Application of fuzzy ontological reasoning in an implementation of medical guidelines
06 Jun 2013-pp 342-349
TL;DR: It is shown that, despite the fact that commonly used ontology languages and supporting tools are not intended to handle vagueness and uncertainty, they can be successfully integrated to represent and execute a set of fuzzy rules.
Abstract: In this paper we address two problems. The first pertains to implementation of medical guidelines in an e-health system supporting self-management of chronic diseases. The system allows patients to enter observed symptoms and measured parameters, then makes assessment of disease state and informs about necessary actions. We propose to formalize guidelines as sets of fuzzy rules. Fuzziness is introduced to cope with uncertainty resulting from self-observations bias, low quality of sensors and limited patients skills. The second problem is more general. It concerns the reuse of knowledge gathered in ontologies and an application of Semantic Web technologies to perform fuzzy inference. We show that, despite the fact that commonly used ontology languages and supporting tools are not intended to handle vagueness and uncertainty, they can be successfully integrated to represent and execute a set of fuzzy rules. The proposed method consists in refactoring a domain ontology, then introducing additional relations expressing fuzzy properties, encoding Mamdani fuzzy rules in SWRL language and executing them with use of Pellet OWL reasoner. We describe a fuzzy reasoning engine applying this approach and discuss translation of fuzzy rules to SWRL constructs taking as example a complete set of rules formalizing a medical guideline for asthma control assessment.
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05 Jul 2015
TL;DR: Control of Discrete-event Systems provides a survey of the most important topics in the discrete-event systems theory with particular focus on finite-state automata, Petri nets and max-plus algebra.
Abstract: Control of Discrete-event Systems provides a survey of the most important topics in the discrete-event systems theory with particular focus on finite-state automata, Petri nets and max-plus algebra. Coverage ranges from introductory material on the basic notions and definitions of discrete-event systems to more recent results. Special attention is given to results on supervisory control, state estimation and fault diagnosis of both centralized and distributed/decentralized systems developed in the framework of the Distributed Supervisory Control of Large Plants (DISC) project. Later parts of the text are devoted to the study of congested systems though fluidization, an over approximation allowing a much more efficient study of observation and control problems of timed Petri nets. Finally, the max-plus algebraic approach to the analysis and control of choice-free systems is also considered. Control of Discrete-event Systems provides an introduction to discrete-event systems for readers that are not familiar with this class of systems, but also provides an introduction to research problems and open issues of current interest to readers already familiar with them. Most of the material in this book has been presented during a Ph.D. school held in Cagliari, Italy, in June 2011. This book constitutes the refereed proceedings of the Third International Workshop on Tools and Algorithms for the Construction and Analysis of Systems, TACAS '97, held in Enschede, The Netherlands, in April 1997. The book presents 20 revised full papers and 5 tool demonstrations carefully selected out of 54 submissions; also included are two extended abstracts and a full paper corresponding to invited talks. The papers are organized in topical sections on space reduction techniques, tool demonstrations, logical techniques, verification support, specification and analysis, and theorem proving, model checking and applications. The refereed proceedings of the 24th International Conference on Applications and Theory of Petri Nets, ICATPN 2003, held in Eindhoven, The Netherlands, in June 2003. The 25 revised full papers presented together with 6 invited contributions were carefully reviewed and selected from 77 submissions. All current issues on research and development in the area of Petri nets are addressed, in particular concurrent systems design and analysis, model checking, networking, business process modeling, formal methods in software engineering, agent systems, systems specification, systems validation, discrete event systems, protocols, and prototyping. The contents of this volume are application oriented. The volume contains a de tailed presentation of 19 applications of CP-nets, covering a broad range of ap plication areas. Most of the projects have been carried out in an industrial set ting.
315 citations
TL;DR: Fuzzy Cognitive Maps are used to capture dependencies between assets, and FCM-based reasoning is performed to calculate risks, and lessons learned indicate that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on the effectiveness of the security system.
Abstract: For contemporary software systems, security is considered to be a key quality factor and the analysis of IT security risk becomes an indispensable stage during software deployment. However, performing risk assessment according to methodologies and standards issued for the public sector or large institutions can be too costly and time consuming. Current business practice tends to circumvent risk assessment by defining sets of standard safeguards and applying them to all developed systems. This leads to a substantial gap: threats are not re-evaluated for particular systems and the selection of security functions is not based on risk models. This paper discusses a new lightweight risk assessment method aimed at filling this gap. In this proposal, Fuzzy Cognitive Maps (FCMs) are used to capture dependencies between assets, and FCM-based reasoning is performed to calculate risks. An application of the method is studied using an example of an e-health system providing remote telemonitoring, data storage and teleconsultation services. Lessons learned indicate that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on the effectiveness of the security system.
