José A. Delgado-Osuna, C. García-Martínez, S. Ventura, J. G. Barbadillo
Colorectal cancer affects to a significant portion of the population and is one of the leading causes of cancer-related deaths in many countries. Professionals of the Reina Sofia University Hospital have fed a database about this pathology for more than 10 years. In this work, we apply classification and association rule learning tools, including a new methodology, to obtain tractable and interpretable descriptions of those cases where complications appeared, which is one of the attributes.
{"title":"Obtaining Tractable and Interpretable Descriptions for Cases with Complications from a Colorectal Cancer Database","authors":"José A. Delgado-Osuna, C. García-Martínez, S. Ventura, J. G. Barbadillo","doi":"10.1109/CBMS.2019.00095","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00095","url":null,"abstract":"Colorectal cancer affects to a significant portion of the population and is one of the leading causes of cancer-related deaths in many countries. Professionals of the Reina Sofia University Hospital have fed a database about this pathology for more than 10 years. In this work, we apply classification and association rule learning tools, including a new methodology, to obtain tractable and interpretable descriptions of those cases where complications appeared, which is one of the attributes.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117213092","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}
A. A. Alyousef, S. Nihtyanova, C. Denton, Pietro Bosoni, R. Bellazzi, A. Tucker
Disease subtyping can assist the development of precision medicine but remains a challenge in data analysis by reason of the many different methods to group individuals depending on their data. However, identification of subclasses of disease will help to produce better models which are more specific to patients and will improve prediction and interpretation of underlying characteristics of disease. This paper presents a novel algorithm that integrates latent class models with supervised learning. The new algorithm uses latent class models to cluster patients within groups that results in improved classification as well as aiding the understanding of the dissimilarities of the discovered groups. The methods are tested on data from patients with Systemic Sclerosis (SSc), a rare potentially fatal condition. Results show that the "Latent Class Multi-Label Classification Model" improves accuracy when compared with competitive similar methods.
{"title":"Latent Class Multi-Label Classification to Identify Subclasses of Disease for Improved Prediction","authors":"A. A. Alyousef, S. Nihtyanova, C. Denton, Pietro Bosoni, R. Bellazzi, A. Tucker","doi":"10.1109/CBMS.2019.00109","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00109","url":null,"abstract":"Disease subtyping can assist the development of precision medicine but remains a challenge in data analysis by reason of the many different methods to group individuals depending on their data. However, identification of subclasses of disease will help to produce better models which are more specific to patients and will improve prediction and interpretation of underlying characteristics of disease. This paper presents a novel algorithm that integrates latent class models with supervised learning. The new algorithm uses latent class models to cluster patients within groups that results in improved classification as well as aiding the understanding of the dissimilarities of the discovered groups. The methods are tested on data from patients with Systemic Sclerosis (SSc), a rare potentially fatal condition. Results show that the \"Latent Class Multi-Label Classification Model\" improves accuracy when compared with competitive similar methods.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127051574","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}
Jayr Alencar Pereira, Carolline Pena, Mariana de Melo, Bruno Cartaxo, R. Fidalgo, S. Soares
Previous research identifies facilitators and barriers related to the use of Alternative and Augmentative Communication Systems, however, more evidence is needed to understand aspects related to introduction of such systems in an outpatient setting. This paper aims to analyze theses aspects by identifying the facilitators and barriers that comprise systems' use by aphasic people at a University Clinic in Brazil. Semi-structured interviews were conducted and the collected data were analyzed based on qualitative techniques like open coding and constant comparison. In addition to the factors found in previous research, this study identified new factors such as: cost, infantilized systems and sentences' quality produced, that can be considered as facilitators or barriers in using AAC systems. The results of this research can be used to improve the current and new AAC systems.
