Intelligence-based medicine最新文献

{"title":"Investigating deep-learning NLP for automating the extraction of oncology efficacy endpoints from scientific literature","authors":"Aline Gendrin-Brokmann ,&nbsp;Eden Harrison ,&nbsp;Julianne Noveras ,&nbsp;Leonidas Souliotis ,&nbsp;Harris Vince ,&nbsp;Ines Smit ,&nbsp;Francisco Costa ,&nbsp;David Milward ,&nbsp;Sashka Dimitrievska ,&nbsp;Paul Metcalfe ,&nbsp;Emilie Louvet","doi":"10.1016/j.ibmed.2024.100152","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100152","url":null,"abstract":"<div><h3>Objective</h3><p>Benchmarking drug efficacy is a critical step in clinical trial design and planning. The challenge is that much of the data on efficacy endpoints is stored in scientific papers in free text form, so extraction of such data is currently a largely manual task. Our objective is to automate this task as much as possible.</p></div><div><h3>Methods</h3><p>In this study we have developed and optimised a framework to extract efficacy endpoints from text in scientific papers, using a machine learning approach.</p></div><div><h3>Results</h3><p>Our machine learning model predicts 25 classes associated with efficacy endpoints and leads to high F1 scores (harmonic mean of precision and recall) of 96.4 % on the test set, and 93.9 % and 93.7 % on two case studies.</p></div><div><h3>Conclusion</h3><p>These methods were evaluated against – and showed strong agreement with – subject matter experts and show significant promise in the future of automating the extraction of clinical endpoints from free text.</p></div><div><h3>Significance</h3><p>Clinical information extraction from text data is currently a laborious manual task which scales poorly and is prone to human error. Demonstrating the ability to extract efficacy endpoints automatically shows great promise for accelerating clinical trial design moving forwards.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266652122400019X/pdfft?md5=a92e134878dd46a959c3a33708e38779&pid=1-s2.0-S266652122400019X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582934","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":"Context based ranking strategies for renowned instructional methodologies","authors":"Saranya V ,&nbsp;Azween Abdullah ,&nbsp;Parthasarathy Ramadass ,&nbsp;Saravanan Srinivasan ,&nbsp;Basu Dev Shivahare ,&nbsp;Sandeep Kumar Mathivanan ,&nbsp;Karthik P","doi":"10.1016/j.ibmed.2024.100186","DOIUrl":"10.1016/j.ibmed.2024.100186","url":null,"abstract":"<div><div>The main objective of this work is to validate the decisions made towards adoption of appropriate instructional methodologies based on the context of a specific region considering the quality of education, the cost of education and the learning outcomes as predominant parameters. The non-deterministic events and uncertain situations that may arise over a long-range period impose a vague and fuzzy environment in the educational system. Investigations have been made to identify suitable educational framework for implementation in the institutions of a specific region in view of these unpredictable events and non-deterministic conditions. Fuzzy decision analysis and rough set theory have been applied to rank the prominent instructional methodologies which are encompassed within each educational framework. Hurwicz Rule is adopted to balance the pessimistic and optimistic opinions about the non-deterministic events while validating the merits of the instructional methodologies. Grey relational analysis is carried out while ranking instructional methodologies in a vague environment. In this work, the instructional methodologies are ranked using fuzzy entropy as well as crisp entropy measures and the outcomes of the fuzzy and rough sets-based decision analysis have been validated.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662591","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 effective U-net model for diagnosing Covid-19 infection","authors":"Shirin Kordnoori ,&nbsp;Maliheh Sabeti ,&nbsp;Hamidreza Mostafaei ,&nbsp;Saeed Seyed Agha Banihashemi","doi":"10.1016/j.ibmed.2024.100156","DOIUrl":"10.1016/j.ibmed.2024.100156","url":null,"abstract":"<div><p>Coronavirus disease 2019 (COVID-19) has become a pandemic all over the world and has spread rapidly. To distinguish between infected and non-infected areas, there is a critical need for segmentation methods that can identify infected areas from Chest Computed Tomography (CT) scans. In recent years, deep learning has become the most widely used approach for medical image segmentation, including the identification of infected areas in Chest CT scans. We propose an encoder-decoder based on the U-NET architecture for segmenting the MedSeg dataset, which contains lung CT scans. To study the effect of input dimensions on the model's output results, we gave CT images with dimensions of 224 × 224, 256 × 256, and 512 × 512 as inputs to the model. The results showed that 224 × 224 achieved higher results compared to 256 × 256 and 512 × 512, with a <span><math><mrow><mi>d</mi><mi>i</mi><mi>c</mi><msub><mi>e</mi><mrow><mi>c</mi><mi>o</mi><mi>e</mi><mi>f</mi></mrow></msub></mrow></math></span> of 81.36, accuracy of 87.65, sensitivity of 84.71, and specificity of 88.35. Additionally, the 224 × 224 input based on the proposed model achieved the highest number of correct answers compared to previous U-net methods. The proposed model can be applied as an effective screening tool to help primary service staff better refer suspected patients to specialists.