A system of serial computation for classified rules prediction in non-regular ontology trees

Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters

Context-aware system applied in industrial assembly environment

The objective of this paper is to present an ongoing development of a context-aware system used within industrial environments. The core of the system is so-called Cognitive Model for Robot Group Control. This model is based on well-known concepts of Ubiquitous Computing, and is used to control robot behaviours in specially designed industrial environments. By using sensors integrated within the environment, the system is able to track and analyse changes, and update its informational buffer appropriately. Based on freshly collected information, the Model is able to provide a transformation of high-level contextual information to lower-level information that is much more suitable and understandable for technical systems. The Model uses semantically defined knowledge to define domain of interest, and Bayesian Network reasoning to deal with the uncertain events and ambiguity scenarios that characterize our naturally unstructured world

Clinical evidence framework for Bayesian networks

There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care

Um modelo para suporte ao raciocínio diagnóstico diante da dinâmica do conhecimento sobre incertezas

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2013A Engenharia do Conhecimento recorre a abordagens transdisciplinares objetivando oferecer soluções às demandas sociais, destacando-se, artefatos para suporte à decisão. A tomada de decisão humana pode ser de magnitude tão complexa que a atividade intensiva em conhecimento realizada pelo especialista demande assistência proveniente de modelos elaborados por uma visão sistêmica do engenheiro do conhecimento no espaço da solução. O problema desta pesquisa emerge da atividade do especialista médico em Classificação de Risco Metabólico em crianças e adolescentes. As variáveis deste cenário e o processo de classificação apresentam incertezas, manifestadas por causalidade e imprecisão. Redes Bayesianas são empregadas no suporte a classificação cujas variáveis que representam o conhecimento são de natureza probabilística. Contudo, o método bayesiano clássico, diante do fator imprecisão, pode convergir para resultados não qualificados em conformidade àqueles obtidos pelo raciocínio clínico. Por outro lado, Redes Fuzzy-Bayesianas aprimoraram o modelo clássico para suportar inferência sobre conceitos ambíguos. Esta pesquisa contribuiu com o desenvolvimento de um modelo de inferência fuzzy-bayesiano para variáveis não-dicotômicas oferecendo suporte ao raciocínio médico num cenário complexo cuja dinâmica da imprecisão é caracterizada por um tipo de superposição conceitual. Essencialmente dispõe de formalismo matemático modificando a equação do Teorema de Bayes, introduzindo qualificadores difusos para lidar com a imprecisão. Para verificar o modelo utilizou-se de simulações aplicadas sobre dados reais de prontuários. Os resultados obtidos mostraram-se convergentes com a interpretação do especialista e a característica notável foi à qualidade destes resultados nos intervalos próximos aos pontos de corte utilizados pelos especialistas e reproduzidos pelo método bayesiano clássico, problema este que não releva a imprecisão. O modelo distribuiu as probabilidades das hipóteses diagnósticas acompanhando a dinâmica inerente a imprecisão das evidências. Este efeito mostra que um paciente, mesmo que de modo gradual, pode estar evoluindo para um cenário de risco metabólico. O modelo é propenso de ser acoplado a metodologias da Engenharia do Conhecimento e sua implementação pode gerar uma ferramenta aliada à prática do diagnóstico clínico. Abstract : The Knowledge Engineering uses transdisciplinary approaches aiming to provide solutions to social demands, especially, artifacts for decision support. The human decision making can be so complex that the magnitude knowledge intensive activity undertaken by specialist demande assistance from models developed by a systemic view of the knowledge engineer in the solution space. The problem of this research emerges from the activity of the specialist physician in Metabolic Risk Rating in children and adolescents. The variables of this scenario and the classification process is uncertain, expressed by causality and imprecision. Bayesian networks are employed to support the classification whose variables representing knowledge are probabilistic in nature. However, the classical Bayesian method, given the uncertainty factor can converge to results unskilled in accordance to those obtained by clinical reasoning. On the other hand, improved Bayesian Networks Fuzzy-classical model to support inference about ambiguous concepts. This research contributed to the development of a fuzzy-Bayesian inference for non-dichotomous variables supporting the medical reasoning in a complex scenario whose dynamics of vagueness is characterized by a kind of conceptual overlay. Essentially offers mathematical formalism modifying the equation of Bayes Theorem, introducing fuzzy qualifiers to deal with imprecision. To verify the model we used simulations applied to real data from medical records. The results obtained were convergent with interpretation specialist and notable feature was the quality of these results in the ranges near the cutoff points used by experts and reproduced by classical Bayesian method, a problem that does not excuse the inaccuracy. The distributed model the odds of diagnostic hypotheses tracking the dynamics inherent imprecision of the evidence. This effect shows that a patient, even if gradually, may be evolving into a scenario of metabolic risk. The model is likely to be coupled to the Knowledge Engineering methodologies and their implementation can generate a tool coupled with the practice of clinical diagnosis

An Ontology-based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines

Abstract. Clinical Practice Guidelines (CPGs) play an important role in improving the quality of care and patient outcomes. Although several machine-readable representations of practice guidelines implemented with semantic web technologies have been presented, there is no implementation to represent uncertainty with respect to activity graphs in clinical practice guidelines. In this paper, we are exploring a Bayesian Network(BN) approach for representing the uncertainty in CPGs based on ontologies. Based on the representation of uncertainty in CPGs, when an activity occurs, we can evaluate its effect on the whole clinical process, which, in turn, can help doctors judge the risk of uncertainty for other activities, and make a decision. A variable elimination algorithm is applied to implement the BN inference and a validation of an aspirin therapy scenario for diabetic patients is proposed.

