Fuzzy Logic in Clinical Practice Decision Support Systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners

Guia de Apoyo a la Decisión en Enfermería Obstetrica: aplicación de la tecnica de la Logica Relativa

Fuzzy Logic has been used as an approach for knowledge representation and a technique for modeling Clinical Decision-Support Systems. In considering such technique underutilization for modeling nursing clinical decisions, this essay aims to present general notions about this technique and through it to develop a theoretical formulation of practice guideline to support decision in amniotomy cases for pregnant women in normal labor.La Logica Relativa hay sido utilisada como una abordage de representación del conocimiento y una tecnica para la modelage de Sistemas de Apoyo a Decisiones Clinicas. Al considerar la baja utilización de esta tecnica para la modelaje de decisiones clinicas de enfermería, esto ensayo objectiva presentar nociones generales sobre esta tecnica e por medio de ella desarrollar uma formulación teórica en forma de guia practico para lo apoyo a la decisión en casos de amniotomya en mujeres embarazadas nulíparas en trabajo de parto normal.A Lógica Fuzzy tem sido utilizada como uma forma de representação de conhecimento e uma técnica para a modelagem de Sistemas de Apoio à Decisões Clínicas. Ao considerar a pouca utilização desta técnica para modelar decisões clínicas de enfermagem, este ensaio objetiva apresentar noções gerais sobre esta técnica e por meio dela desenvolver uma formulação teórica, em forma de guia prático, para o apoio à decisão nos casos de amniotomia em gestantes pimíparas em trabalho de parto normal.UNIFESPUniversidade de Santo Amaro Faculdade de EnfermagemUNIFESP Departamento de EnfermagemUNIFESP, Depto. de EnfermagemSciEL

Expert System Development for the Prevention of Hoof Pathologies Applied to the Intensive Swine Production

Claw lameness can be associated with biomechanical factors caused by imbalances of the pressure distribution under the hooves when swine are confined in modern facilities with hard concrete flooring. Comparing hoof pressure distribution data of swine boars walking over two different types of floors (standard concrete vs. 3mm rubber mattress) in previous research, it was found a great advantage favoring the rubber mat flooring showing that it was capable of reducing pressures under the claws as the pressure became more evenly distributed under this treatment resulting in balanced weight-bearing surfaces. The objective of this study was to develop an expert system based on Fuzzy logic algorithm for the prevention of hoof pathologies applied to the intensive swine production by estimating occurrence of claw lesions based on the association of knowledge gathered on pressure distribution from previous research as well as the influences of nutrition, friction coefficients found on different types of available flooring, hoof sizes and animal weight on the welfare of the swine’s locomotory system. The data were correlated initially using Matlab® platform associating expert’s knowledge and literature through a knowledge system that weights the variables according to their impact on claw health. The final user interface was coded using Microsoft Visual Studio Rapid Application Development tool and the resulting system was validated in several different laboratory scenarios and its performance was considered to be satisfactory according to findings in the literature. The expert system was coded and the authors concluded that the system could be a great contribution and advance in the swine’s industry, nonetheless, its performance still requires field testing for fine adjustments which should be encouraged to be carried out in further researches

Trusting a Humanoid Robot : Exploring Personality and Trusting Effects in a Human-Robot Partnership

Research on trust between humans and machines has primarily investigated factors relating to environmental or system characteristics, largely neglecting individual differences that play an important role in human behavior and cognition. This study examines the role of the Big Five personality traits on trust in a partnership between a human user and a humanoid robot. A wizard of oz methodology was used in an experiment to simulate an artificially intelligent robot that could be leveraged as a partner to complete a life or death survival simulation. Eye-tracking was employed to measure system utilization and validated psychometric instruments were used to measure trust and personality traits. Results suggest that individuals scoring high on the openness personality trait may have greater trust in a humanoid robot partner than those with low scores in the openness personality dimension

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data&mdash;a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented<br /

