Informatics in Medicine Unlocked最新文献

{"title":"End-to-end machine learning based discrimination of neoplastic and non-neoplastic intracerebral hemorrhage on computed tomography","authors":"Jawed Nawabi ,&nbsp;Sophia Schulze-Weddige ,&nbsp;Georg Lukas Baumgärtner ,&nbsp;Tobias Orth ,&nbsp;Andrea Dell'Orco ,&nbsp;Andrea Morotti ,&nbsp;Federico Mazzacane ,&nbsp;Helge Kniep ,&nbsp;Uta Hanning ,&nbsp;Michael Scheel ,&nbsp;Jens Fiehler ,&nbsp;Tobias Penzkofer","doi":"10.1016/j.imu.2025.101633","DOIUrl":"10.1016/j.imu.2025.101633","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop and evaluate a fully automated segmentation and classification tool for the discrimination of neoplastic and non-neoplastic intracerebral hemorrhage (ICH) on admission Computed Tomography (CT).</div></div><div><h3>Materials and methods</h3><div>Two models were developed using a retrospective dataset of acute ICH patients with unknown etiology upon admission, based on CT scans from a single institution (January 2016 to May 2020). An nnU-Net segmentation model was trained on manually segmented ICH and perihematomal edema (PHE) masks, alongside a ResNet-34 classification model for differentiating between neoplastic and non-neoplastic ICH. The combined tool was evaluated on the test set and validated on an external cohort. Validation performance was reevaluated after enriching the training data of the segmentation model. Evaluation metrics included accuracy (Acc), area under the curve (AUC), sensitivity, specificity, and Matthews Correlation Coefficient (MCC). Performance was compared to human raters.</div></div><div><h3>Results</h3><div>Among 291 patients, 116 (39.86 %) had neoplastic and 175 (60.14 %) non-neoplastic ICH. The tool achieved an Acc of 86 % and an AUC of 85 % with a sensitivity and specificity of 80 % and 93 % in the test set. On the validation cohort (n = 58), the tool achieved an AUC of 68 % reaching 83 % after retraining of the segmentation model. The tool achieved an MCC of 0.62, compared to 0.47–0.61 for the human raters.</div></div><div><h3>Conclusion</h3><div>The tool demonstrated high diagnostic performance with potential as a decision-aiding tool; however, it relies on multi-vendor data for improved robustness, warranting further validation across diverse datasets.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101633"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600504","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":"Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance","authors":"Chun-Ren Phang ,&nbsp;Shintaro Uehara ,&nbsp;Sachiko Kodera ,&nbsp;Akiko Yuasa ,&nbsp;Shin Kitamura ,&nbsp;Yohei Otaka ,&nbsp;Akimasa Hirata","doi":"10.1016/j.imu.2025.101643","DOIUrl":"10.1016/j.imu.2025.101643","url":null,"abstract":"<div><div>Stroke severity is associated with the presence or absence of motor-evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS). However, there is limited evidence regarding the relationship between MEP waveforms, post-stroke motor impairment, and functional performance. This study aimed to evaluate the predictive value of inter-trial correlation (ITC), a novel metric reflecting waveform consistency, along with MEP amplitude and resting motor threshold (rMT), in estimating post-stroke motor outcomes. Thirty-eight stroke participants were enrolled, and TMS was applied to the hotspot of the first dorsal interosseous muscle in the ipsilesional or contralesional hemisphere to elicit MEPs. MEP amplitude, ITC, and rMT were analyzed in 20 participants with detectable MEPs. Pearson correlation coefficient (PCC) analysis assessed the relationships between MEP features and motor outcomes, including the Stroke Impairment Assessment Set (SIAS), Fugl-Meyer Assessment (FMA), and Action Research Arm Test (ARAT). A linear support vector machine (SVM) was trained using leave-one-subject-out cross-validation to predict the motor outcomes. Participants without detectable MEPs (n = 18) had significantly lower motor scores than those with detectable MEPs did. MEP amplitude from the contralesional side was positively correlated with SIAS, FMA, and ARAT (PCC = 0.51, 0.47, and 0.55, respectively), whereas LICI amplitude and ITC from the ipsilesional side were negatively correlated with motor scores. The SVM model predicted motor outcomes with an R² of 0.42 and a normalized root mean square error of 0.26. A Gaussian classifier achieved 75 % accuracy in classifying motor outcome improvements. These findings suggest that bilateral MEP features, particularly those from the contralesional hemisphere, offer valuable prognostic information. This study proposes a practical framework for post-stroke motor outcome prediction based on MEP analysis with potential utility in individualized rehabilitation planning.