38 citations
Cites methods from "Application of fuzzy ontological re..."
...Additional information related to the system architecture, the technologies used and particular communication solutions can be found in the works of Szwed (2013), Szwed et al. (2013), or Kobylarz and Danda (2013)....
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TL;DR: This paper focuses on realizing the urgent need for interoperability in EHR to improve healthcare quality, and trying to solve that problem by recommending fuzzy ontology as an intelligent information system solution.
Abstract: Over the past years, the knowledge and data are rising rapidly with the development of communication technology. There is a growing demand for integrating data, especially the medical healthcare da...
17 citations
09 Nov 2015
TL;DR: A user-friendly method for specification of transformation patterns, which is based on a simple annotations language, is proposed for extraction of concepts from unstructured Polish texts with special focus on correct morphological forms of obtained concept names.
Abstract: We present recently developed solution allowing extraction of concepts from unstructured Polish texts with special focus on correct morphological forms of obtained concept names. As Polish is a highly inflected language, detected names need to be transformed following Polish grammar rules. We propose a user-friendly method for specification of transformation patterns, which is based on a simple annotations language. Annotations prepared by a user are compiled into transformation rules. During the concept extraction process the input document is split into sentences and the rules are applied to sequences of words comprised in sentences. Recognized strings forming concept names are aggregated at various levels and assigned with scores. We report also results of initial experiments performed on a medical text.
10 citations
Cites methods from "Application of fuzzy ontological re..."
...We selected this document, as it was used in previous works aiming at building ontologies of medical guidelines and performing fuzzy reasoning based on ontological models [24]....
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01 Jan 2019
TL;DR: A unified semantic interoperability framework for distributed EHR based on fuzzy ontology is proposed, which has many benefits and advantages over frameworks that rely on crisp ontology only and supports the idea of plug and play where any system with any structure can be integrated anonymously with existing systems without affecting the current working environment.
Abstract: Electronic health records (EHR) provide efficient management of clinical information in any healthcare organization. It is a complete and longitudinal electronic registration of all occasions and data identified with the person's health status, from birth to death. Medical data are growing rapidly. These data are heterogeneous, distributed, and nonstructured. Each data element can have its schema, structure, standard, format, coding system, level of abstraction, and semantic. Medical personnel need to query the distributed EHR systems anonymously by using a single language. Combination and integration of the data are vital to recover the history of patients, to share information, and to elicit queries. Semantic interoperability provides a meaningful exchange and the use of clinical data between many healthcare systems. Physicians often send fuzzy questions to EHR systems and need answers from distributed systems. In this chapter, a unified semantic interoperability framework for distributed EHR based on fuzzy ontology is proposed. The framework architecture consists of three main layers. The lowest layer (local ontologies construction) stores the EHRs heterogeneous data with different database schemas, standards, terminologies, purposes, locations, and formats. The sources of this information may be different databases (e.g., MySQL, SqlServer, DB2, Access, and Oracle) in heterogeneous schemas, EHR standards, XML files, spreadsheet files, or archetype definition language (ADL) files. These different inputs are transformed into crisp ontology using a mediator (e.g., DB2OWL, X2OWL or ADL2OntoModule) suitable for each type. In the middle layer (global ontology construction), the local ontologies are mapped (using mapping algorithms or human experts with the help of common terminology vocabularies) to a crisp global one. The global reference ontology combines and integrates all local ontologies and therefore describes all data. Then this crisp ontology is converted to a unified fuzzy ontology. Finally, the third layer is the user interface in which a doctor or any specialist can ask any linguistic or semantic queries by dealing with only the global reference fuzzy ontology. That ontology is more dynamic and helps in understanding natural language deep medical queries. The result is a global and robust semantic interoperability technique. The proposed solution is based on a fuzzy ontology semantic to integrate different healthcare systems. That framework has many benefits and advantages over frameworks that rely on crisp ontology only, including: (1) it moves toward achieving full semantic interoperability of heterogeneous EHRs, (2) it supports the idea of plug and play where any system with any structure can be integrated anonymously with existing systems without affecting the current working environment, and (3) it is an expandable and designed in a modular way as it based on using ontologies and terminologies; the functionality of the proposed framework can be extended uniformly. We expect that our framework will handle the current EHR semantic interoperability challenges, reduce the cost of the integration process, and get a higher acceptance and accuracy rate than previous studies.
9 citations
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10,451 citations
"Application of fuzzy ontological re..." refers methods in this paper
...The model is based on Global Initiative for Asthma (GINA) guideline as of 2011 [9]....
