Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011379
P. Antal, D. Timmerman, T. Mészáros, T. Dobrowiecki
The increasing amount and variety of domain knowledge and the availability of increasingly large quantities of electronic literature requires new types of support for the development of complex knowledge models. P. Antal et al. (2001) proposed the application of so-called annotated Bayesian networks (ABNs), which are textually-enriched probabilistic domain models that help knowledge engineers and medical experts to find and organize the information that is necessary in model-building. In this paper, we describe an information retrieval language in which the formalized domain knowledge and the attached textual information can be accessed in an integrated fashion and can be used to define various retrieval schemes and relevance measures. This language on the one hand provides maximum flexibility for knowledge engineers to exploit the available annotated domain model as contextual information. On the other hand, it allows the definition of complex, high-level queries, in which the contextual use of the annotated domain model can be optimized for clinical situations. We compare the performance of the standard and the proposed query language in the ovarian cancer domain.
{"title":"Domain knowledge based information retrieval language: an application of annotated Bayesian networks in ovarian cancer domain","authors":"P. Antal, D. Timmerman, T. Mészáros, T. Dobrowiecki","doi":"10.1109/CBMS.2002.1011379","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011379","url":null,"abstract":"The increasing amount and variety of domain knowledge and the availability of increasingly large quantities of electronic literature requires new types of support for the development of complex knowledge models. P. Antal et al. (2001) proposed the application of so-called annotated Bayesian networks (ABNs), which are textually-enriched probabilistic domain models that help knowledge engineers and medical experts to find and organize the information that is necessary in model-building. In this paper, we describe an information retrieval language in which the formalized domain knowledge and the attached textual information can be accessed in an integrated fashion and can be used to define various retrieval schemes and relevance measures. This language on the one hand provides maximum flexibility for knowledge engineers to exploit the available annotated domain model as contextual information. On the other hand, it allows the definition of complex, high-level queries, in which the contextual use of the annotated domain model can be optimized for clinical situations. We compare the performance of the standard and the proposed query language in the ovarian cancer domain.","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":"128929904","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.2002.1011380
S. Abidi
We present a personalized health information generation and delivery system that leverages case-based reasoning techniques to dynamically author a personalized health information package based on an individual's current health profile. The work features a compositional adaptation approach, whereby relevant health information elements from the solution component of multiple similar past cases are carefully selected and systematically combined to yield a new personalized health information package. We have implemented a generic Java-based case-based reasoning engine that applies a novel compositional adaptation algorithm to author a HTML-based personalized health information package that can be e-mailed to users.
{"title":"A case base reasoning framework to author personalized health maintenance information","authors":"S. Abidi","doi":"10.1109/CBMS.2002.1011380","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011380","url":null,"abstract":"We present a personalized health information generation and delivery system that leverages case-based reasoning techniques to dynamically author a personalized health information package based on an individual's current health profile. The work features a compositional adaptation approach, whereby relevant health information elements from the solution component of multiple similar past cases are carefully selected and systematically combined to yield a new personalized health information package. We have implemented a generic Java-based case-based reasoning engine that applies a novel compositional adaptation algorithm to author a HTML-based personalized health information package that can be e-mailed to users.","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":"129186881","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.2002.1011409
L. Frank, A. Mukherjee
We describe how it is possible for a group of hospitals to implement both distributed and integrated electronic patient encounters with high performance and availability. We also describe how it is possible in such a system to implement approximated ACID (atomicity, consistency, isolation and durability) properties by using the countermeasure transaction model. This is used to implement recovery, and approximated concurrency control.
{"title":"Distributed electronic patient encounter with high performance and availability","authors":"L. Frank, A. Mukherjee","doi":"10.1109/CBMS.2002.1011409","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011409","url":null,"abstract":"We describe how it is possible for a group of hospitals to implement both distributed and integrated electronic patient encounters with high performance and availability. We also describe how it is possible in such a system to implement approximated ACID (atomicity, consistency, isolation and durability) properties by using the countermeasure transaction model. This is used to implement recovery, and approximated concurrency control.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"33 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":"123817464","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.2002.1011364
G. Masuda, N. Sakamoto, Ryuichi Yamamoto
Dynamic evidence-based medicine (DEBM) is defined as the process of finding evidence about the care of individual patients automatically and dynamically in those cases when we cannot rely on any literature or guidelines. In this paper, we develop a framework for DEBM using data mining technologies that make it possible to automatically analyze huge clinical databases and to discover patterns behind them. We define the requirements of a data mining system for DEBM. The following functions are required of the system: (1) support for clinical decision making, and (2) discovery of rare patterns which human beings can hardly find. In order to support clinical decision making, rule discovery methods such as association rule mining are applied to this framework. We adopt a post-analysis approach using a rule base and queries. The discovered rules are collected into a rule base for further analysis. By submitting queries to the rule base, users can obtain keys to evidence for making decisions about clinical care. We preliminarily implement a prototype of a rule base and a post-analysis tool based on our framework. This tool can assist users in analyzing the discovered rules.
