Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011354
L. Lhotská, T. Vlček
Describes several types of efficiency enhancements of "classical" rule-based diagnostic expert systems. The blackboard control structure enables one to explore more knowledge bases of the same syntax in parallel, the taxonomy structures make fast zooming of attention possible and provide an additional inference mechanism based on inheritance principles. In addition to these mechanisms, we describe a method utilizing a machine learning approach in the process of developing and refining a knowledge base. The applicability of the enhancing techniques and the machine learning is documented in four case studies exploring the extended FEL-EXPERT shell in different tasks of medical decision-making. The authors consider these techniques as useful steps on the way from "classical" diagnostic expert systems towards more complex multi-agent decision tools.
{"title":"Efficiency enhancement of rule-based expert systems","authors":"L. Lhotská, T. Vlček","doi":"10.1109/CBMS.2002.1011354","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011354","url":null,"abstract":"Describes several types of efficiency enhancements of \"classical\" rule-based diagnostic expert systems. The blackboard control structure enables one to explore more knowledge bases of the same syntax in parallel, the taxonomy structures make fast zooming of attention possible and provide an additional inference mechanism based on inheritance principles. In addition to these mechanisms, we describe a method utilizing a machine learning approach in the process of developing and refining a knowledge base. The applicability of the enhancing techniques and the machine learning is documented in four case studies exploring the extended FEL-EXPERT shell in different tasks of medical decision-making. The authors consider these techniques as useful steps on the way from \"classical\" diagnostic expert systems towards more complex multi-agent decision tools.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123282716","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011357
M. Sprogar, P. Kokol, S. Alayón
A novel autonomous evolutionary algorithm for the construction of decision trees is presented, together with an analysis of different medical data sets. The algorithm's capability to self-adapt to a given problem is used as a measure to predict if some data set is just difficult or whether it is impossible to analyze. If a specific data set doesn't include enough or proper information for the creation of a good general decision model then over-fitting will occur. To detect over-fitting, we can use several existing techniques; the most common uses special test data that is excluded from the learning phase. On average, the autonomous algorithm produces very general solutions, or gives no solution if the data set is prone to over-fitting.
{"title":"Autonomous evolutionary algorithm in medical data analysis","authors":"M. Sprogar, P. Kokol, S. Alayón","doi":"10.1109/CBMS.2002.1011357","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011357","url":null,"abstract":"A novel autonomous evolutionary algorithm for the construction of decision trees is presented, together with an analysis of different medical data sets. The algorithm's capability to self-adapt to a given problem is used as a measure to predict if some data set is just difficult or whether it is impossible to analyze. If a specific data set doesn't include enough or proper information for the creation of a good general decision model then over-fitting will occur. To detect over-fitting, we can use several existing techniques; the most common uses special test data that is excluded from the learning phase. On average, the autonomous algorithm produces very general solutions, or gives no solution if the data set is prone to over-fitting.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120967044","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011401
S. Zaidi, S. Abidi, S. Manickam
This paper presents a case for an intelligent agent-based framework for knowledge discovery in a distributed healthcare environment comprising multiple heterogeneous healthcare data repositories. Data-mediated knowledge discovery, especially from multiple heterogeneous data resources, is a tedious process and imposes significant operational constraints on end-users. We demonstrate that autonomous, reactive and proactive intelligent agents provide an opportunity to generate end-user-oriented, packaged, value-added decision-support/strategic planning services for healthcare professionals and managers. We propose the use of intelligent agents to implement a distributed agent-based data mining information structure that provides a suite of healthcare-oriented decision-support/strategic planning services.