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101643"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM","authors":"Kazuki Hebiguchi ,&nbsp;Hiroyoshi Togo ,&nbsp;Akimasa Hirata","doi":"10.1016/j.imu.2025.101624","DOIUrl":"10.1016/j.imu.2025.101624","url":null,"abstract":"<div><div>Wearable ECG devices encounter significant challenges in replicating the diagnostic capabilities of standard 12-lead ECGs, primarily due to the complexity of electrode placement and the need for specialized equipment. This study aims to develop a deep learning model capable of reconstructing complete 12-lead ECG waveforms using a minimal number of chest leads, thereby optimizing lead configurations for wearable ECG systems. Leveraging the PTB-XL ECG dataset, we preprocessed the signals to eliminate noise and trained a model integrating 1D convolutional layers with a Bi-directional Long Short-Term Memory (Bi-LSTM) architecture. Reconstruction performance was assessed using Pearson's correlation coefficient and root mean squared error (RMSE) across various input lead configurations, ranging from single to quintuple inputs. Our preprocessing and network architecture effectively capture both spatial and temporal features. The model achieved its highest reconstruction accuracy for leads located near the input leads, with performance gradually diminishing for more distant leads. Notably, the transitional zone between leads V₃ and V₄ presented reconstruction challenges due to polarity shifts. While increasing the number of input leads enhanced reconstruction accuracy and reduced variability, the improvements plateaued beyond the use of double input leads. Among configurations, double input leads, particularly those with two intervening leads between input pairs, offered an optimal balance between reconstruction accuracy and model complexity. This study highlights that accurate reconstruction of 12-lead ECG is achievable with only two input leads, providing a balance between diagnostic accuracy and reduced electrode requirements. These findings offer valuable insights for designing wearable ECG systems capable of reliable monitoring with fewer electrodes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101624"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372759","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":"Unveiling the secrets of neural network scaling for ECG classification","authors":"Byeong Tak Lee,&nbsp;Joon-myoung Kwon,&nbsp;Yong-Yeon Jo","doi":"10.1016/j.imu.2025.101639","DOIUrl":"10.1016/j.imu.2025.101639","url":null,"abstract":"<div><div>We present a new perspective on scaling neural networks for electrocardiograms (ECG). Although ResNet-based models are widely used in ECG classification, the potential benefits of network scaling remain unexplored. Our research investigates the impact of changes in the depth of layers, the number of channels, and the dimensions of the convolution kernels on performance. Contrary to computer vision practices, we found that shallower networks, with more channels and smaller kernels, lead to better performance for ECG classifications. Based on these findings, we provide insights that can guide the efficient development of models in practice. Finally, we explore why scaling hyperparameters affects ECG and computer vision differently. Our findings suggest that the inherent periodicity of the ECG signals plays a crucial role in this difference.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101639"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informatics-driven unsupervised learning of comorbidity clusters for COVID-19 reinfection risk: A finite mixture modeling approach","authors":"Grant B. Morgan ,&nbsp;Andreas Stamatis ,&nbsp;Chelsea C. Yager ,&nbsp;Ali Boolani","doi":"10.1016/j.imu.2025.101649","DOIUrl":"10.1016/j.imu.2025.101649","url":null,"abstract":"<div><h3>Purpose</h3><div>This study applied an informatics-focused, unsupervised learning framework (finite mixture modeling) to determine whether distinct clusters of coexisting conditions among patients with coronavirus disease 2019 (COVID-19) are associated with multiple (reinfection) versus single infections.</div></div><div><h3>Methods</h3><div>We analyzed 42,974 patient records containing COVID-19 diagnoses using an machine learning classification algorithm to identify comorbidity profiles. Of nearly 850 recorded conditions, 29 were retained if they occurred in at least 5 % of the sample. We then compared patients with single versus multiple COVID-19 diagnoses within each profile.</div></div><div><h3>Results</h3><div>Three comorbidity profiles emerged. The first profile (Minimal Comorbidity) was the largest (67 % of sample) and was characterized by few additional conditions. Patients classified into this profile were also 20–30 years younger, on average, than members of the other profiles. The second (Elevated Select Comorbidity) profile consisted of 24 % of the sample and was characterized by moderate-risk factors such as hypertension, hyperlipidemia, and acute respiratory failure. The third (High Comorbidity Burden) third was represented by 9 % of the sample and was characterized by conditions related to cardiovascular, renal, endocrine, and respiratory systems. Among the high-burden group, 30 % experienced reinfection, versus only 9 % in the minimal group. Overall, patients with more extensive cardiometabolic or pulmonary conditions were more likely to experience repeated infection.