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000231/pdfft?md5=b94ffe85fb10dc2d0a608b570ad4353c&pid=1-s2.0-S2666521224000231-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629823","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":"Improving the quality of pulmonary nodules segmentation using the new proposed U-Net neural network","authors":"A. Sadremomtaz,&nbsp;M. Zadnorouzi","doi":"10.1016/j.ibmed.2024.100166","DOIUrl":"10.1016/j.ibmed.2024.100166","url":null,"abstract":"<div><p>Diagnosing lung cancer is difficult due to the complexity of the nature of nodules. CT scan imaging is the most common imaging to diagnosis of lung cancer. Detection of nodules from these images is a challenge for radiologists and doctors. In recent years, neural networks have been developed for automatic, faster and more accurate diagnosis of diseases from medical images. In the present study, a new improved U-Net neural network is introduced for the automatic detection and segmentation of pulmonary nodules. The evaluation of this model has been done on LIDC-IDRI database. Our results have high values of recall, specificity and accuracy. The highest Recall value is 97.97 and is related to Juxtra-vascular. Specificity and accuracy for non-solid, partially solid and tiny has a value of 96.99.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000334/pdfft?md5=e97dbb0434a7786853114c1216a89ad7&pid=1-s2.0-S2666521224000334-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049535","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 Convolutional Neural Network- Based Deep Learning To Detect Reticulocytes From Human Peripheral Blood","authors":"Keerthy Reghunandanan ,&nbsp;V.S. Lakshmi ,&nbsp;Rose Raj ,&nbsp;Kasi Viswanath ,&nbsp;Christeen Davis ,&nbsp;Rajesh Chandramohanadas","doi":"10.1016/j.ibmed.2024.100175","DOIUrl":"10.1016/j.ibmed.2024.100175","url":null,"abstract":"<div><div>Machine learning approaches are rapidly augmenting, and in some cases, replacing the conventional methods in biomedical data analysis; to reduce time, cost, biases, and the need for sophisticated analytical platforms. Hence, significant interest has been compounded in the integration of automated image analysis for various clinical applications, such as the detection of infected or inflamed wounds, bone fractures or for the purpose of disease diagnosis – such as <em>Plasmodium</em> parasites or circulating tumour cells in blood. Here, we report the development of a Convolutional Neural Network (CNN)-based method on CPU to distinguish and count immature human red blood cells known as reticulocytes from blood smears. Reticulocytes represent a heterogeneous and relatively small percentage of cells in peripheral blood, and contain residual RNA in complex with proteins which generates thread-like patterns when stained with New Methylene Blue (NMB) dye. We used more than 200 NMB-stained images from leukocyte-depleted blood to train and optimize the model for immature reticulocytes (stained positive with NMB, intensity and pattern of which depends on the developmental stage of the reticulocyte) and mature RBCs (no staining with NMB). The training performance evaluation metrics demonstrated a mean average precision (mAP50) of 0.88, a precision of 0.83, a recall of 0.88, and an F1 score of 0.87. Our model was able to successfully count reticulocytes with accuracy more than 90% from unknown samples which were subsequently cross-verified through microscopy and counting. Given the importance of reticulocyte maturation and its clinical relevance, the newly developed model will find important, easy to adopt biomedical applications that can be achieved on a simple PC.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578622","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":"Prognostics for respiratory epidemic dynamics by multivariate gaidai risk assessment methodology","authors":"Oleg Gaidai ,&nbsp;Hongchen Li ,&nbsp;Yu Cao ,&nbsp;Alia Ashraf ,&nbsp;Yan Zhu","doi":"10.1016/j.ibmed.2024.100173","DOIUrl":"10.1016/j.ibmed.2024.100173","url":null,"abstract":"<div><h3>Introduction</h3><div>current study introduces an accurate prediction spatiotemporal model for epidemic outbreaks risk assessment.</div></div><div><h3>Methods</h3><div>utilize state-of-the-art statistical methodology on raw/unfiltered clinical datasets. In order to provide trustworthy long-term forecasts of viral outbreak risks, this research suggests a novel biosystem bio-reliability approach that works particularly well for multi-regional biological, environmental, and public health systems that are monitored over a representative time-lapse.