Self-adaptation via concurrent multi-action evaluation for unknown context

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project. Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach. The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains. The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason. The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered. In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques

Semantically aware hierarchical Bayesian network model for knowledge discovery in data : an ontology-based framework

Several mining algorithms have been invented over the course of recent decades. However, many of the invented algorithms are confined to generating frequent patterns and do not illustrate how to act upon them. Hence, many researchers have argued that existing mining algorithms have some limitations with respect to performance and workability. Quantity and quality are the main limitations of the existing mining algorithms. While quantity states that the generated patterns are abundant, quality indicates that they cannot be integrated into the business domain seamlessly. Consequently, recent research has suggested that the limitations of the existing mining algorithms are the result of treating the mining process as an isolated and autonomous data-driven trial-and-error process and ignoring the domain knowledge. Accordingly, the integration of domain knowledge into the mining process has become the goal of recent data mining algorithms. Domain knowledge can be represented using various techniques. However, recent research has stated that ontology is the natural way to represent knowledge for data mining use. The structural nature of ontology makes it a very strong candidate for integrating domain knowledge with data mining algorithms. It has been claimed that ontology can play the following roles in the data mining process: •Bridging the semantic gap. •Providing prior knowledge and constraints. •Formally representing the DM results. Despite the fact that a variety of research has used ontology to enrich different tasks in the data mining process, recent research has revealed that the process of developing a framework that systematically consolidates ontology and the mining algorithms in an intelligent mining environment has not been realised. Hence, this thesis proposes an automatic, systematic and flexible framework that integrates the Hierarchical Bayesian Network (HBN) and domain ontology. The ultimate aim of this thesis is to propose a data mining framework that implicitly caters for the underpinning domain knowledge and eventually leads to a more intelligent and accurate mining process. To a certain extent the proposed mining model will simulate the cognitive system in the human being. The similarity between ontology, the Bayesian Network (BN) and bioinformatics applications establishes a strong connection between these research disciplines. This similarity can be summarised in the following points: •Both ontology and BN have a graphical-based structure. •Biomedical applications are known for their uncertainty. Likewise, BN is a powerful tool for reasoning under uncertainty. •The medical data involved in biomedical applications is comprehensive and ontology is the right model for representing comprehensive data. Hence, the proposed ontology-based Semantically Aware Hierarchical Bayesian Network (SAHBN) is applied to eight biomedical data sets in the field of predicting the effect of the DNA repair gene in the human ageing process and the identification of hub protein. Consequently, the performance of SAHBN was compared with existing Bayesian-based classification algorithms. Overall, SAHBN demonstrated a very competitive performance. The contribution of this thesis can be summarised in the following points. •Proposed an automatic, systematic and flexible framework to integrate ontology and the HBN. Based on the literature review, and to the best of our knowledge, no such framework has been proposed previously. •The complexity of learning HBN structure from observed data is significant. Hence, the proposed SAHBN model utilized the domain knowledge in the form of ontology to overcome this challenge. •The proposed SAHBN model preserves the advantages of both ontology and Bayesian theory. It integrates the concept of Bayesian uncertainty with the deterministic nature of ontology without extending ontology structure and adding probability-specific properties that violate the ontology standard structure. •The proposed SAHBN utilized the domain knowledge in the form of ontology to define the semantic relationships between the attributes involved in the mining process, guides the HBN structure construction procedure, checks the consistency of the training data set and facilitates the calculation of the associated conditional probability tables (CPTs). •The proposed SAHBN model lay out a solid foundation to integrate other semantic relations such as equivalent, disjoint, intersection and union

Bayesian Networks for Evidence Based Clinical Decision Support.

PhDEvidence based medicine (EBM) is defined as the use of best available evidence for decision making, and it has been the predominant paradigm in clinical decision making for the last 20 years. EBM requires evidence from multiple sources to be combined, as published results may not be directly applicable to individual patients. For example, randomised controlled trials (RCT) often exclude patients with comorbidities, so a clinician has to combine the results of the RCT with evidence about comorbidities using his clinical knowledge of how disease, treatment and comorbidities interact with each other. Bayesian networks (BN) are well suited for assisting clinicians making evidence-based decisions as they can combine knowledge, data and other sources of evidence. The graphical structure of BN is suitable for representing knowledge about the mechanisms linking diseases, treatments and comorbidities and the strength of relations in this structure can be learned from data and published results. However, there is still a lack of techniques that systematically use knowledge, data and published results together to build BNs. This thesis advances techniques for using knowledge, data and published results to develop and refine BNs for assisting clinical decision-making. In particular, the thesis presents four novel contributions. First, it proposes a method of combining knowledge and data to build BNs that reason in a way that is consistent with knowledge and data by allowing the BN model to include variables that cannot be measured directly. Second, it proposes techniques to build BNs that provide decision support by combining the evidence from meta-analysis of published studies with clinical knowledge and data. Third, it presents an evidence framework that supplements clinical BNs by representing the description and source of medical evidence supporting each element of a BN. Fourth, it proposes a knowledge engineering method for abstracting a BN structure by showing how each abstraction operation changes knowledge encoded in the structure. These novel techniques are illustrated by a clinical case-study in trauma-care. The aim of the case-study is to provide decision support in treatment of mangled extremities by using clinical expertise, data and published evidence about the subject. The case study is done in collaboration with the trauma unit of the Royal London Hospital