Smart Health最新文献

{"title":"IoT-based vital sign monitoring: A literature review","authors":"Alexandre Andrade,&nbsp;Arthur Tassinari Cabral,&nbsp;Bárbara Bellini,&nbsp;Vinicius Facco Rodrigues,&nbsp;Rodrigo da Rosa Righi,&nbsp;Cristiano André da Costa,&nbsp;Jorge Luis Victória Barbosa","doi":"10.1016/j.smhl.2024.100462","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100462","url":null,"abstract":"<div><p>The Internet of Things (IoT) applied to the health area is in significant growth, with companies putting effort into developing specialized devices. Remote patient healthcare monitoring, in particular, benefits society as it unburdens hospitals and helps patients with chronic diseases. Analyzing the health status with IoT and Artificial Intelligence (AI) is new in the digital community. The literature yet presents a limited number of references explicitly concerning the topic of qualified data acquisition. In this sense, the present literature review aims to update the joint subject of IoT and vital signs, seeking to understand the state-of-the-art and future directions. We have analyzed 78 articles and IoT manufacturer websites that address vital signs collection to answer a group of primary and specific questions. In particular, we revisited architectures, communication protocols, data acquisition mechanisms, evaluation metrics, and how to efficiently transfer data through the lens of sensors, actuators, and healthcare. Currently, two themes are considered as promising directions for studies in the joint area of IoT and vital sign-based healthcare monitoring. The first is the connection promotion between third-party applications and IoT devices to collect and process time-critical data with the support of edge, fog, and cloud infrastructures. The second theme again brings the focus to data compression methodologies since monitoring vital signs in a smart city geographical area naturally requires strategies to optimize network bandwidth consumption and data storage on computational resources. Moreover, both themes are directly linked to energy-saving approaches and quality of service (QoS) for efficient patient healthcare checking.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100462"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140195632","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":"Patient flow modeling and simulation to study HAI incidence in an Emergency Department","authors":"Sarawat Murtaza Sara ,&nbsp;Ravi Chandra Thota ,&nbsp;Md Yusuf Sarwar Uddin ,&nbsp;Majid Bani-Yaghoub ,&nbsp;Gary Sutkin ,&nbsp;Mohamed Nezar Abourraja","doi":"10.1016/j.smhl.2024.100467","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100467","url":null,"abstract":"<div><p>Healthcare-associated infections (HAIs), or nosocomial infections, refer to patients getting new infections while getting treatment for an existing condition in a healthcare facility. HAI poses a significant challenge in healthcare delivery that results in higher rates of mortality and morbidity as well as a longer duration of hospital stay. While the real cause of HAI in a hospital varies widely and in most cases untraceable, it is popularly believed that patient flow in a hospital—which hospital units patients visit and where they spend the most time since their admission into the hospital—can trace back to HAI incidence in the hospital. Based on this observation, we, in this paper, model and simulate patient flow in an emergency department of a hospital and then utilize the developed model to study HAI incidence therein. We obtain (a) a flowchart of patient movement (admission to discharge) and (b) anonymous patient data from University Health Medical Center for a duration of 11 months (Aug 2022–June 2023). Based on these data, we develop and validate the patient flow model. Our model captures patient movement in different areas of a typical emergency department, such as triage, waiting room, and minor procedure rooms. We employ the discrete-event simulation (DES) technique to model patient flow and associated HAI infections using the simulation software, Anylogic. Our simulation results show that the rates of HAI incidence are proportional to both the specific areas patients occupy and the duration of their stay. By utilizing our model, hospital administrators and infection control teams can implement targeted strategies to reduce the incidence of HAI and enhance patient safety, ultimately leading to improved healthcare outcomes and more efficient resource allocation.