de O Andrade, Luanne C Mendes, Camille M Alves, Kristina Daunoravičienė, Vytautas Abromavičius, Jurgita Žižienė

{"title":"Engineering and experimental evaluation of a smart wireless glove for gamified upper limb rehabilitation in Parkinson's disease.","authors":"Kotryna Vaišnoraitė, Justinas Višinskas, Adriano de O Andrade, Luanne C Mendes, Camille M Alves, Kristina Daunoravičienė, Vytautas Abromavičius, Jurgita Žižienė","doi":"10.1177/09287329261438652","DOIUrl":"10.1177/09287329261438652","url":null,"abstract":"BackgroundUpper extremity rehabilitation is critical for individuals with neurological disorders such as Parkinson's disease, where motor impairments significantly affect daily functionality.ObjectiveThis study presents the design and prototyping of a novel rehabilitation glove aimed at improving hand and wrist motor recovery through gamified therapy.MethodsThe proposed glove features wireless communication via a Wi-Fi network, adaptability to various hand sizes, and an integrated strength training mechanism using resistance bands and metal hooks. The glove consists of a lightweight forearm frame, a palm component, and a textile glove with embedded mechanical connectors, all designed based on anthropometric data. 3D printing techniques were employed using PLA and flexible TPU materials to create a modular, low-cost, and comfortable structure. The system interfaces with an interactive game, allowing users to control an avatar (bee) through hand movements, promoting motivation and active engagement.ResultsThe prototype effectively addresses key limitations of previous rehabilitation systems, including mobility, comfort, adaptability, and functionality.ConclusionThe proposed rehabilitation glove offers a promising solution for home-based neurorehabilitation.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"445-463"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural determinants of effective academic-industry technology transfer in biomedical engineering. 生物医学工程中有效的学术-产业技术转移的结构决定因素。

IF 1.3 4区医学

Technology and Health Care Pub Date : 2026-05-01 Epub Date: 2026-03-30 DOI: 10.1177/09287329251381507

Jolanta Pauk, Konrad Pauk, Anna Stec

{"title":"Structural determinants of effective academic-industry technology transfer in biomedical engineering.","authors":"Jolanta Pauk, Konrad Pauk, Anna Stec","doi":"10.1177/09287329251381507","DOIUrl":"10.1177/09287329251381507","url":null,"abstract":"BackgroundWhile macro-level factors influencing collaboration are well-studied, the daily operational processes between academia and industry remain unclear, especially in biomedical engineering, where coordination is complex and critical. This study fills this gap by analyzing decision-making, governance, and coordination practices from both perspectives to improve technology transfer and innovation outcomes.MethodsThis study employed a mixed-methods approach, including a structured questionnaire distributed among academic and industrial stakeholders, complemented by detailed case studies illustrating practical examples of collaboration in biomedical technology transfer.ResultsFindings reveal both converging and diverging motivations: academia values access to new technologies and societal impact, while industry prioritizes rapid market implementation and competitiveness. Key barriers include time constraints, conflicting publication and confidentiality demands, and challenges in intellectual property negotiations. Support mechanisms such as long-term funding, administrative support, and hybrid work models facilitate collaboration. Case studies demonstrate that successful partnerships hinge on clear regulatory frameworks and mutual understanding of objectives.ConclusionsFuture university-industry partnerships should prioritize clear communication, joint IP frameworks, and inclusive governance from the start. Involving clinical practitioners and end-users early improves product relevance, while proactive regulatory planning, supported by institutional guidance, helps avoid delays and compliance issues.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"435-444"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized EMG preprocessing and normalization for musculoskeletal simulation. 肌肉骨骼模拟的个性化肌电图预处理和规范化。

IF 1.3 4区医学

Technology and Health Care Pub Date : 2026-05-01 Epub Date: 2026-03-25 DOI: 10.1177/09287329261432854

Dovydas Cicėnas, Jurgita Žižienė, Kristina Daunoravičienė

{"title":"Personalized EMG preprocessing and normalization for musculoskeletal simulation.","authors":"Dovydas Cicėnas, Jurgita Žižienė, Kristina Daunoravičienė","doi":"10.1177/09287329261432854","DOIUrl":"10.1177/09287329261432854","url":null,"abstract":"BackgroundAccurate interpretation of electromyography (EMG) signals is essential for reliable control of musculoskeletal (MS) models in biomechanics and rehabilitation applications. Conventional preprocessing methods may not account for subject-specific signal characteristics and task-related muscle function.ObjectiveThis study aimed to develop and validate an adaptive and personalized EMG preprocessing pipeline to enhance the physiological accuracy of EMG-driven musculoskeletal models during elbow flexion-extension tasks.MethodsEMG signals from six upper limb muscles were recorded using a Delsys system while participants performed elbow flexion-extension movements. The signals were preprocessed using individualized spectral filtering and a dual-stage normalization approach. First, dynamic maximum voluntary contraction (MVC) based min-max normalization was applied to standardize signal amplitudes. Second, functional weighting was used to scale each muscle's activation based on its biomechanical contribution to the movement. The processed signals were used as input to an OpenSim elbow model, and resulting joint kinematics were compared to reference motion data captured by an Xsens system.ResultsThe EMG-driven OpenSim model showed strong agreement with the Xsens data, with correlation coefficients exceeding 0.98 and root mean square error (RMSE) values below 8°. While a minor systematic offset was observed, joint angle trajectories remained consistent and physiologically plausible across trials.ConclusionThe proposed subject-specific EMG preprocessing pipeline enhances the accuracy and interpretability of biomechanical models. Future research should explore adaptive signal alignment techniques and AI-based processing methods to improve model robustness in dynamic and wearable scenarios.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"371-391"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heart rate variability as a predictor of migraine: Sleep-time data analysis of pre-migraine nights. 心率变异性作为偏头痛的预测因子：偏头痛前夜晚的睡眠时间数据分析。

