Healthcare analytics (New York, N.Y.)最新文献

{"title":"A novel deep learning graph attention network for Alzheimer’s disease image segmentation","authors":"Md Easin Hasan ,&nbsp;Amy Wagler","doi":"10.1016/j.health.2024.100310","DOIUrl":"https://doi.org/10.1016/j.health.2024.100310","url":null,"abstract":"<div><p>Neuronal cell segmentation identifies and separates individual neurons in an image, typically to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function of neurons are understood. The proposed method is based on convolutional neural networks (CNNs) and graph attention networks (GATs) for segmenting biomedical images. A contracting path built upon a couple of convolution layers and max pooling is included in the architecture to capture context. After that, the GATs are applied to the captured context. In GATs, each node in the graph is associated with a vector of hidden features, and the model calculates attention coefficients between pairs of nodes. These attention coefficients are learned during training and can be used to weigh the contribution of each node’s features to the representation of the graph. An expanding path that utilizes the outputs generated by GATs paves the way for exact segmentation. The dataset comprises 606 microscopic images, mainly categorized into different cell types (astrocytes, cortex, and SHSY5Y). By implementing our proposed U-GAT algorithm, we obtained the highest accuracy of 86.5% and an F1 score of 0.719 compared to the CNN, U-Net, SegResNet, SegNet VGG16, and GAT benchmarking algorithms. This proposed method could help researchers in the biotech industry develop novel drugs since a more accurate deep-learning method is essential for segmenting complex images like neuronal images.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000121/pdfft?md5=bc3dc76aa6d276d3e986f4f45e80a2ae&pid=1-s2.0-S2772442524000121-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738218","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":"An investigation of the COVID-19 impact on liver cancer using exploratory and predictive analytics","authors":"Victor Chang,&nbsp;Rameshwari Mukeshkumar Patel,&nbsp;Meghana Ashok Ganatra,&nbsp;Qianwen Ariel Xu","doi":"10.1016/j.health.2024.100309","DOIUrl":"https://doi.org/10.1016/j.health.2024.100309","url":null,"abstract":"<div><p>This study presents the influence of COVID-19 and the pandemic on individuals diagnosed with hepatocellular carcinoma and intrahepatic cholangiocarcinoma, the two most common types of primary liver cancer. The study compares the effects before and after the pandemic on these patients. Additionally, it endeavors to predict the likelihood of survival for liver cancer patients. Our research will employ various methodologies to investigate this. Exploratory data analysis techniques are utilized, including univariate analysis, correlation analysis, bivariate analysis, chi-square testing, and T-sample testing. For predictive analytics, machine learning algorithms such as Logistic Regression, Decision Trees, Classification And Regression Tree (CART), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVMs) will be applied. For our outputs, Logistic Regression and SVMs emerged as top-performing algorithms, boasting a remarkable accuracy rate of 93%. The study reveals that COVID-19 affected all age groups similarly. However, a gender-based difference was observed, indicating that males faced a higher risk of both cancer and mortality. Furthermore, the study found that variables such as year, month, bleeding, cirrhosis, and previously known cirrhosis did not significantly influence patient survival.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100309"},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277244252400011X/pdfft?md5=29ce6fe47398e94a0c12f7634a551b42&pid=1-s2.0-S277244252400011X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational fractional order model for optimal control of wearable healthcare monitoring devices for maternal health","authors":"Onuora Ogechukwu Nneka ,&nbsp;Kennedy Chinedu Okafor ,&nbsp;Christopher A. Nwabueze ,&nbsp;Chimaihe B Mbachu ,&nbsp;J.P. Iloh ,&nbsp;Titus Ifeanyi Chinebu ,&nbsp;Bamidele Adebisi ,&nbsp;Okoronkwo Chukwunenye Anthony","doi":"10.1016/j.health.2024.100308","DOIUrl":"https://doi.org/10.1016/j.health.2024.100308","url":null,"abstract":"<div><p>The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model's positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000108/pdfft?md5=e6e07dfd7589ef9a68cbacfc42d252a0&pid=1-s2.0-S2772442524000108-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A triplanar ensemble model for brain tumor segmentation with volumetric multiparametric magnetic resonance images","authors":"Snehal Rajput ,&nbsp;Rupal Kapdi ,&nbsp;Mohendra Roy ,&nbsp;Mehul S. Raval","doi":"10.1016/j.health.2024.100307","DOIUrl":"https://doi.org/10.1016/j.health.2024.100307","url":null,"abstract":"<div><p>Automated segmentation methods can produce faster segmentation of tumors in medical images, aiding medical professionals in diagnosis and treatment plans. A 3D U-Net method excels in this task but has high computational costs due to large model parameters, which limits their application under resource constraints. This study targets an optimized triplanar (2.5D) model ensemble to generate accurate segmentation with fewer parameters. The proposed triplanar model uses spatial and channel attention mechanisms and information from multiple orthogonal planar views to predict segmentation labels. In particular, we studied the optimum filter size to improve the accuracy without increasing the network complexity. The model generated output is further post-processed to fine-tune the segmentation results. The Dice similarity coefficients (Dice-score) of the Brain Tumor Segmentation (BraTS) 2020 training set for enhancing tumor (ET), whole tumor (WT), and tumor core (TC) are 0.736, 0.896, and 0.841, whereas, for the validation set, they are 0.713, 0.873, and 0.778, respectively. The proposed base model has only <span><math><mrow><mn>10</mn><mo>.