{"title":"Facilitators and Barriers to Using Alternative and Augmentative Communication Systems by Aphasic: Therapists Perceptions","authors":"Jayr Alencar Pereira, Carolline Pena, Mariana de Melo, Bruno Cartaxo, R. Fidalgo, S. Soares","doi":"10.1109/CBMS.2019.00077","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00077","url":null,"abstract":"Previous research identifies facilitators and barriers related to the use of Alternative and Augmentative Communication Systems, however, more evidence is needed to understand aspects related to introduction of such systems in an outpatient setting. This paper aims to analyze theses aspects by identifying the facilitators and barriers that comprise systems' use by aphasic people at a University Clinic in Brazil. Semi-structured interviews were conducted and the collected data were analyzed based on qualitative techniques like open coding and constant comparison. In addition to the factors found in previous research, this study identified new factors such as: cost, infantilized systems and sentences' quality produced, that can be considered as facilitators or barriers in using AAC systems. The results of this research can be used to improve the current and new AAC systems.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125414271","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}
A. Nentidis, Anastasia Krithara, Grigorios Tsoumakas, G. Paliouras
Biomedical literature in MEDLINE/PubMed is semantically indexed with MeSH thesaurus entries (subject annotations) which may correspond to more than one related but distinct domain concepts. In such cases, the subject annotations do not follow the level of detail available in the domain and do not always suffice to meet the information needs of domain experts. In this work, we propose a method to automatically refine subject annotations at the level of concepts and employ it in the case of the MeSH descriptor for Alzheimer's Disease, which corresponds to six different concepts representing disease sub-types. The results indicate that the use of concept-occurrence as weak supervision can improve upon the predictive performance of literal string matching alone. The refined annotations can support more precise concept-based search, enable the integration of subject annotations with other semantic information and facilitate the maintenance of subject annotation consistency, as the MeSH thesaurus evolves with the addition of more detailed entries.
{"title":"Beyond MeSH: Fine-Grained Semantic Indexing of Biomedical Literature Based on Weak Supervision","authors":"A. Nentidis, Anastasia Krithara, Grigorios Tsoumakas, G. Paliouras","doi":"10.1109/CBMS.2019.00045","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00045","url":null,"abstract":"Biomedical literature in MEDLINE/PubMed is semantically indexed with MeSH thesaurus entries (subject annotations) which may correspond to more than one related but distinct domain concepts. In such cases, the subject annotations do not follow the level of detail available in the domain and do not always suffice to meet the information needs of domain experts. In this work, we propose a method to automatically refine subject annotations at the level of concepts and employ it in the case of the MeSH descriptor for Alzheimer's Disease, which corresponds to six different concepts representing disease sub-types. The results indicate that the use of concept-occurrence as weak supervision can improve upon the predictive performance of literal string matching alone. The refined annotations can support more precise concept-based search, enable the integration of subject annotations with other semantic information and facilitate the maintenance of subject annotation consistency, as the MeSH thesaurus evolves with the addition of more detailed entries.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":" 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113951861","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}
Existing systems to support the daily decisiontaking process carried out by health professionals need to be used independently to perform different text mining subtasks. In practice, there are few systems that unify all the subtasks into an unique framework, easing therefore the clinical work by automating complex clinical tasks such as the detection of clinical alerts as well as clinical information coding. In this sense, the MiNerDoc system is proposed, whose main objective is to support clinical decision-taking process by analysing tons of textual clinical reports in an unified framework. MiNerDoc performs two basic functions that are of great importance in the medical field: detection of risk factors based on the recognition of five medical entities (Disease, Pharmacologic, Region/Part Body, Procedure/Test, Finding/Sign), and automatic prediction of standardized diagnostic codes (MeSH descriptors). A major feature of MiNerDoc is it includes external knowledge sources such as MetaMap and UMLS to terminologically and semantically enrich the interpretation of clinical texts. Some study cases are considered in this work to demonstrate the power of MiNerDoc.