</div></div><div><h3>Conclusions</h3><div>By identifying and characterizing comorbidity clusters, this informatics-based approach offers deeper insight into COVID-19 reinfection dynamics. The findings may support targeted prevention, data-driven resource allocation, and precision medicine strategies by highlighting subgroups at elevated risk. Moreover, the unsupervised modeling framework is potentially adaptable to other multifactorial conditions, underscoring its broader utility in medical informatics.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101649"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review","authors":"Radwan Qasrawi ,&nbsp;Ghada Issa ,&nbsp;Suliman Thwib ,&nbsp;Razan AbuGhoush ,&nbsp;Malak Amro ,&nbsp;Raghad Ayyad ,&nbsp;Stephanny Vicuna ,&nbsp;Eman Badran ,&nbsp;Yousef Khader ,&nbsp;Raeda Al Qutob ,&nbsp;Faris Al Bakri ,&nbsp;Hana Trigui ,&nbsp;Elie Sokhn ,&nbsp;Emmanuel Musa ,&nbsp;Jude Dzevela Kong","doi":"10.1016/j.imu.2025.101651","DOIUrl":"10.1016/j.imu.2025.101651","url":null,"abstract":"<div><div>This systematic review analyzes the implementation and effectiveness of machine learning (ML) approaches for infectious disease surveillance and prediction across the Middle East and North Africa (MENA) region. Adhering to PRISMA guidelines, studies published between 2016 and 2024 were examined to assess model structures, performance metrics, and dataset characteristics. The findings reveal a predominance of deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieving mean accuracy rates of 96.3 % in pathogen detection from medical imaging. Random Forest algorithms demonstrated superior performance in disease outbreak prediction, with mean ACC scores of 0.85. Iran, Saudi Arabia, and Egypt emerged as regional leaders, collectively contributing 54 % of the analyzed studies. The temporal analysis showed peak research output in 2022 (n = 30 studies), followed by a 25 % decline in 2023. Despite promising performance, challenges such as data quality, infrastructural limitations, and algorithmic bias persist. This review highlights the need for standardized protocols, enhanced digital infrastructure, and collaborative efforts to realize the full potential of ML in enhancing public health interventions across the region. Future research directions should prioritize multi-center validation studies, standardized reporting frameworks, and integration of diverse data modalities to enhance model robustness and clinical applicability.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101651"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing users and intention to use online health information resources: A comprehensive study","authors":"André Michaud ,&nbsp;Virginie Blanchette ,&nbsp;François Boudreau ,&nbsp;Sarah Lafontaine ,&nbsp;Denis Leroux ,&nbsp;Paule Miquelon ,&nbsp;Michel Vallée ,&nbsp;Joany Rousseau-Bédard ,&nbsp;Lyne Cloutier","doi":"10.1016/j.imu.2025.101640","DOIUrl":"10.1016/j.imu.2025.101640","url":null,"abstract":"","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101640"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electronic health records in non-hospital settings of developing economies: A systematic review on enablers and barriers","authors":"Bejie Rodriguez ,&nbsp;Joenelyn Kaye Demoral ,&nbsp;Jan Jacob Carpio ,&nbsp;Alan Napoleon Gultia ,&nbsp;Gloria Shiela Coyoca ,&nbsp;Cecilio Garciano Jr. ,&nbsp;Lemuel Clark Velasco","doi":"10.1016/j.imu.2025.101634","DOIUrl":"10.1016/j.imu.2025.101634","url":null,"abstract":"<div><div>In recent years, rapid advancements in Information and Communications Technology (ICT) have greatly transformed the healthcare landscape by streamlining health data management and providing decision-makers with secure and convenient access to health records. In developing economies, limited resources hinder healthcare access. Implementing EHRs in non-hospital settings is essential for enhancing healthcare quality and accessibility. While existing literature supports EHR use, further research is needed to pinpoint specific barriers and enablers. Using PRISMA guidelines, 18 relevant articles were systematically analyzed with the Human, Organization, and Technology Fit (HOT-fit) framework to examine these factors in non-hospital settings within developing economies. This study found that human factors take precedence in both enablers and barriers. The first two barriers emphasize the human element, highlighting the critical importance of addressing individual user challenges. However, organizational issues take on a supporting role, highlighting the possibility that the prominence of user-centric challenges stems from the lack of devolution of governance and leadership in non-hospital settings. Additionally, the findings indicate that prioritizing robust IT infrastructure, which meets both functional and usability requirements, remains a fundamental concern for EHR implementation. By focusing on the enablers and barriers of EHR implementation, this study highlights the research gaps that can be explored as well as the potential and challenges that are faced by healthcare systems within the non-hospital settings of -developing economies. From these findings, we infer that further research is needed to identify specific training components for EHR systems to enable individuals for effective system use in non-hospital settings.