</div></div><div><h3>Results</h3><div>study made use of daily clinically reported patient counts from COVID-19 (SARS-CoV-2) throughout all impacted Dutch administrative areas. The objective of this research was to establish new benchmark for novel bio-reliability methodology that enables efficient risk analysis, based on recorded raw clinical patient numbers, with accounting for pertinent area mapping.</div></div><div><h3>Remarks and significance</h3><div>by effectively employing various clinical survey datasets that are now accessible, the proposed technique may be used for contemporary biomedical applications, as well as the general welfare.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442514","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":"Exploring the business aspects of digital pathology, deep learning in cancers","authors":"Arjun Reddy ,&nbsp;Darnell K. Adrian Williams ,&nbsp;Gillian Graifman ,&nbsp;Nowair Hussain ,&nbsp;Maytal Amiel ,&nbsp;Tran Priscilla ,&nbsp;Ali Haider ,&nbsp;Bali Kumar Kavitesh ,&nbsp;Austin Li ,&nbsp;Leael Alishahian ,&nbsp;Nichelle Perera ,&nbsp;Corey Efros ,&nbsp;Myoungmee Babu ,&nbsp;Mathew Tharakan ,&nbsp;Mill Etienne ,&nbsp;Benson A. Babu","doi":"10.1016/j.ibmed.2024.100172","DOIUrl":"10.1016/j.ibmed.2024.100172","url":null,"abstract":"<div><h3>Introduction</h3><div>Cancer remains one of the leading causes of morbidity and mortality worldwide. Deep learning in digital pathology has the potential to improve operational efficiency, costs, and care.</div></div><div><h3>Methods</h3><div>We searched Web of Science, Arxiv, MedRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, and Cochrane databases for whole slide imaging and deep learning articles published between 2019 and 2023. The final six articles were selected from 776 articles identified through an inclusion criterion.</div></div><div><h3>Conclusion</h3><div>Digital pathology services that utilize deep learning have the potential to enhance clinical workflow efficiencies and can have a positive impact on business activities. We anticipate cost reductions as deep learning technology advances and more companies enter the digital pathology ecosystem. However, the limited availability of business use cases, primarily due to publication bias, poses a challenge in medicine without clear examples to learn from.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100172"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586836","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":"Developing a decision model to early predict ICU admission for COVID-19 patients: A machine learning approach","authors":"Abdulaziz Ahmed ,&nbsp;Ferhat D. Zengul ,&nbsp;Sheena Khan ,&nbsp;Kristine R. Hearld ,&nbsp;Sue S. Feldman ,&nbsp;Allyson G. Hall ,&nbsp;Gregory N. Orewa ,&nbsp;James Willig ,&nbsp;Kierstin Kennedy","doi":"10.1016/j.ibmed.2024.100136","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100136","url":null,"abstract":"<div><p>Emergency department (ED) overcrowding is a significant problem in the US. This paper develops a decision model to mitigate ED overcrowding by helping hospitals proactively plan patient boarding processes. The information obtained after the initial assessment of COVID-19 patients in the ED, including patient demographics and medical history, is utilized to predict ICU admission earlier. The predicted information can be communicated with the inpatient unit to prepare an ICU bed for the patients who need ICU care. As a result, the boarding time when patients wait for an ICU bed to be ready can be reduced. The data used in this study included 100 features and 19,155 COVID-19 patients from an academic medical center located in the Southeast United States. Multiple feature selection methods along with Extreme Gradient Boosting (XGBoost) were utilized to develop the models. The parameters of the XGBoost models are optimized using simulated annealing (SA). Among the proposed models, the best model included ten features and resulted in an area under the curve (AUC) of 89.2%, which is the highest among the models proposed in the literature. The proposed prediction model allows hospital administrators to allocate ICU beds more efficiently, enhance patient flow, and mitigate ED overcrowding<strong>.</strong></p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"9 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000036/pdfft?md5=fb2565bfa79e72f25f4e18b5603f990e&pid=1-s2.0-S2666521224000036-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140327936","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":"Clustering polycystic ovary syndrome laboratory results extracted from a large internet forum with machine learning","authors":"Rebecca H.K. Emanuel ,&nbsp;Paul D. Docherty ,&nbsp;Helen Lunt ,&nbsp;Rua Murray ,&nbsp;Rebecca E. Campbell","doi":"10.1016/j.ibmed.2024.100135","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100135","url":null,"abstract":"<div><h3>Background</h3><p>Polycystic Ovary Syndrome (PCOS) is reported to affect between 4% and 21% of reproductive aged people with ovaries. It is a heterogeneous condition with a lack of established phenotypes that address the range of reproductive and metabolic features present in PCOS. These reproductive and metabolic features may result in patients undergoing a variety of relevant laboratory tests. Previous work has led to the gathering of laboratory test results from a PCOS specific forum, hosted on a website called reddit.