{"title":"MiNerDoc: a Semantically Enriched Text Mining System to Transform Clinical Text into Knowledge","authors":"Carmen Luque, J. M. Luna, S. Ventura","doi":"10.1109/CBMS.2019.00142","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00142","url":null,"abstract":"Existing systems to support the daily decisiontaking process carried out by health professionals need to be used independently to perform different text mining subtasks. In practice, there are few systems that unify all the subtasks into an unique framework, easing therefore the clinical work by automating complex clinical tasks such as the detection of clinical alerts as well as clinical information coding. In this sense, the MiNerDoc system is proposed, whose main objective is to support clinical decision-taking process by analysing tons of textual clinical reports in an unified framework. MiNerDoc performs two basic functions that are of great importance in the medical field: detection of risk factors based on the recognition of five medical entities (Disease, Pharmacologic, Region/Part Body, Procedure/Test, Finding/Sign), and automatic prediction of standardized diagnostic codes (MeSH descriptors). A major feature of MiNerDoc is it includes external knowledge sources such as MetaMap and UMLS to terminologically and semantically enrich the interpretation of clinical texts. Some study cases are considered in this work to demonstrate the power of MiNerDoc.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129044468","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}
Radwa El Shawi, Youssef Mohamed, M. Al-Mallah, S. Sakr
Although complex machine learning models (e.g., Random Forest, Neural Networks) are commonly outperforming the traditional simple interpretable models (e.g., Linear Regression, Decision Tree), in the healthcare domain, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. With the new General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. To tackle this challenge, recently, several machine learning interpretability techniques have been developed and introduced. In general, the main aim of these interpretability techniques is to shed light and provide insights into the predictions process of the machine learning models and explain how the model predictions have resulted. However, in practice, assessing the quality of the explanations provided by the various interpretability techniques is still questionable. In this paper, we present a comprehensive experimental evaluation of three recent and popular local model agnostic interpretability techniques, namely, LIME, SHAP and Anchors on different types of real-world healthcare data. Our experimental evaluation covers different aspects for its comparison including identity, stability, separability, similarity, execution time and bias detection. The results of our experiments show that LIME achieves the lowest performance for the identity metric and the highest performance for the separability metric across all datasets included in this study. On average, SHAP has the smallest average time to output explanation across all datasets included in this study. For detecting the bias, SHAP enables the participants to better detect the bias.
{"title":"Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques","authors":"Radwa El Shawi, Youssef Mohamed, M. Al-Mallah, S. Sakr","doi":"10.1109/CBMS.2019.00065","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00065","url":null,"abstract":"Although complex machine learning models (e.g., Random Forest, Neural Networks) are commonly outperforming the traditional simple interpretable models (e.g., Linear Regression, Decision Tree), in the healthcare domain, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. With the new General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. To tackle this challenge, recently, several machine learning interpretability techniques have been developed and introduced. In general, the main aim of these interpretability techniques is to shed light and provide insights into the predictions process of the machine learning models and explain how the model predictions have resulted. However, in practice, assessing the quality of the explanations provided by the various interpretability techniques is still questionable. In this paper, we present a comprehensive experimental evaluation of three recent and popular local model agnostic interpretability techniques, namely, LIME, SHAP and Anchors on different types of real-world healthcare data. Our experimental evaluation covers different aspects for its comparison including identity, stability, separability, similarity, execution time and bias detection. The results of our experiments show that LIME achieves the lowest performance for the identity metric and the highest performance for the separability metric across all datasets included in this study. On average, SHAP has the smallest average time to output explanation across all datasets included in this study. For detecting the bias, SHAP enables the participants to better detect the bias.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"32 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116575688","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}
Leila Yousefi, S. Swift, Mahir Arzoky, L. Sacchi, L. Chiovato, A. Tucker
There is a great deal of debate over the importance of explanation in AI models inferred from health data. In particular, there is a balance that needs to be made between the accuracy of complex 'deep' models such as convolutional neural networks and the transparency of models that aim to model data in a more 'human' way such as expert systems. In this paper, we explore the use of temporal association rules to validate and uncover the meaning behind discrete hidden variables that have been inferred from clinical diabetes data. We use a recently published technique based upon the IC* (Induction Causation) algorithm that limits the number of hidden variables and places them within a network structure. Here, we take the hidden variables and compare their underlying discrete states to clusters that have been generated from temporal association rules. This allows us to characterise the hidden states based upon different sequences of complications. Results are very promising, with many hidden states aligning with the discovered clusters giving us a direct interpretation.