{"title":"Distributed data mining from heterogeneous healthcare data repositories: towards an intelligent agent-based framework","authors":"S. Zaidi, S. Abidi, S. Manickam","doi":"10.1109/CBMS.2002.1011401","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011401","url":null,"abstract":"This paper presents a case for an intelligent agent-based framework for knowledge discovery in a distributed healthcare environment comprising multiple heterogeneous healthcare data repositories. Data-mediated knowledge discovery, especially from multiple heterogeneous data resources, is a tedious process and imposes significant operational constraints on end-users. We demonstrate that autonomous, reactive and proactive intelligent agents provide an opportunity to generate end-user-oriented, packaged, value-added decision-support/strategic planning services for healthcare professionals and managers. We propose the use of intelligent agents to implement a distributed agent-based data mining information structure that provides a suite of healthcare-oriented decision-support/strategic planning services.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115501497","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011397
J. M. Bueno, F. J. T. Chino, A. Traina, C. Traina, P. M. A. Marques
This paper presents a new picture archiving and communication system (PACS), called cbPACS (content-based PACS), which has content-based image retrieval resources. cbPACS answers similarity (range and nearest-neighbor) queries, taking advantage of a metric access method embedded into the image database manager. The images are compared via their features, which are extracted by an image processing system module. The system works on features based on the color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant with regard to scale, translation and rotation of images and also to brightness transformations. cbPACS is prepared to integrate new image features, based on the texture and shape of the main objects in the image.
{"title":"How to add content-based image retrieval capability in a PACS","authors":"J. M. Bueno, F. J. T. Chino, A. Traina, C. Traina, P. M. A. Marques","doi":"10.1109/CBMS.2002.1011397","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011397","url":null,"abstract":"This paper presents a new picture archiving and communication system (PACS), called cbPACS (content-based PACS), which has content-based image retrieval resources. cbPACS answers similarity (range and nearest-neighbor) queries, taking advantage of a metric access method embedded into the image database manager. The images are compared via their features, which are extracted by an image processing system module. The system works on features based on the color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant with regard to scale, translation and rotation of images and also to brightness transformations. cbPACS is prepared to integrate new image features, based on the texture and shape of the main objects in the image.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115680432","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011390
S. Aerts, P. Antal, D. Timmerman, B. Moor, Y. Moreau
We have developed a World Wide Web application for the collection of EPRs (electronic patient records) from uterine adnexal masses pre-operatively examined with transvaginal ultrasonography. The application has been used intensively since November 2000 by nine of the 19 international centers that joined the International Ovarian Tumor Analysis (IOTA) consortium. The IOTA database contains 68 parameters for 1,150 masses. We report the design and implementation of the generic Web-based clinical data entry system and describe the advantages and drawbacks that we have experienced while developing, using and maintaining the system. The data model, the user interface, the help system, the constraints (mandatory/optional) and the quality checking were all based on the medical protocol created by the IOTA consortium. The data collection system has become an open and transparent implementation of the formalized protocol. It covers the complete path of the patient data from the clinical situation to the finalized database. This approach provides new types of possibilities for the data analysis, since all aspects of the data collection are documented and formally available to the data analyst. The IOTA Web site can be found at , which also serves as the entry point for the secure EPR application.
{"title":"Web-based data collection for uterine adnexal tumors: a case study","authors":"S. Aerts, P. Antal, D. Timmerman, B. Moor, Y. Moreau","doi":"10.1109/CBMS.2002.1011390","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011390","url":null,"abstract":"We have developed a World Wide Web application for the collection of EPRs (electronic patient records) from uterine adnexal masses pre-operatively examined with transvaginal ultrasonography. The application has been used intensively since November 2000 by nine of the 19 international centers that joined the International Ovarian Tumor Analysis (IOTA) consortium. The IOTA database contains 68 parameters for 1,150 masses. We report the design and implementation of the generic Web-based clinical data entry system and describe the advantages and drawbacks that we have experienced while developing, using and maintaining the system. The data model, the user interface, the help system, the constraints (mandatory/optional) and the quality checking were all based on the medical protocol created by the IOTA consortium. The data collection system has become an open and transparent implementation of the formalized protocol. It covers the complete path of the patient data from the clinical situation to the finalized database. This approach provides new types of possibilities for the data analysis, since all aspects of the data collection are documented and formally available to the data analyst. The IOTA Web site can be found at , which also serves as the entry point for the secure EPR application.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116076213","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011373
Z. Hashmi, S. Abidi, Y. Cheah
Within the confines of a healthcare enterprise memory (HEM), most traditional medical systems do not sufficiently provide the necessary assistance to healthcare practitioners in the handling of critical situations. Furthermore, localized knowledge repositories often lack the required knowledge for problem solving. Therefore, in this paper, we present an agent-based knowledge broker called the Intelligent Healthcare Knowledge Assistant (IHKA) for dynamic knowledge gathering, filtering, adaptation and acquisition from a HEM comprising an amalgamation of (i) databases storing empirical knowledge, (ii) case bases storing experiential knowledge, (iii) scenario bases storing tacit knowledge and (iv) document bases storing explicit knowledge. The featured work leverages intelligent agent techniques for autonomous HEM-wide navigation, approximate content matching, inter- and intra-content correlation, and knowledge adaptation and procurement to meet the user's healthcare knowledge needs.