</mo><mn>25</mn><mspace></mspace><mi>M</mi></mrow></math></span> parameters, three times less than BraTS 2020’s best-performing model (ET 0.798, WT 0.912, TC 0.857) on the validation set. The proposed ensemble model has <span><math><mrow><mn>93</mn><mo>.</mo><mn>5</mn><mspace></mspace><mi>M</mi></mrow></math></span> parameters, 1.6 times less than the top-ranked model and two times less than the third-ranked model (ET 0.793, WT 0.911, TC 0.853 on validation set) of BraTS2020 challenge.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100307"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000091/pdfft?md5=d29fc0533e483abd517c7cab8004bdcb&pid=1-s2.0-S2772442524000091-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139710235","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":"Using process mining algorithms for process improvement in healthcare","authors":"Fazla Rabbi ,&nbsp;Debapriya Banik ,&nbsp;Niamat Ullah Ibne Hossain ,&nbsp;Alexandr Sokolov","doi":"10.1016/j.health.2024.100305","DOIUrl":"https://doi.org/10.1016/j.health.2024.100305","url":null,"abstract":"<div><p>Healthcare professionals must provide their patients with the best possible service and be well-informed and expert at carrying out complex surgical procedures to fulfill this responsibility. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience. Through continuous learning and training, medical practitioners trained in up-to-date and state-of-the-art surgical techniques and technologies make productive and effective healthcare systems possible. Healthcare systems often report on problems with surgical processes, skipped procedures, unusual activities during operations, and lengthy transition times. This event log data allows implementing process mining methods to deliver medical professionals with simple and understandable findings using Petri nets for process analysis and enhancement. This study identifies the parallels and discrepancies between the pre-and post-stages and their respective frequency on each typical Central Venous Catheter (CVC) installation activity. The Process Mining for Python (PM4Py) frameworks used four major mining algorithms to view the event log (i.e., alpha miner, directly-follows graph (DFG), heuristic miner, and inductive miner). This study's findings indicate that medical residents are more susceptible to error during pre-operative procedures.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100305"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000078/pdfft?md5=d25e53aa28e307b96560fec95871fd89&pid=1-s2.0-S2772442524000078-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mathematical tumor growth model for exploring saturated response of M2 macrophages","authors":"Kaushik Dehingia ,&nbsp;Yamen Alharbi ,&nbsp;Vikas Pandey","doi":"10.1016/j.health.2024.100306","DOIUrl":"https://doi.org/10.1016/j.health.2024.100306","url":null,"abstract":"<div><p>This study addresses a tumor–macrophage interaction model to examine the role of the saturated response of M2 macrophages. We find the equilibrium point of the model and analyze local stability at each equilibrium. We show that tumor-free equilibrium is always stable, whereas, under certain conditions, the tumor-dominant and interior equilibrium are asymptotically stable. Moreover, stable and unstable limit cycles and period-doubling bifurcation have been observed at the interior equilibrium point. A remarkable result has been observed: in the presence of a saturated response of M2 macrophages, with a relatively higher activation rate of M2 macrophages due to tumor cells, the disease spreads more quickly in the body. Hence, M1 macrophages cannot stabilize the system, and aperiodic oscillations are observed. Furthermore, we show that a better immune response can reverse that system’s unstable nature. Numerical simulations verify the analytical results.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100306"},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277244252400008X/pdfft?md5=56bbf5f1b26299586ec2ca78c05789d3&pid=1-s2.0-S277244252400008X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139653032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of artificial intelligence techniques for oral cancer detection","authors":"Kavyashree C. ,&nbsp;H.S. Vimala ,&nbsp;Shreyas J.","doi":"10.1016/j.health.2024.100304","DOIUrl":"10.1016/j.health.2024.100304","url":null,"abstract":"<div><p>Oral cancer is a form of cancer that develops in the tissue of an oral cavity. Detection at an early stage is necessary to prevent the mortality rate in cancer patients. Artificial intelligence (AI) techniques play a significant role in assisting with diagnosing oral cancer. The AI techniques provide better detection accuracy and help automate oral cancer detection. The study shows that AI has a wide range of algorithms and provides outcomes in the most precise manner possible. We provide an overview of different input types and apply an appropriate algorithm to detect oral cancer. We aim to provide an overview of various AI techniques that can be used to automate oral cancer detection and to analyze these techniques to improve the efficiency and accuracy of oral cancer screening. We provide a summary of various methods available for oral cancer detection. We cover different input image formats, their processing, and the need for segmentation and feature extraction. We further include a list of other conventional strategies. We focus on various AI techniques for detecting oral cancer, including deep learning, machine learning, fuzzy computing, data mining, and genetic algorithms, and evaluates their benefits and drawbacks. The larger part of the articles focused on deep learning (37%) methods, followed by machine learning (32%), genetic algorithms (12%), data mining techniques (10%), and fuzzy computing (9%) for oral cancer detection.