{"title":"A framework for dynamic evidence based medicine using data mining","authors":"G. Masuda, N. Sakamoto, Ryuichi Yamamoto","doi":"10.1109/CBMS.2002.1011364","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011364","url":null,"abstract":"Dynamic evidence-based medicine (DEBM) is defined as the process of finding evidence about the care of individual patients automatically and dynamically in those cases when we cannot rely on any literature or guidelines. In this paper, we develop a framework for DEBM using data mining technologies that make it possible to automatically analyze huge clinical databases and to discover patterns behind them. We define the requirements of a data mining system for DEBM. The following functions are required of the system: (1) support for clinical decision making, and (2) discovery of rare patterns which human beings can hardly find. In order to support clinical decision making, rule discovery methods such as association rule mining are applied to this framework. We adopt a post-analysis approach using a rule base and queries. The discovered rules are collected into a rule base for further analysis. By submitting queries to the rule base, users can obtain keys to evidence for making decisions about clinical care. We preliminarily implement a prototype of a rule base and a post-analysis tool based on our framework. This tool can assist users in analyzing the discovered rules.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"2 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":"122528256","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.2002.1011381
A. Tsymbal, S. Puuronen, D. Patterson
Ensembles of simple Bayesian classifiers have traditionally not been in the focus of classification research partly because of the stability of a simple Bayesian classifier and because of the rarely valid basic assumption that the classification features are independent of each other, given the predicted value. As a way to try to circumvent these problems we suggest the use of an ensemble of simple Bayesian classifiers each concentrating on solving a sub-problem of the problem domain. Our experiments with the problem of separating acute appendicitis show that in this way it is possible to retain the comprehensibility and at the same time to increase the diagnostic accuracy, sensitivity, and specificity. The advantages of the approach include also simplicity and speed of learning, small storage space needed during the classification, speed of classification, and the possibility of incremental learning.
{"title":"Ensemble feature selection with the simple Bayesian classification in medical diagnostics","authors":"A. Tsymbal, S. Puuronen, D. Patterson","doi":"10.1109/CBMS.2002.1011381","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011381","url":null,"abstract":"Ensembles of simple Bayesian classifiers have traditionally not been in the focus of classification research partly because of the stability of a simple Bayesian classifier and because of the rarely valid basic assumption that the classification features are independent of each other, given the predicted value. As a way to try to circumvent these problems we suggest the use of an ensemble of simple Bayesian classifiers each concentrating on solving a sub-problem of the problem domain. Our experiments with the problem of separating acute appendicitis show that in this way it is possible to retain the comprehensibility and at the same time to increase the diagnostic accuracy, sensitivity, and specificity. The advantages of the approach include also simplicity and speed of learning, small storage space needed during the classification, speed of classification, and the possibility of incremental learning.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"366 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":"114862103","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.2002.1011405
Néstor J. Rodríguez, D. Z. Sands
The interaction style used in electronic patient record (EPR) systems and its usability can have a significant impact on the acceptance, efficiency and satisfaction of its users. In this paper, we describe a study of physician interaction with a text-based EPR system and a graphical-based EPR system. The usability attributes of learnability, efficiency and satisfaction are evaluated on typical tasks, such as viewing a patient's record, documenting and ordering. The results of the study revealed that a graphical-based interface can significantly reduce the time it takes physicians to complete typical tasks in comparison with a text-based interface. The results of the study also revealed that physicians can get more satisfaction from interacting with a graphical-based EPR system than with a text-based system.
{"title":"A study of physicians' interaction with text-based and graphical-based electronic patient record systems","authors":"Néstor J. Rodríguez, D. Z. Sands","doi":"10.1109/CBMS.2002.1011405","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011405","url":null,"abstract":"The interaction style used in electronic patient record (EPR) systems and its usability can have a significant impact on the acceptance, efficiency and satisfaction of its users. In this paper, we describe a study of physician interaction with a text-based EPR system and a graphical-based EPR system. The usability attributes of learnability, efficiency and satisfaction are evaluated on typical tasks, such as viewing a patient's record, documenting and ordering. The results of the study revealed that a graphical-based interface can significantly reduce the time it takes physicians to complete typical tasks in comparison with a text-based interface. The results of the study also revealed that physicians can get more satisfaction from interacting with a graphical-based EPR system than with a text-based system.","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":"122256556","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.2002.1011368
Lisheng Xu, Kuanquan Zhang, David Zhang, Shih-Min Cheng
The pulse waveform plays an important role in pulse diagnosis, which is the key technique in traditional Chinese medicine. However, its baseline wander introduced in the acquisition process will result in misdiagnosis. Therefore a wavelet based cascade adaptive filter to remove this wander is presented. This cascade adaptive filter works in two stages. The first stage is a discrete Meyer wavelet filter and the second stage is the cubic spline estimation. Compared with some traditional methods, such as cubic spline estimation and linear-phase FIR least-squares error minimization digital filter, the proposed approach has better performance for removing the baseline wander of the pulse waveform.
{"title":"Adaptive baseline wander removal in the pulse waveform","authors":"Lisheng Xu, Kuanquan Zhang, David Zhang, Shih-Min Cheng","doi":"10.1109/CBMS.2002.1011368","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011368","url":null,"abstract":"The pulse waveform plays an important role in pulse diagnosis, which is the key technique in traditional Chinese medicine. However, its baseline wander introduced in the acquisition process will result in misdiagnosis. Therefore a wavelet based cascade adaptive filter to remove this wander is presented. This cascade adaptive filter works in two stages. The first stage is a discrete Meyer wavelet filter and the second stage is the cubic spline estimation. Compared with some traditional methods, such as cubic spline estimation and linear-phase FIR least-squares error minimization digital filter, the proposed approach has better performance for removing the baseline wander of the pulse waveform.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"54 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":"122174483","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.2002.1011354
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.2002.1011349
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.2002.1011373
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}
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