Fuzzy Temporal Logic Research Articles

Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital

BackgroundThe increasing aging population presents a significant challenge, accompanied by a shortage of professional caregivers, adding to the therapeutic burden. Clinical decision support systems, utilizing computerized clinical guidelines, can improve healthcare quality, reduce expenses, save time, and boost caregiver efficiency. Objectives1) Develop and evaluate an automated quality assessment (QA) system for retrospective longitudinal care quality analysis, focusing on clinical staff adherence to evidence-based guidelines (GLs). 2) Assess the system’s technical feasibility and functional capability for senior nurse use in geriatric pressure-ulcer management. MethodsA computational QA system using our Quality Assessment Temporal Patterns (QATP) methodology was designed and implemented. Our methodology transforms the GL’s procedural-knowledge into declarative-knowledge temporal-abstraction patterns representing the expected execution trace in the patient’s data for correct therapy application. Fuzzy temporal logic allows for partial compliance, reflecting individual and grouped action performance considering their values and temporal aspects. The system was tested using a pressure ulcer treatment GL and data from 100 geriatric patients’ Electronic Medical Records (EMR). After technical evaluation for accuracy and feasibility, an extensive functional evaluation was conducted by an experienced nurse, comparing QA scores with and without system support, and versus automated system scores. Time efficiency was also measured. ResultsQA scores from the geriatric nurse, with and without system’s support, did not significantly differ from those provided by the automated system (p < 0.05), demonstrating the effectiveness and reliability of both manual and automated methods. The system-supported manual QA process reduced scoring time by approximately two-thirds, from an average of 17.3 min per patient manually to about 5.9 min with the system’s assistance, highlighting the system’s efficiency potential in clinical practice. ConclusionThe QA system based on QATP, produces scores consistent with an experienced nurse’s assessment for complex care over extended periods. It enables quick and accurate quality care evaluation for multiple patients after brief training. Such automated QA systems may empower nursing staff, enabling them to manage more patients, accurately and consistently, while reducing costs due to saved time and effort, and enhanced compliance with evidence-based guidelines.

Design of a bi-directional methodology for automated assessment of compliance to continuous application of clinical guidelines, and its evaluation in the type 2 diabetes domain.

We introduce a new approach for automated guideline-based-care quality assessment, the bidirectional knowledge-based assessment of compliance (BiKBAC) method, and the DiscovErr system, which implements it. Our methodology compares the guideline's Asbru-based formal representation, including its intentions, with the longitudinal medical record, using a top-down and bottom-up approach. Partial matches are resolved using fuzzy temporal logic. The system was evaluated in the type 2 Diabetes management domain, comparing it to three expert clinicians, including two diabetes experts. The system and the experts commented on the management of 10 patients, randomly selected from 2,000 diabetes patients. On average, each record spanned 5.23 years; the data included 1,584 medical transactions. The system provided 279 comments. The experts made 181 different unique comments. The completeness (recall) of the system was 91% when the gold standard was comments made by at least two of the three experts, and 98%, compared to comments made by all three experts. The experts also assessed all of the 114 medication-therapy-related comments, and a random 35% of the 165 tests-and-monitoring-related comments. The system's correctness (precision) was 81%, compared to comments judged as correct by both diabetes experts, and 91%, compared to comments judged as correct by one diabetes expert and at least as partially correct by the other. 89% of the comments were judged as important by both diabetes experts, 8% were judged as important by one expert, and 3% were judged as less important by both experts. Adding the validated system comments to the experts' comments, the completeness scores of the experts were 75%, 60%, and 55%; the expert correctness scores were respectively 99%, 91%, and 88%. Thus, the system could be ranked first in completeness and second in correctness. We conclude that systems such as DiscovErr can effectively assess the quality of continuous guideline-based care.

Complex objective optimization in fuzzy environments

Multi-objective optimization can be used to address possible conflicting relationships between multiple objectives. However, some objectives have a fuzzy temporal relationship between them, making it difficult to give a common method to portray the fuzzy temporal relationship. To fill this gap, we propose the concept of complex objectives, which can be described by fuzzy temporal logic that includes both temporal and logical operators. Furthermore, we investigated the optimal control problems of complex objectives and developed a fuzzy system called possibilistic decision systems (PDSs) to establish a framework for optimal control. In PDSs, states of fuzzy systems are determined by a family of variables, and transitions induced by actions between fuzzy states of systems are also fuzzy uncertain and determined by a possibility degree. Importantly, we proved that memoryless strategies are sufficient for optimal control of complex objectives. Finally, the theory presented in this paper is illustrated by a mobile robot simulation.