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100467"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297030","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":"EQS-Band Human Body Communication through frequency hopping and MCU-Based transmitter","authors":"Abdelhay Ali,&nbsp;Amr N. Abdelrahman,&nbsp;Abdulkadir Celik,&nbsp;Ahmed M. Eltawil","doi":"10.1016/j.smhl.2024.100471","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100471","url":null,"abstract":"<div><p>Human Body Communication (HBC) is an emerging technology that uses the human body as a communication channel. It offers significant advantages over traditional RF techniques in terms of power consumption and security. In recent developments, Electro-quasistatic HBC (EQS-HBC) in the frequency band below 1 MHz has been employed to enable communication without signal radiation beyond the body, effectively turning the body into a wired communication medium. This paper delves into the application of the EQS band for HBC. Experimental results show the determinantal effect of intermittent noise that sporadically disrupts communications across the band of interest. To address this challenge, we introduce an innovative frequency-hopping transceiver system, which allows the transmitter to seamlessly adapt to different frequencies. In addition, we present a miniature transmitter design, incorporating a simplified micro-controller unit (MCU) to facilitate the implementation of HBC. Furthermore, to validate this proposed design, we present a fully functional prototype of an HBC system that effectively employs frequency hopping techniques for practical applications.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100471"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341331","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":"UbiHeart: A novel approach for non-invasive blood pressure monitoring through real-time facial video","authors":"Kazi Shafiul Alam,&nbsp;Sayed Mashroor Mamun,&nbsp;Masud Rabbani,&nbsp;Parama Sridevi,&nbsp;Sheikh Iqbal Ahamed","doi":"10.1016/j.smhl.2024.100473","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100473","url":null,"abstract":"<div><p>Monitoring blood pressure (BP) is an essential component of evaluating cardiovascular health, aiding in the early detection and management of hypertension-related complications. Traditional methods of BP measurement often involve invasive or cumbersome devices, leading to discomfort and reduced compliance. We propose a framework to monitor BP non-invasively, analyzing the face video captured by a webcam or smartphone camera leveraging the relationship of image-based Pulse Transit Time (iPTT) and Heart Rate Variability (HRV) with BP. We have built a dataset of 90 sets of collected videos using a mobile phone front camera and BP data from a standard digital BP monitor from 12 individuals from an approved Institutional Review Board (IRB) to evaluate our system. We have got a Mean Absolute Error (MAE) of <span><math><mrow><mn>10</mn><mo>.</mo><mn>35</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>2</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for systolic BP (SBP) and <span><math><mrow><mn>7</mn><mo>.</mo><mn>8</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>1</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for diastolic BP (DBP) while using the HRV representation RMSSD. On the other hand, an MAE of <span><math><mrow><mn>8</mn><mo>.</mo><mn>25</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>3</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for SBP and <span><math><mrow><mn>7</mn><mo>.</mo><mn>7</mn><mo>+</mo><mo>/</mo><mo>−</mo><mn>2</mn><mo>.</mo><mn>5</mn></mrow></math></span> mmHg for DBP while using the HRV representation SDRR. Finally, we have developed a framework and built a real-time system to monitor BP as a mobile and web-based application that can facilitate early detection of trends and anomalies, allowing healthcare providers to intervene promptly and personalize treatment plans.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100473"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297029","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":"Multi-level cancer profiling through joint cell-graph representations","authors":"Luis Carlos Rivera Monroy ,&nbsp;Leonhard Rist ,&nbsp;Frauke Wilm ,&nbsp;Christian Ostalecki ,&nbsp;Andreas Baur ,&nbsp;Julio Vera ,&nbsp;Katharina Breininger ,&nbsp;Andreas Maier","doi":"10.1016/j.smhl.2024.100470","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100470","url":null,"abstract":"<div><p>Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of <span><math><mrow><mn>88</mn><mtext>%</mtext></mrow></math></span>, and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining techniques.