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01 Aug 1999-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy, and the control strategy set up linguistically proved to be far better than expected in its own right.
Abstract: This paper describes an experiment on the “linguistic” synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.
6,392 citations
Book•
01 Dec 1994
TL;DR: This chapter discusses Fuzzy Systems Simulation, specifically the development of Membership Functions and the Extension Principle, and some of the methods used to derive these functions.
Abstract: About the Author. Preface to the Third Edition. 1 Introduction. The Case for Imprecision. A Historical Perspective. The Utility of Fuzzy Systems. Limitations of Fuzzy Systems. The Illusion: Ignoring Uncertainty and Accuracy. Uncertainty and Information. The Unknown. Fuzzy Sets and Membership. Chance Versus Fuzziness. Sets as Points in Hypercubes. Summary. References. Problems. 2 Classical Sets and Fuzzy Sets. Classical Sets. Operations on Classical Sets. Properties of Classical (Crisp) Sets. Mapping of Classical Sets to Functions. Fuzzy Sets. Fuzzy Set Operations. Properties of Fuzzy Sets. Alternative Fuzzy Set Operations. Summary. References. Problems. 3 Classical Relations and Fuzzy Relations. Cartesian Product. Crisp Relations. Cardinality of Crisp Relations. Operations on Crisp Relations. Properties of Crisp Relations. Composition. Fuzzy Relations. Cardinality of Fuzzy Relations. Operations on Fuzzy Relations. Properties of Fuzzy Relations. Fuzzy Cartesian Product and Composition. Tolerance and Equivalence Relations. Crisp Equivalence Relation. Crisp Tolerance Relation. Fuzzy Tolerance and Equivalence Relations. Value Assignments. Cosine Amplitude. Max Min Method. Other Similarity Methods. Other Forms of the Composition Operation. Summary. References. Problems. 4 Properties of Membership Functions, Fuzzification, and Defuzzification. Features of the Membership Function. Various Forms. Fuzzification. Defuzzification to Crisp Sets. -Cuts for Fuzzy Relations. Defuzzification to Scalars. Summary. References. Problems. 5 Logic and Fuzzy Systems. Part I Logic. Classical Logic. Proof. Fuzzy Logic. Approximate Reasoning. Other Forms of the Implication Operation. Part II Fuzzy Systems. Natural Language. Linguistic Hedges. Fuzzy (Rule-Based) Systems. Graphical Techniques of Inference. Summary. References. Problems. 6 Development of Membership Functions. Membership Value Assignments. Intuition. Inference. Rank Ordering. Neural Networks. Genetic Algorithms. Inductive Reasoning. Summary. References. Problems. 7 Automated Methods for Fuzzy Systems. Definitions. Batch Least Squares Algorithm. Recursive Least Squares Algorithm. Gradient Method. Clustering Method. Learning From Examples. Modified Learning From Examples. Summary. References. Problems. 8 Fuzzy Systems Simulation. Fuzzy Relational Equations. Nonlinear Simulation Using Fuzzy Systems. Fuzzy Associative Memories (FAMS). Summary. References. Problems. 9 Decision Making with Fuzzy Information. Fuzzy Synthetic Evaluation. Fuzzy Ordering. Nontransitive Ranking. Preference and Consensus. Multiobjective Decision Making. Fuzzy Bayesian Decision Method. Decision Making Under Fuzzy States and Fuzzy Actions. Summary. References. Problems. 10 Fuzzy Classification. Classification by Equivalence Relations. Crisp Relations. Fuzzy Relations. Cluster Analysis. Cluster Validity. c-Means Clustering. Hard c-Means (HCM). Fuzzy c-Means (FCM). Fuzzy c-Means Algorithm. Classification Metric. Hardening the Fuzzy c-Partition. Similarity Relations from Clustering. Summary. References. Problems. 11 Fuzzy Pattern Recognition. Feature Analysis. Partitions of the Feature Space. Single-Sample Identification. Multifeature Pattern Recognition. Image Processing. Summary. References. Problems. 12 Fuzzy Arithmetic and the Extension Principle. Extension Principle. Crisp Functions, Mapping, and Relations. Functions of Fuzzy Sets Extension Principle. Fuzzy Transform (Mapping). Practical Considerations. Fuzzy Arithmetic. Interval Analysis in Arithmetic. Approximate Methods of Extension. Vertex Method. DSW Algorithm. Restricted DSW Algorithm. Comparisons. Summary. References. Problems. 13 Fuzzy Control Systems. Control System Design Problem. Control (Decision) Surface. Assumptions in a Fuzzy Control System Design. Simple Fuzzy Logic Controllers. Examples of Fuzzy Control System Design. Aircraft Landing Control Problem. Fuzzy Engineering Process Control. Classical Feedback Control. Fuzzy Control. Fuzzy Statistical Process Control. Measurement Data Traditional SPC. Attribute Data Traditional SPC. Industrial Applications. Summary. References. Problems. 14 Miscellaneous Topics. Fuzzy Optimization. One-Dimensional Optimization. Fuzzy Cognitive Mapping. Concept Variables and Causal Relations. Fuzzy Cognitive Maps. Agent-Based Models. Summary. References. Problems. 15 Monotone Measures: Belief, Plausibility, Probability, and Possibility. Monotone Measures. Belief and Plausibility. Evidence Theory. Probability Measures. Possibility and Necessity Measures. Possibility Distributions as Fuzzy Sets. Possibility Distributions Derived from Empirical Intervals. Deriving Possibility Distributions from Overlapping Intervals. Redistributing Weight from Nonconsonant to Consonant Intervals. Comparison of Possibility Theory and Probability Theory. Summary. References. Problems. Index.