{"title":"Opening the Black Box: Exploring Temporal Pattern of Type 2 Diabetes Complications in Patient Clustering Using Association Rules and Hidden Variable Discovery","authors":"Leila Yousefi, S. Swift, Mahir Arzoky, L. Sacchi, L. Chiovato, A. Tucker","doi":"10.1109/CBMS.2019.00048","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00048","url":null,"abstract":"There is a great deal of debate over the importance of explanation in AI models inferred from health data. In particular, there is a balance that needs to be made between the accuracy of complex 'deep' models such as convolutional neural networks and the transparency of models that aim to model data in a more 'human' way such as expert systems. In this paper, we explore the use of temporal association rules to validate and uncover the meaning behind discrete hidden variables that have been inferred from clinical diabetes data. We use a recently published technique based upon the IC* (Induction Causation) algorithm that limits the number of hidden variables and places them within a network structure. Here, we take the hidden variables and compare their underlying discrete states to clusters that have been generated from temporal association rules. This allows us to characterise the hidden states based upon different sequences of complications. Results are very promising, with many hidden states aligning with the discovered clusters giving us a direct interpretation.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122260885","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}
Clinical decision support systems (CDSS) are currently essential tools to guide medical diagnostics and patients' treatments, and they are specially important for the better care management of chronic diseases, such as cancer and diabetes. These systems help to decide on the best treatment solution, namely in centres where there is a shortage of medical experts. CDSS tools are often integrated into the Electronic Health Record (EHR) to facilitate the reuse of patient data. However, many times, creating new and intuitive protocols that are disease-specific is still a challenge. In this paper we present an open source solution (GenericCDSS) that can be used to streamline the development of autonomous CDSS, avoiding the dependency on third-party tools to manage patient data and clinical protocols. The software tool provides a modern user interface, supporting multi-platforms such as mobile and desktop devices. GenericCDSS is publicly available at https://github.com/bioinformatics-ua/GenericCDSS, under a GNU GPL license.
{"title":"GenericCDSS - A Generic Clinical Decision Support System","authors":"João Rafael Almeida, J. Oliveira","doi":"10.1109/CBMS.2019.00046","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00046","url":null,"abstract":"Clinical decision support systems (CDSS) are currently essential tools to guide medical diagnostics and patients' treatments, and they are specially important for the better care management of chronic diseases, such as cancer and diabetes. These systems help to decide on the best treatment solution, namely in centres where there is a shortage of medical experts. CDSS tools are often integrated into the Electronic Health Record (EHR) to facilitate the reuse of patient data. However, many times, creating new and intuitive protocols that are disease-specific is still a challenge. In this paper we present an open source solution (GenericCDSS) that can be used to streamline the development of autonomous CDSS, avoiding the dependency on third-party tools to manage patient data and clinical protocols. The software tool provides a modern user interface, supporting multi-platforms such as mobile and desktop devices. GenericCDSS is publicly available at https://github.com/bioinformatics-ua/GenericCDSS, under a GNU GPL license.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128004654","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":"[Title page iii]","authors":"","doi":"10.1109/cbms.2019.00002","DOIUrl":"https://doi.org/10.1109/cbms.2019.00002","url":null,"abstract":"","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133401096","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}
Sara Montagna, Angelo Croatti, A. Ricci, V. Agnoletti, Vittorio Albarello
The problem of tracking has gained a central role in healthcare research since it enables the acquisition of the information needed for improving healthcare management and efficiency, alongside patient safety. In literature, it is mainly discussed as an allocation problem that must deal with limited resources (rooms, physicians, equipment) to optimise workflows, and Real-Time Location Systems have been introduced with the main goal of locating and identifying assets and personnel in a healthcare facility. In this paper, we propose a novel perspective of pervasive tracking into Hospital 4.0, devised explicitly for time-dependent acute patient flow. The goal is to develop a tracking system that acquires not only the time and location of entities, exploiting state-of-the-art techniques, but also the main clinical events occurred. As an example application we describe TraumaTracker, a system developed to support the accurate and complete documentation of trauma resuscitation processes from pre-hospital care.
{"title":"Pervasive Tracking for Time-Dependent Acute Patient Flow: A Case Study in Trauma Management","authors":"Sara Montagna, Angelo Croatti, A. Ricci, V. Agnoletti, Vittorio Albarello","doi":"10.1109/CBMS.2019.00057","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00057","url":null,"abstract":"The problem of tracking has gained a central role in healthcare research since it enables the acquisition of the information needed for improving healthcare management and efficiency, alongside patient safety. In literature, it is mainly discussed as an allocation problem that must deal with limited resources (rooms, physicians, equipment) to optimise workflows, and Real-Time Location Systems have been introduced with the main goal of locating and identifying assets and personnel in a healthcare facility. In this paper, we propose a novel perspective of pervasive tracking into Hospital 4.0, devised explicitly for time-dependent acute patient flow. The goal is to develop a tracking system that acquires not only the time and location of entities, exploiting state-of-the-art techniques, but also the main clinical events occurred. As an example application we describe TraumaTracker, a system developed to support the accurate and complete documentation of trauma resuscitation processes from pre-hospital care.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"4564 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116566605","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}
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