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100470"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000266/pdfft?md5=7c470921f13bec160c26b55acac4f145&pid=1-s2.0-S2352648324000266-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140290856","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":"Adaptive attention-aware fusion for human-in-the-loop behavioral health detection","authors":"Martin Brown ,&nbsp;Abm Adnan Azmee ,&nbsp;Md. Abdullah Al Hafiz Khan ,&nbsp;Dominic Thomas ,&nbsp;Yong Pei ,&nbsp;Monica Nandan","doi":"10.1016/j.smhl.2024.100475","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100475","url":null,"abstract":"<div><p>Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. In this work, we develop a tool to automatically detect behavioral health cases from police public narrative reports by identifying behavioral health indicator signals. We propose a novel adaptive attention-aware fusion model for detecting behavioral health signals in sensitive police reports. Our model leverages contextual and semantic information from the reports and relevant behavioral health cues as keywords from a pre-trained attention-weighted keyword-based model. Our model also employs label self-attention mechanisms to correlate label embeddings with the report and keyword representations. Furthermore, we propose a novel clustering-based uncertainty-enabled informative sampling query strategy to integrate humans-in-the-loop in the active learning framework to reduce required annotation from experts. This querying strategy selects the most informative and diverse samples for expert annotation. Our experimental results showed that the proposed model outperforms state-of-the-art classifiers on a dataset of 300 manually annotated ground truth police reports, achieving an accuracy of 87.58% and an F1-score of 85.67%. Applying our querying strategy to our proposed model increased the detection of behavioral health, achieving an accuracy of 92% and an F1-score of 91.1%. Also, our proposed model achieves an accuracy score of 93.75% and an F1-score of 93.61% on unseen samples. Lastly, our proposed model demonstrates its interpretability by extracting the keywords associated with each behavioral health category.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100475"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140343877","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":"Explainable AI and transformer models: Unraveling the nutritional influences on Alzheimer's disease mortality","authors":"Ziming Liu ,&nbsp;Longjian Liu ,&nbsp;Robert E. Heidel ,&nbsp;Xiaopeng Zhao","doi":"10.1016/j.smhl.2024.100478","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100478","url":null,"abstract":"<div><p>This pioneering study introduces the use of transformer-based machine learning models and explainable AI approaches to explore the impact of nutrition on Alzheimer's disease (AD) mortality. Using data from the Third National Health and Nutrition Examination Survey (Nhanes iii 1988 to 1994) and the NHANES III Mortality-Linked File (2019) databases, we investigate the intricate relationship between various nutritional factors and AD mortality. Our approach features a novel application of transformer models, which are then benchmarked against established methods like random forests and support vector machines. This comparison not only underscores the strengths of transformer models in handling complex medical datasets but also highlights their potential for providing deeper insights into disease progression. Key findings, such as the significant roles of Platelet distribution width in AD mortality in transformer and Serum Vitamin B12 in random forest, are enhanced by the use of Explainable Artificial Intelligence (XAI), particularly the Shapley Additive Explanations (SHAP) and the integrated gradient methods. This study serves as a vital step forward in applying advanced AI techniques to medical research, offering new perspectives in understanding and combating Alzheimer's Disease.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100478"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320797","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":"Application of time series analysis to classify therapeutic breathing patterns","authors":"João Lucas Oliveira Canhoto ,&nbsp;Paulo Salgado Gomes de Mattos Neto ,&nbsp;Taiwan Roberto Barbosa ,&nbsp;José Emmanuel Matias da Silva Santos ,&nbsp;Igor Mauricio de Campos ,&nbsp;Geraldo Leite Maia Junior ,&nbsp;João Victor Cordeiro Coutinho ,&nbsp;Márcio Evaristo da Cruz Brito ,&nbsp;Anna Luisa Araújo Brito ,&nbsp;Daniella Cunha Brandão ,&nbsp;Armele de Fátima Dornelas de Andrade ,&nbsp;Herbert Albérico de Sá Leitão ,&nbsp;Shirley Lima Campos","doi":"10.1016/j.smhl.2024.100460","DOIUrl":"10.1016/j.smhl.2024.100460","url":null,"abstract":"<div><h3>Objective</h3><p>Compare various methods for measuring time series similarity in order to classify referenced therapeutic breathing patterns (BP) used in respiratory disorder rehabilitation.</p></div><div><h3>Methods</h3><p>This experimental study involved the collection of respiratory signals during specified breathing exercises conducted with healthy volunteers. The study employed a screening phase using a k-NN classifier and eight distance measurement methods, including Minkowski Distance, Dynamic Time Warping-DTW (including FastDTW and constrained-cDTW variations), Longest Common Subsequence-LCSS, Edit Distance on Real Sequences-EDR, Time Warp Edit Distance-TWEED, and Minimum Jump Costs-MJC. Two distinct approaches were employed for classifying therapeutic BP based on time series similarity: (1) using the k-Shape algorithm for clustering, and 2) integrating methods to represent therapeutic BP and classify test curves using the most relevant measurement methods obtained from the first approach.