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100304"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000066/pdfft?md5=4271ee0a4378ec8144ed336855cbfa61&pid=1-s2.0-S2772442524000066-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139636955","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":"An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection","authors":"F M Javed Mehedi Shamrat ,&nbsp;Rashiduzzaman Shakil ,&nbsp;Sharmin ,&nbsp;Nazmul Hoque ovy ,&nbsp;Bonna Akter ,&nbsp;Md Zunayed Ahmed ,&nbsp;Kawsar Ahmed ,&nbsp;Francis M. Bui ,&nbsp;Mohammad Ali Moni","doi":"10.1016/j.health.2024.100303","DOIUrl":"https://doi.org/10.1016/j.health.2024.100303","url":null,"abstract":"<div><p>Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100303"},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000054/pdfft?md5=d8486a0b7c2a66d37a79ca700f9d36fd&pid=1-s2.0-S2772442524000054-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel fractional-order stochastic epidemic model to analyze the role of media awareness in the spread of conjunctivitis","authors":"Shiv Mangal ,&nbsp;Ebenezer Bonyah ,&nbsp;Vijay Shankar Sharma ,&nbsp;Y. Yuan","doi":"10.1016/j.health.2024.100302","DOIUrl":"https://doi.org/10.1016/j.health.2024.100302","url":null,"abstract":"<div><p>This study introduces a novel fractional-order stochastic epidemic model to analyze the spread of conjunctivitis, a prevalent ocular infection, while accounting for the influence of media awareness on disease transmission. The model incorporates fractional derivatives to capture memory effects and non-local interactions inherent in epidemic processes, allowing for a more accurate representation of disease dynamics. The stability analysis of equilibrium points is carried out based on the basic reproduction number <span><math><msub><mrow><mi>ℛ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> and fractional-order <span><math><mi>α</mi></math></span>. Further, the Hopf bifurcation phenomenon is discussed in this paper. Stochasticity accounts for the randomness in transmission events. The findings of this study provide insights into the complex interrelationship between disease dynamics and media influence, shedding light on the role of public awareness in mitigating or exacerbating conjunctivitis outbreaks. The implications of this work extend to public health policy formulation, highlighting the importance of targeted communication strategies in controlling and preventing the spread of conjunctivitis and similar infectious diseases.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100302"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000042/pdfft?md5=38829598f690a40a705f819fef29eef9&pid=1-s2.0-S2772442524000042-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139487305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel machine learning approach for diagnosing diabetes with a self-explainable interface","authors":"Gangani Dharmarathne ,&nbsp;Thilini N. Jayasinghe ,&nbsp;Madhusha Bogahawaththa ,&nbsp;D.P.P. Meddage ,&nbsp;Upaka Rathnayake","doi":"10.1016/j.health.2024.100301","DOIUrl":"https://doi.org/10.1016/j.health.2024.100301","url":null,"abstract":"<div><p>This study introduces the first-ever self-explanatory interface for diagnosing diabetes patients using machine learning. We propose four classification models (Decision Tree (DT), K-nearest Neighbor (KNN), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGB)) based on the publicly available diabetes dataset. To elucidate the inner workings of these models, we employed the machine learning interpretation method known as Shapley Additive Explanations (SHAP). All the models exhibited commendable accuracy in diagnosing patients with diabetes, with the XGB model showing a slight edge over the others. Utilising SHAP, we delved into the XGB model, providing in-depth insights into the reasoning behind its predictions at a granular level. Subsequently, we integrated the XGB model and SHAP’s local explanations into an interface to predict diabetes in patients. This interface serves a critical role as it diagnoses patients and offers transparent explanations for the decisions made, providing users with a heightened awareness of their current health conditions. Given the high-stakes nature of the medical field, this developed interface can be further enhanced by including more extensive clinical data, ultimately aiding medical professionals in their decision-making processes.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100301"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000030/pdfft?md5=494bc571d60d347c01d68d0c317c4288&pid=1-s2.0-S2772442524000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139487303","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}