{"title":"An intelligent agent-based knowledge broker for enterprise-wide healthcare knowledge procurement","authors":"Z. Hashmi, S. Abidi, Y. Cheah","doi":"10.1109/CBMS.2002.1011373","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011373","url":null,"abstract":"Within the confines of a healthcare enterprise memory (HEM), most traditional medical systems do not sufficiently provide the necessary assistance to healthcare practitioners in the handling of critical situations. Furthermore, localized knowledge repositories often lack the required knowledge for problem solving. Therefore, in this paper, we present an agent-based knowledge broker called the Intelligent Healthcare Knowledge Assistant (IHKA) for dynamic knowledge gathering, filtering, adaptation and acquisition from a HEM comprising an amalgamation of (i) databases storing empirical knowledge, (ii) case bases storing experiential knowledge, (iii) scenario bases storing tacit knowledge and (iv) document bases storing explicit knowledge. The featured work leverages intelligent agent techniques for autonomous HEM-wide navigation, approximate content matching, inter- and intra-content correlation, and knowledge adaptation and procurement to meet the user's healthcare knowledge needs.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125749748","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011349
J. Estévez, S. Alayón, L. M. Ruiz, R. Aguilar, J. Sigut
A system based on a fuzzy finite state machine (FFSM) has been developed for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. The system uses computer vision techniques to analyse cell nuclei in order to extract determinate features and to try to find, by means of genetic algorithms (GA), the ideal FFSM that is able to classify them. This application to breast cancer diagnosis uses the characteristics of individual cells to discriminate benign from malignant breast lumps. In our system, we try to find a texture measurement that can be included in the feature set in order to improve the classifier performance: a complexity measurement of the structural pattern is used to discriminate between benign and malign cells. With this measure and the technique described, we have observed that not only is the absolute complexity of the image relevant, but also the way in which the complexity is distributed at different scales.
{"title":"Cytological breast fine needle aspirate images analysis with a genetic fuzzy finite state machine","authors":"J. Estévez, S. Alayón, L. M. Ruiz, R. Aguilar, J. Sigut","doi":"10.1109/CBMS.2002.1011349","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011349","url":null,"abstract":"A system based on a fuzzy finite state machine (FFSM) has been developed for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. The system uses computer vision techniques to analyse cell nuclei in order to extract determinate features and to try to find, by means of genetic algorithms (GA), the ideal FFSM that is able to classify them. This application to breast cancer diagnosis uses the characteristics of individual cells to discriminate benign from malignant breast lumps. In our system, we try to find a texture measurement that can be included in the feature set in order to improve the classifier performance: a complexity measurement of the structural pattern is used to discriminate between benign and malign cells. With this measure and the technique described, we have observed that not only is the absolute complexity of the image relevant, but also the way in which the complexity is distributed at different scales.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125047982","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011406
M. Miquel, A. Tchounikine
Data warehousing is an attractive solution for centralizing and analyzing high-quality data. In the medical research field, this technology can be used to validate assumptions and to discover trends in large amounts of patient data. However, in many medical studies based on knowledge extraction, medical data, and especially raw sensor data, need to be pre-processed before turning to valuable material for data-mining features. Scientists, researchers and physicians emphasize the necessity to be able to select raw sensor data, processed information, and also the appropriate transforming processes. We propose a solution to ease medical data mining by integrating data and processes in a single warehouse. Our solution provides features for loading, modelling and querying a medical data warehouse. The stored data are multi-dimensional data (patient identity, therapeutic data, etc.), raw sensor data (ECG, X-rays, etc.) and software components. A prototype has been implemented and is illustrated in the cardiology domain.