Esteira eletrônica com velocidade controlada por lógica fuzzy

The aim of this work is to develop an intelligent system to speed control of a treadmill. The intelligent control system minimizes the risks of the user’s cardiac activity, allowing the maximization of the benefits that the physical activity can grant the user of this equipment. The developed intelligent controller is based on fuzzy control techniques, and has a simplified software. Besides, the developed hardware is based on cheaper and simpler electronic circuits, which allows its installation on driver kinds of treadmills existing in the market. The main characteristic of the developed equipment and that a controller adaptable is generated in agreement with the user`s profile, that is, for a group of such characteristics lite age, physical conditioning, index of corporal mass and training area recommended. The system builds a controller fuzzy automatically inside capable of maintaining the user`s heart activity of suitable safety´s strip for the doctors and / or physiotherapists, in agreement with the supplied characteristics. The developed equipment, its hardware and software, is described in full detail and the results of the tests accomplished with several users are compared to simulated values by a software dedicated to industrial control, presenting an inferior discrepancy of 10%. The system also presents way results that guarantee the physical integrity of who made use of the equipment, it doesn't tend, at any time, outdated the value of maximum heart frequency allowed for the user. It is still, maintained the heart frequency in a strip among 60 to 85% of the maximum heart frequency, verifying the controller's efficiency.O trabalho apresenta o desenvolvimento de um sistema inteligente, baseado em lógica fuzzy, o qual controla a velocidade de uma esteira ergométrica com a finalidade de minimizar os riscos da atividade cardíaca do usuário, permitindo ainda maximizar os benefícios que a atividade física pode proporcionar ao usuário deste equipamento. O controlador inteligente desenvolvido foi baseado na técnica de controle fuzzy, possuindo por isso um software simplificado. Além disto o hardware desenvolvido foi baseado em circuitos eletrônicos simples e de baixo custo, o que permite sua instalação nos mais diversos tipos de esteiras existentes no mercado. A contribuição técnico cientifica do equipamento desenvolvido é um controlador adaptativo que é gerado de acordo com o perfil do usuário, isto é, para um conjunto de características tais como: idade, condicionamento físico, índice de massa corporal e zona de treinamento desejada. O sistema constrói automaticamente um controlador fuzzy capaz de manter a atividade cardíaca do usuário dentro da faixa de segurança indicada pelos médicos e / ou fisioterapeutas, de acordo com as características fornecidas. O equipamento desenvolvido, hardware e software, são descritos detalhadamente e os resultados dos testes realizados com diversos usuários são comparados a valores simulados por um software dedicado a controle industrial, apresentando uma discrepância inferior a 10%. O sistema também apresentou resultados de maneira a garantir a integridade física de quem fez uso do equipamento, não tendo, em momento algum, ultrapassado o valor de freqüência cardíaca máxima permitida para o usuário. E ainda, manteve a freqüência cardíaca numa faixa entre 60 a 85% da freqüência cardíaca máxima, constatando a eficiência do controlador

Heuristic Health Resource Referral (H2R2) Engine

Searching for health resources is difficult for many individuals because it requires domain knowledge and understanding of search engines techniques. Our system proposes a paradigm shift whereby users provide as much or as little information as they feel comfortable, and we endeavor to match them with relevant health resources. The system first attempts to identify risk factors through a heuristic engine that employs fuzzy logic and then searches for health resources based on the user’s profile. We aim to unburden the user from having to understand complex health information and sometimes esoteric search techniques. Our preliminary findings show that a fuzzy-based rule engine has utility for determining alcohol suggested care and finding health resources for both alcohol and cigarette dependencies

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis

Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness

2011 - 2012The research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to adapt interfaces, tailor the set of application-relevant data, increase the precision of information retrieval, discover services, make the user interaction implicit, or build smart environments. Despite being slightly different, Situation and Context Awareness involve common problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches to knowledge representation (i.e. contexts, concepts, relations, situations, etc.) and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated and distributed systems, with considerable computing power, to support the reasoning on a huge quantity of knowledge, extracted by sensor data. So, the thesis researches new approaches for distributed Context and Situation Awareness and proposes to apply them in order to achieve some related research objectives such as knowledge representation, semantic reasoning, pattern recognition and information retrieval. The research work starts from the study and analysis of state of art in terms of techniques, technologies, tools and systems to support Context/Situation Awareness. The main aim is to develop a new contribution in this field by integrating techniques deriving from the fields of Semantic Web, Soft Computing and Computational Intelligence. From an architectural point of view, several frameworks are going to be defined according to the multi-agent paradigm. Furthermore, some preliminary experimental results have been obtained in some application domains such as Airport Security, Traffic Management, Smart Grids and Healthcare. Finally, future challenges is going to the following directions: Semantic Modeling of Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other Application Domains and More Experiments. [edited by author]XI n.s