IF 1.3 4区医学

Technology and Health Care Pub Date : 2026-05-01 Epub Date: 2026-01-28 DOI: 10.1177/09287329251412968

Rūta Jankevičiūtė, Viroslava Kapustynska, Saulius Andruškevičius, Kristina Ryliškienė, Vytautas Abromavičius

{"title":"Heart rate variability as a predictor of migraine: Sleep-time data analysis of pre-migraine nights.","authors":"Rūta Jankevičiūtė, Viroslava Kapustynska, Saulius Andruškevičius, Kristina Ryliškienė, Vytautas Abromavičius","doi":"10.1177/09287329251412968","DOIUrl":"10.1177/09287329251412968","url":null,"abstract":"BackgroundMigraine is a complex neurological disorder characterized by recurrent, often debilitating headaches. Current evidence suggests that autonomic nervous system (ANS) alterations play a significant role in migraine pathophysiology, affecting sensory, limbic, and homeostatic processing. Heart rate variability (HRV), a well-established, noninvasive marker of ANS function, is associated with migraine severity and treatment efficacy.ObjectiveIn this study, we aimed to evaluate the use of wearable sensor technology in predicting migraine attacks by monitoring changes in the ANS during the prodrome phase.MethodsWe recruited 23 migraine sufferers and analyzed HRV during nocturnal sleep using wearable biosensors and machine learning, extracting HRV features from BVP signals and applying feature engineering to predict migraine episodes.ResultsThe analysis of HRV provides an important approach to migraine attack prediction, revealing significant individual variability in physiological responses. Overall, these results lay the groundwork for developing more effective and personalized migraine prediction models, which could lead to earlier interventions and improved participant outcomes. Future research should consider controlled inclusion of post-migraine nights, potentially leveraging additional statistical or machine learning techniques to mitigate misclassification risks while capturing these transitional dynamics.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"34 3","pages":"422-434"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147989661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introduction to the special issue on data-driven and translational approaches in biomedical engineering: From signal interpretation to rehabilitation technologies. 介绍生物医学工程中数据驱动和转化方法的特刊：从信号解释到康复技术。

IF 1.8 4区医学

Technology and Health Care Pub Date : 2026-05-01 Epub Date: 2026-04-27 DOI: 10.1177/09287329261446025

Kristina Daunoravičienė, Julius Griškevičius

引用次数: 0

Deciphering atrial repolarization morphology: A spline interpolation framework for atrial arrhythmia diagnosis. 解读心房复极形态：用于心房心律失常诊断的样条插值框架。

IF 1.8 4区医学

Technology and Health Care Pub Date : 2026-05-01 Epub Date: 2026-04-01 DOI: 10.1177/09287329261424119

Arya Bhardwaj, Bala Chakravarthy Neelapu, R Pradeep Kumar, Kunal Pal, J Sivaraman

{"title":"Deciphering atrial repolarization morphology: A spline interpolation framework for atrial arrhythmia diagnosis.","authors":"Arya Bhardwaj, Bala Chakravarthy Neelapu, R Pradeep Kumar, Kunal Pal, J Sivaraman","doi":"10.1177/09287329261424119","DOIUrl":"10.1177/09287329261424119","url":null,"abstract":"BackgroundThe characterization of atrial repolarization (Ta wave) remains largely elusive due to its inherently low amplitude and concealment beneath the dominant QRS complex.ObjectiveThis study aims to witness Ta wave within QRS complex using spline interpolation framework.Methodology10-s ECGs of 50 Sinus Tachycardia (SiT) and 20 Atrial Tachycardia (AT) were recorded using standard 12-lead. Lead-II signals were pre-processed for noise removal and fiducial points detection. Later, three spline models were used to synthesize hidden Ta wave using the datapoints from PR and ST segment. Further, validation analysis was performed to select the optimal spline model with the Ta wave of Atrio-Ventricular Block (AVB) ECG.ResultsIt was noted that the clamped cubic & B-spline interpolation model gave the best SSIM score of 0.7 and lowest power spectrum % difference of 1.33 of interpolated Ta wave within QRS complex. Further, Ta wave voltage and temporal features including Ta dispersion, area, peak location, Ta area/duration, duration/amplitude, and Ta2/Ta1 were crafted. Statistically significant P, Ta & P-Ta features were fed to seven Machine Learning (ML) models. The best ML models, were used to design a stacked ensemble architecture with combined P-Ta features to enhance the classification accuracy 99% and F1 score 0.99.ConclusionThe proposed method demonstrated that along with the existing P wave features, Ta wave features have potential in better classification of atrial arrhythmia, while interpolation model offers ease of implementation and adaptability to diverse clinical applications.","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"392-407"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0