</p></div><div><h3>Results</h3><p>Among the two tested approaches, the combination of the cDTW algorithm and Minkowski distance (p = 2), using the 1-NN classifier, achieved the highest scores in this study, closely matching the metrics obtained from visual inspection conducted by human evaluators.</p></div><div><h3>Conclusion</h3><p>The use of combined classification methods in the analysis of flow curves referring to therapeutic breathing patterns improves the classification results, with metrics closely aligned with those obtained through visual evaluation conducted by individuals.</p></div><div><h3>Significance</h3><p>Time series analysis methods proved to be sensitive to classify respiratory flow curves equivalent to therapeutic breathing patterns used in respiratory disorder rehabilitation. This methodology can be used to monitor respiratory curves in new applications and implementation in devices for evaluating and treating the ventilatory pattern.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100460"},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091152","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":"Few-shot meta-learning for pre-symptomatic detection of Covid-19 from limited health tracker data","authors":"Atifa Sarwar,&nbsp;Abdulsalam Almadani,&nbsp;Emmanuel O. Agu","doi":"10.1016/j.smhl.2024.100459","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100459","url":null,"abstract":"<div><p>Detecting (or screening for) Covid-19 even before symptoms fully manifest, could enable patients to receive timely and life-saving treatment. Prior work has demonstrated that heart rate and step data from low-end wearables analyzed using deep learning models can detect Covid-19 reliably. However, significant individual differences in vital sign manifestation (high inter-subject variability) present a challenge to the generalization of deep learning models across diverse users. The limited amount of data in many medical scenarios further exacerbates this issue. Consequently, neural network models that can learn from limited vital sign data and varied inter-subject patterns are compelling. Meta-learning has emerged as a powerful technique for tackling various machine learning challenges, including insufficient data, domain shifts across datasets, and issues with generalization. This study proposes <em>MetaCovid</em>, a deep adaptation framework that employs meta-learning to address the variability of vital sign manifestation between subjects using only two days of data in order to detect Covid-19 before symptoms manifest. <em>MetaCovid</em> leverages heart rate and step measurements collected from consumer-grade health trackers over the preceding 2 days, extracts 45 digital bio-markers (features), which along with raw data, are fed into a deep GRU-based network with an attention mechanism, followed by uncertainty filtering. <em>MetaCovid</em> is trained using OC-MAML, a one-class few-shot MAML variant that adapts to the target distribution/user using only samples from the majority class. <em>MetaCovid</em> generalized well across two relatively small, publicly available Covid-19 datasets, achieving a recall of 0.81 and 0.92, and detecting 61% (14 out of 23) and 50% (17 out of 34) of users infected with Covid-19 before symptom onset. When OC-MAML was excluded from <em>MetaCovid</em> in an ablation study, the F₂ score dropped by 36%, highlighting that meta-learning indeed facilitates adaptation of deep sensing models to varying vital sign patterns. Notably, <em>MetaCovid</em> outperforms the current state-of-art method by predicting Covid-19 early on day <span><math><mi>N</mi></math></span> using heart rate and step measurements from only the preceding 2 days compared to 28 days, reducing data requirements by 93%. To the best of our knowledge, our study is the first to propose utilizing meta-learning to mitigate vital sign variability with limited data for Covid-19 screening. We believe that <em>MetaCovid</em> will pave the way for innovative Covid-19 interventions that are accurate even with limited data and help contain the spread of infectious diseases in the future.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100459"},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031506","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":"Retraction notice to ‘Blockchain technology in the future of healthcare’ [Smart Health 23 (2022) 100223]","authors":"Quazi Mamun","doi":"10.1016/j.smhl.2024.100458","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100458","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100458"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235264832400014X/pdfft?md5=fac559044334cbd8c0b8378ca18a3a66&pid=1-s2.0-S235264832400014X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139999226","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}