A content recommendation system for effective e-learning using embedded feature selection and fuzzy DT based CNN

This paper proposes a new content recommendation system which combines the newly proposed embedded feature selection method and the new Fuzzy Temporal Logic based Decision Tree incorporated Convolutional Neural Network classifier. The newly proposed embedded feature selection called Fuzzy Decision Tree and Weighted Gini-Index based Feature Selection Algorithm (FDTWGI-FSA) that contains the existing incorporated the Fuzzy Decision Tree (FDT) and the Weighted Gini-index based Feature Selection Algorithm (WGIFSA) for getting optimized feature subset. Moreover, an enhanced CNN and Fuzzy Temporal Decision Tree for performing the deep learning process which is able to identify the exact e-content from the huge volume of data with the help of the recommended features by the proposed embedded feature selection method. The exact e-content can be identified after performing the five-layer network structure for extracting the relevant features and it also can be classified by applying the Fuzzy Temporal Decision Tree for the e-learners. Finally, the proposed content recommendation system provides exact content to the e-learners according to their level of understanding and it also satisfies them by providing the exact high level contents. The experiments have been conducted for evaluating the proposed content recommendation system and compared with the existing classifier including the standard CNN.

FTCLogic: Fuzzy Temporal Constraint Logic

In this paper we present FTCLogic, a formal first-order logic that can manage fuzzy temporal constraints between variables efficiently. In this logic, the use of explicit temporal axioms is unnecessary, and therefore the deduction mechanism doesn't slow down for this reason. FTCLogic has an immediate precedent: the Extended Fuzzy Temporal Constraint Logic or EFTCL. However, while EFTCL is based on Timed Possibilistic Logic, FTCLogic uses the Possibilistic Logic to formulate an original semantics according to its syntax. In fact, FTCLogic defines both syntax and semantics from a powerful combination of two formalisms: the Possibilistic Logic and the Fuzzy Temporal Constraints Networks. FTCLogic has provided the basis for the creation of a fuzzy temporal PROLOG: PROLogic, which is implemented through Haskell, and which is currently undergoing evaluation.

Fuzzy temporal logic, flexible methods for interaction analysis

On July 17 2014 in Karlsruhe Germany, Joris IJsselmuiden successfully defended his PhD thesis entitled Interaction analysis in smart work environments through fuzzy temporal logic [1]. The examination committee consisted of Rainer Stiefelhagen, Jurgen Beyerer, Michael Beigl, Dorothea Wagner, Oliver Hummel, and Peter H. Schmitt (Fig. 1). The main publications associated with this PhD thesis are [2-9].

Intuitionistic fuzzy logics as tools for evaluation of Data Mining processes

The Intuitionistic Fuzzy Sets (IFSs), proposed in 1983, are extensions of fuzzy sets. Some years after their introduction, sequentially, intuitionistic fuzzy propositional logic, intuitionistic fuzzy predicate logic, intuitionistic fuzzy modal logic and intuitionistic fuzzy temporal logic have been introduced, presented here shortly. During the last 25years, different intuitionistic fuzzy tools have been used for evaluation of objects from the area of the Artificial Intelligence, as expert systems (having, e.g. facts and rules, with intuitionistic fuzzy degrees of validity and non-validity), decision making processes (having, e.g. intuitionistic fuzzy estimations of the criteria), neural networks, pattern recognitions, metaheuristic algorithms, etc. Short review of these legs of research is offered, with some concrete ideas of possible new directions of study. On this basis, a non-formal discussion is raised on the benefits of applying various elements of intuitionistic fuzzy logics as tools for evaluation of Data Mining processes.

FTL-CFree: A Fuzzy Real-Time Language for Runtime Verification

This paper presents the functional specification language fuzzy temporal logic context free (FTL-CFree), a real runtime language designed to enable industry to model and verify complex scenarios. Such specification requirements demand the flexibility to manage manufacturing event correlations, performance constraints, and timing restrictions, such as holdups and delays. Moreover, this language enriches temporal logic expressiveness with random access to past values and fuzzy evolutionary semantics. Given a specific input trace, this interpretation not only aims to measure the degree of truth of an assertion, but also sets how it will evolve in the future if new trace suffixes are provided. As it is usually the case in runtime verification, the language provides for oracle generation, and so an automatic observable specification can be obtained from high-level requirements. To test the expressiveness and practical usefulness of FTL-CFree, the specifications of two industrial use cases are generated: 1) a batch plant in a glassworks; and 2) a manufacturing execution system.

Social Learners’ Profiles in a Distance Learning System Powered by a Social Network

This work is integrated into the general problem of the research systems of distance learning; and more particularly in the monitoring and the positioning of social profile learners in a distance learning system powered by a social network. In this article, we propose a model multi-entry to determine the learners' profile and positioning his social type. This template allows you to exploit the different traces generated by a learner in the platform and produce indicators on his own profile. Our approach leverages the tools of the fuzzy set theory, the modal and temporal logic. The motivation of this research is to create a tool which helps the tutor to better observe and follow the actions of the learners at the level of the learning platform, and to anticipate potential discouragements or abandonment of the learner.

Fuzzy temporal logic based railway passenger flow forecast model.

Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.