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101634"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642421","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":"Neonatal asphyxia prediction using features extracted from cardiotocography data by explainable artificial intelligence","authors":"Hayato Kinoshita ,&nbsp;Hiroaki Fukunishi ,&nbsp;Chihiro Shibata ,&nbsp;Toyofumi Hirakawa ,&nbsp;Kohei Miyata ,&nbsp;Fusanori Yotsumoto","doi":"10.1016/j.imu.2025.101636","DOIUrl":"10.1016/j.imu.2025.101636","url":null,"abstract":"<div><h3>Background and objective</h3><div>Developing Artificial Intelligence (AI)-assisted technology for cardiotocography (CTG) monitoring system is highly anticipated in the field of obstetrics. This study developed a neonatal asphyxia prediction model to assist obstetricians and practitioners in making early treatment decisions in clinical practice.</div></div><div><h3>Methods</h3><div>Using 32,711 CTG records, features based on fetal heart rate (FHR) were extracted following Japanese Society of Obstetrics and Gynecology (JSOG) guidelines. The machine learning algorithm LightGBM was adopted to construct a binary prediction model of normal and abnormal states for newborns after delivery. To address the data imbalance between normal and abnormal samples, multiple prediction models were constructed using the underbagging technique. Furthermore, features impacting neonatal asphyxia were analyzed using the SHapley Additive exPlanations (SHAP), an explainable artificial intelligence (XAI) technique.</div></div><div><h3>Results</h3><div>The best prediction model used the Apgar score as the outcome variable and 13 FHR-based features + maternal age as the feature set, with an area under the curve of 0.759. This performance is reliable because this study used 32,711 CTG records, whereas most prior studies used datasets with only a few hundred records. When risk factors were analyzed via SHAP, the top three features were mean FHR, frequency of acceleration, and frequency of marked variability. The relationship between many of the features and abnormal risk corresponded to the CTG interpretation of the JSOG guidelines.</div></div><div><h3>Conclusions</h3><div>This study demonstrated reliable prediction performance using a large dataset along with the rationale behind its prediction. These results will facilitate the use of AI-assisted technology in clinical practice. In the future, it is expected that XAI technology will be integrated into real-time CTG monitoring systems, and that the display of associated risk factors will occur simultaneously with risk alerts.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101636"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611190","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":"Investigating the accuracy of neural networks for blood pressure prediction in the ICU","authors":"Charles J. Gillan,&nbsp;Bartosz Gorecki","doi":"10.1016/j.imu.2025.101635","DOIUrl":"10.1016/j.imu.2025.101635","url":null,"abstract":"<div><div>This paper reports on research which investigates the viability of artificial neural networks, used in an ICU environment, for predicting both systolic and diastolic blood pressure up to 1 h ahead. In this environment, patients often receive pharmacological intervention to increase or decrease blood pressure. The physiological state of an ICU patient is therefore quite different to a hyper or hypotensive patient outside hospital, suggesting that predicting blood pressure in this environment is more challenging The work investigates whether building neural network architectures with multivariate input data is capable of predicting blood pressures in this environment. Our work uses skin temperature and heart rate readings in addition to systolic and diastolic blood pressure. Two types of neural network are explored are explored in this paper: an encoder-decoder long short-term memory architecture and, separately, a convolutional neural network architecture. The top-performing configuration, when using a 70 %–30 % train-test split of data, is a convolutional neural network model. This predicted systolic and diastolic blood pressures for a patient with an error of approximately _{3<em>.</em>4 %}. These results are at the same level of accuracy as work on blood pressure prediction outside the ICU environment. Our work shows that neural networks are a viable tool for short term prediction of arterial blood pressures in an ICU context.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101635"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}