4,958 citations
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...[18] control, home appliances, cameras, embedded systems, etc....
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Book•
17 Oct 2011
TL;DR: The third volume of a definitive work on coloured Petri nets as discussed by the authors contains a detailed presentation of 19 applications of CP-nets across a broad range of application areas, including a security system, ATM networks, audio/video systems, transaction processing, ISDN services, VLSI chips, document storage, distributed programming, electronic funds transfer, a naval vessel, chemical processing, nuclear waste management, and many more.
Abstract: This is the third volume of a definitive work on coloured Petri nets. It contains a detailed presentation of 19 applications of CP-nets across a broad range of application areas, including a security system, ATM networks, audio/video systems, transaction processing, ISDN services, VLSI chips, document storage, distributed programming, electronic funds transfer, a naval vessel, chemical processing, nuclear waste management, and many more. Most of the projects were carried out in an industrial setting, and in each case the original authors have cooperated with the author and approved the new presentation. The author has taken care to unify the terminology and the CPN diagrams and to ensure that the background knowledge required has been provided in the first two volumes of the work.
2,041 citations
Book•
[...]
01 Jan 2009
TL;DR: All users of CP-nets are forced to make simulations because it is impossible to construct a CP-net without thinking about the possible effects of the individual transitions, and the proper question is not whether the modeller should make simulations or not, but whether he wants computer support for the simulation activity.
Abstract: data base When the user creates a CPN diagram, the editor stores all the semantic information in an abstract data base, from which it can easily be retrieved by the CPN simulator and other analysis programs. The abstract data base was designed as a relational data base, but for efficiency it is implemented by means of a set of list structures making the most commonly used data base operations as efficient as possible. The existence of the abstract data base makes it much easier to integrate new and existing editors and analysis programs with the CPN tools. For this purpose CPN ML provides three sets of functions. The first set reads the information of the abstract data base, e.g., the colour set of a place. The second set creates pages and auxiliary objects (which have a graphical representation, but no representation in the abstract data base). Finally, the third set converts auxiliary objects to CPN objects (which means that they become included in the 176 6 Computer Tools for Coloured Petri Nets abstract data base). The three sets of functions make it easy to write programs which translate CPN diagrams into textual or graphical representations of other Petri net tools, and vice versa.data base). The three sets of functions make it easy to write programs which translate CPN diagrams into textual or graphical representations of other Petri net tools, and vice versa. 6.2 Simulation of CP-nets Simulation of CP-nets can be supported by a computer tool or it can be totally manual, for example, performed on a blackboard or in the head of a modeller. Simulation is similar to the debugging of a program, in the sense that it can reveal errors, but in practice it can never be sufficient to prove the correctness of a system. Some people argue that this makes simulation uninteresting and that the user should instead concentrate on the more formal analysis methods. We do not agree with this conclusion. On the contrary, we consider simulation to be just as important and necessary as the formal analysis methods. In our opinion, all users of CP-nets (and other kinds of Petri nets) are forced to make simulations because it is impossible to construct a CP-net without thinking about the possible effects of the individual transitions. Thus the proper question is not whether the modeller should make simulations or not, but whether he wants computer support for the simulation activity. With this rephrasing the answer becomes trivial. Of course, we want computer support. This means that the simulations can be done much faster and with no errors. Moreover, it means that the modeller can use all his mental capabilities to interpret the simulation results instead of using most of his efforts to calculate the possible occurrence sequences. Simulation is often used in the design phases and the early investigation of a system design, while the more formal analysis methods are used for validation.
952 citations