A Rough Set based Fuzzy Inference System for Mining Temporal Medical Databases

The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in medical databases. The lower approximation concepts and fuzzy decision table with the fuzzy features are used to obtain fuzzy decision classes for building the classifier. The goals are pre-processing for feature selection, construction of classifier, and rule induction based on increment rough set approach. The features are selected using Hybrid Genetic Algorithm. Moreover the elementary sets are obtained from lower approximations are categorized into the decision classes. Based on the decision classes a discernibility vector is constructed to define the temporal consistency degree among the objects. Now the Rule Based Classifier is transformed into a temporal rule based fuzzy inference system by incorporating the Allen’s temporal algebra to induce rules. It is proposed to use incremental rough set to update rule induction in dynamic databases. Ultimately these rules are categorized as rules with range values to perform prediction effectively. The efficiency of the approach is compared with other classifiers in order to assess the accuracy of the fuzzy temporal rule based classifier. Experiments have been carried out on the diabetic dataset and the simulation results obtained prove that the proposed temporal rule-based classifier on clinical diabetic dataset stays as an evidence for predicting the severity of the disease and precision in decision support system.

실시간 퍼지 시간논리구조를 이용한 교차로 네트워크의 모델링과 제어

This paper deals with modeling method and application of Fuzzy Discrete Event System(FDES). FDES have characteristics which Crisp Discrete Event System(CDES) can't deals with and is constituted with the events that is determined by vague and uncertain judgement like biomedical or traffic control. We proposed Real-time Fuzzy Temporal Logic Framework(RFTLF) to model Fuzzy Discrete Event System. It combines Temporal Logic Framework with Fuzzy Theory. We represented the model of traffic signal systems for intersection to have the property of Fuzzy Discrete Event System with Real-time Fuzzy Temporal Logic Framework and designed a traffic signal controller for smooth traffic flow. Moreover, we proposed the method to find the minimum-time route to reach the desired destination with information obtained in each intersection. In order to evaluate the performance of Real-time Fuzzy Temporal Logic Framework model proposed in this paper, we simulated unit-time extension traffic signal controller model of the latest signal control method on the same condition.

A sound and complete fuzzy temporal constraint logic

In this work, we define an extended fuzzy temporal constraint logic (EFTCL) based on possibilistic logic. EFTCL allows us to handle fuzzy temporal constraints between temporal variables and, therefore, enables us to express interrelated events through fuzzy temporal constraints. EFTCL is compatible with a theoretical temporal reasoning model: the fuzzy temporal constraint networks (FTCN). The syntax, the semantics and the deduction and refutation theorems for EFTCL are similar to those defined for the sound and noncomplete fuzzy temporal constraint logic (FTCL). In this paper, a resolution principle for performing inferences which take these constraints into account is proposed for EFTCL. Moreover, we prove the soundness and the completeness of the refutation by resolution in EFTCL.

A generalized time quantifier approach to approximate reasoning

In this paper, a rather expressive fuzzy temporal logic for linear time is introduced. First, this logic is a multivalued generalization (Lukasiewicz style) of a two-valued linear-time temporal logic based on, e.g., the “until” operator. Second, it is obtained by introducing a generalized time quantifier (a generalization of the partition operator investigated by Shen) applied to fuzzy time sets. In this fuzzy temporal logic, generalized compositional rules of inference, suitable for approximate reasoning in a temporal setting, are presented as valid formulas. Some medical examples illustrate our approach.

Syntax and Semantics for a Fuzzy Temporal Constraint Logic

In this paper we establish the relationship between the syntax and semantics of a fuzzy temporal constraint logic (FTCL) proposed by Cardenas et al. FTCL enables us to express interrelated events by means of fuzzy temporal constraints. Moreover, it provides a resolution principle for performing inferences which take these constraints into account. FTCL is compatible with the theoretical temporal reasoning model proposed by Marin et al. – the Fuzzy Temporal Constraint Networks (FTCN). The main contributions of this paper are, on the one hand, the proofs of the FTCL-deduction and the FTCL-refutation theorems, and, on the other, the proof of the soundness of the refutation by resolution in this formal system, together with an exhaustive study of its completeness.

Fuzzy temporal constraint logic: a valid resolution principle

In this work we propose a fuzzy temporal constraint logic. First of all, we provide the formal language which will allow the expression of well-formed formulas related to the temporal events by means of temporal constraints. Secondly, we introduce a valid resolution principle in order to solve the queries in this logic. Finally, we will show that this resolution principle is a generalization of the resolution principle proposed for a possibilistic logic with fuzzy predicates (Dubois and Prade, Internat. J. Approx. Reason. 4 (1990) 1–21). All this will serve to reason within a context of the theoretical model of the temporal reasoning proposed by Marı́n and Barro (Fuzzy Temporal Constraint Network, FTCN) (Marı́n et al., Cybernet. Systems 25(2) (1994) 207–215). This model underlies a module for the resolution of temporal queries. This module belongs to a diagnostic and intelligent monitoring system of patients, based on temporal reasoning. The system is applied to the patients admitted in the Intensive Care Units with severe ischemic cardiopathy, submitted to continuous monitoring of the electrical and mechanical signals of the heart. However, what is exposed here in this document is not limited to a field of application in particular, but instead, it is completely general.