</p></div><div><h3>Objectives</h3><p>In this paper, laboratory results and body mass index (BMI) posted on the PCOS reddit forum were clustered to show the usefulness of the PCOS forum for PCOS research and validate existing PCOS phenotypes or discover other appropriate phenotypes.</p></div><div><h3>Methods and results</h3><p>Over 1500 sets of PCOS-related reddit laboratory test results and BMIs were clustered using nearest neighbour imputation and K-means clustering. However, only non-imputed data was included in the final clusters. Kernel Density Estimation plots were used to display the distinct clusters. The clustered test results suggested the existence of distinct metabolic and reproductive phenotypes, as well as a group displaying mild features of both types of dysregulations and a group skewed towards normal results. It was also possible to separate the groups further into distinct hypothyroid groups within the mixed dysregulation group and to separate insulin resistant and diabetes-like groups within the metabolic group.</p></div><div><h3>Conclusions</h3><p>This research further validates the usefulness of exploring alternate data sources in the age of the internet and machine learning. The reddit clusters reinforced the existing notion that people with PCOS can be separated into a primarily metabolic pathology group, a primarily reproductive pathology group and an in between group with pathology in both domains.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"9 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000024/pdfft?md5=87b2d688b9b327bd7f8d3d181ee40e71&pid=1-s2.0-S2666521224000024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140134211","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":"Machine learning prediction of Dice similarity coefficient for validation of deformable image registration","authors":"Yun Ming Wong ,&nbsp;Ping Lin Yeap ,&nbsp;Ashley Li Kuan Ong ,&nbsp;Jeffrey Kit Loong Tuan ,&nbsp;Wen Siang Lew ,&nbsp;James Cheow Lei Lee ,&nbsp;Hong Qi Tan","doi":"10.1016/j.ibmed.2024.100163","DOIUrl":"10.1016/j.ibmed.2024.100163","url":null,"abstract":"<div><h3>Introduction</h3><p>Deformable image registration (DIR) plays a vital role in adaptive radiotherapy (ART). For the clinical implementation of DIR, evaluation of deformation accuracy is a critical step. While contour-based metrics, for example Dice similarity coefficient (DSC), are widely implemented for DIR validation, they require delineation of contours which is time-consuming and would cause hold-ups in an ART workflow. Therefore, this work aims to accomplish the prediction of DSC using various metrics based on deformation vector field (DVF) by applying machine learning (ML), in order to provide an efficient means of DIR validation with minimised human intervention.</p></div><div><h3>Methods</h3><p>Planning CT image was deformed to the cone-beam CT images for 20 prostate cancer patients. Various DVF-based metrics and DSC were calculated, and the former was used as input features to predict the latter using three ML models, namely linear regression (LR), Nu Support Vector Regression (NuSVR) and Random Forest Regressor (RFR). Four datasets were used for analysis: 1) prostate, 2) bladder, 3) rectum and 4) all the organs combined. Average mean absolute error (MAE) was computed to evaluate the model performance. The classification performance of the best-performing model was further evaluated, and the prediction interval and feature importance were calculated.</p></div><div><h3>Results</h3><p>Overall, RFR achieved the lowest average MAE, ranging between 0.045 and 0.069 for the four datasets, while LR and NuSVR had slightly poorer performances. Analysis on the results of best-performing model showed that sensitivity and specificity of 0.86 and 0.51, respectively, were obtained when a prediction threshold of 0.85 was used to classify the fourth dataset. Jacobian determinant was found to be a significant contributor to the predictions of all four datasets using this model.</p></div><div><h3>Conclusion</h3><p>This study demonstrated the potential of several ML models, especially RFR, to be applied for prediction of DSC to speed up the DIR validation process.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000309/pdfft?md5=92ebfdf38ebfa5ad2817955b2f352129&pid=1-s2.0-S2666521224000309-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985703","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}