{"title":"Software components integration in medical data warehouses: a proposal","authors":"M. Miquel, A. Tchounikine","doi":"10.1109/CBMS.2002.1011406","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011406","url":null,"abstract":"Data warehousing is an attractive solution for centralizing and analyzing high-quality data. In the medical research field, this technology can be used to validate assumptions and to discover trends in large amounts of patient data. However, in many medical studies based on knowledge extraction, medical data, and especially raw sensor data, need to be pre-processed before turning to valuable material for data-mining features. Scientists, researchers and physicians emphasize the necessity to be able to select raw sensor data, processed information, and also the appropriate transforming processes. We propose a solution to ease medical data mining by integrating data and processes in a single warehouse. Our solution provides features for loading, modelling and querying a medical data warehouse. The stored data are multi-dimensional data (patient identity, therapeutic data, etc.), raw sensor data (ECG, X-rays, etc.) and software components. A prototype has been implemented and is illustrated in the cardiology domain.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116281550","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011363
V. Podgorelec, P. Kokol, Milojka Molan Stiglic
We study a new approach to rule induction from medical data sets with the help of physicians' assessment of the progressing solution in order to find new patterns in the available data. A method for the automatic extraction of rules is presented which is based on the evolutionary induction of decision trees and on automatic programming. The method is applied to a cardiovascular database. Several sets of rules are induced upon different groups of attributes which may possibly reveal the presence of some specific cardiovascular problems in young patients. The obtained rules are assessed by a physician in order to evaluate the strength of the developed knowledge discovery method.
{"title":"Searching for new patterns in cardiovascular data","authors":"V. Podgorelec, P. Kokol, Milojka Molan Stiglic","doi":"10.1109/CBMS.2002.1011363","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011363","url":null,"abstract":"We study a new approach to rule induction from medical data sets with the help of physicians' assessment of the progressing solution in order to find new patterns in the available data. A method for the automatic extraction of rules is presented which is based on the evolutionary induction of decision trees and on automatic programming. The method is applied to a cardiovascular database. Several sets of rules are induced upon different groups of attributes which may possibly reveal the presence of some specific cardiovascular problems in young patients. The obtained rules are assessed by a physician in order to evaluate the strength of the developed knowledge discovery method.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130933921","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}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011358
Reeva M. Lederman, C. Parkes
We examine the prescribing process in a hospital to see whether information systems failure contributes to the occurrence of prescribing errors. J.T. Reason's (1990) model of organisational failure suggests that this may be the case. Reason identified circumstances in work systems and processes where errors may occur. While Reason's model has been applied in a medical context, it has not previously been linked to errors which result from information systems failure. It is held, however, that the model can be used as a predictive tool, suggesting that prescribing errors have an increased likelihood of occurring if one or more of the types of failure identified in Reason's model are present in the existing information delivery process in a hospital. In this paper, we examine the application of Reason's model in predicting prescribing errors and then calculate the extent to which these errors are evident in the hospital ward under examination.
{"title":"Exploring errors in a medication process: an analysis of information delivery","authors":"Reeva M. Lederman, C. Parkes","doi":"10.1109/CBMS.2002.1011358","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011358","url":null,"abstract":"We examine the prescribing process in a hospital to see whether information systems failure contributes to the occurrence of prescribing errors. J.T. Reason's (1990) model of organisational failure suggests that this may be the case. Reason identified circumstances in work systems and processes where errors may occur. While Reason's model has been applied in a medical context, it has not previously been linked to errors which result from information systems failure. It is held, however, that the model can be used as a predictive tool, suggesting that prescribing errors have an increased likelihood of occurring if one or more of the types of failure identified in Reason's model are present in the existing information delivery process in a hospital. In this paper, we examine the application of Reason's model in predicting prescribing errors and then calculate the extent to which these errors are evident in the hospital ward under examination.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133803891","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：