Moving towards a new paradigm of creation, dissemination, and application of computer-interpretable medical knowledge

Computer-Interpretable Guidelines (CIGs) exploit the scientific strength of evidence-based medicine to make recommendations available in Clinical Decision Support Systems. However, systems that deploy them have not been widely successful, in part due to the limitations of CIG frameworks in the adoption of inclusive and open technologies and the use of Artificial Intelligence techniques as tools to make their systems stronger and more adaptable. In this work we propose a web-based CIG framework to tackle some of these challenges and facilitate the integration of CIG-based advice not only in the everyday activities of health care professionals but also in the lives of whoever may need it.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported by a FCT grant with the reference SFRH/BD/8- 5291/ 2012.info:eu-repo/semantics/publishedVersio

Representation of clinical practice guideline components in OWL

Serie : Advances in intelligent systems and computing, ISSN 2194-5357, vol. 221The main purpose to attain with the advent of clinical decision sup-port systems is either to improve the quality of patient care or to reduce the oc-currence of clinical malpractice, such as medical errors and defensive medicine. It is therefore necessary a machine-readable support to integrate the recommen-dations of Clinical Practice Guidelines in such systems. CompGuide is a Com-puter-Interpretable Guideline model developed under Ontology Web Language that offers support for administrative information concerning a guideline, work-flow procedures, and the definition of clinical and temporal constraints. When compared to other models of the same type, besides having a comprehensive task network model, it introduces new temporal representations and the possi-bility of reusing pre-existing knowledge and integrating it in a guideline.(undefined

Guias clínicas : representação e raciocínio

Dissertação de integrado em Engenharia BiomédicaOs ambientes de cuidados de saúde são extremamente exigentes para os profissionais. Nestes ambientes há uma grande exposição a situações de tensão, que se repercutem na qualidade da prática clínica. O stress ocupacional origina situações de erro médico, de variações indesejadas na prática clínica e de medicina defensiva. As Guias Clínicas (GCs), como recomendações clínicas baseadas em investigação científica sólida, podem solucionar tais problemas, fornecendo um suporte para a prática da medicina, baseado na evidência, e preenchendo eventuais vazios de conhecimento dos profissionais de saúde. Contudo, o seu formato actual não responde às exigências de um processo clínico que obriga a tomar decisões rápidas, com segurança. A solução pode passar pela implementação de formatos informáticos de GCs, as chamadas Guias Interpretáveis por Computador (Computer-Interpretable Guidelines - CIGs), em sistemas de apoio à decisão. As abordagens actuais de CIGs concentram-se sobretudo em aspectos relacionados com a modelação de tarefas, restrições temporais à execução de guiase integrações com sistemas de informação locais. No entanto, não providenciam um tratamento de casos de Informação Imperfeita, que são comuns nos processos clínicos. Há necessidade de uma representação de guias, que combine a capacidade de modelação de tarefas das abordagens actuais de CIGs, com linguagens de programação que permitam expressar casos de Informação Imperfeita e métodos que permitam quantificá-los. Para o efeito, recolheram-se as principais características das abordagens de CIGs actuais e propôs-se um modelo, utilizando a Extensão à Programação em Lógica (EPL) e o método da Qualidade da Informação (Quality of Information - QoI). A aplicabilidade deste modelo foi estudada através de um caso de estudo com uma GC para detecção e tratamento de elevados níveis de colesterol. Concluiu-se que, embora careça de melhoramentos ao nível de um suporte para o estado do paciente e ao nível da estruturação da informação, o modelo apresenta potencial para melhorar os resultados do processo clínico.Healthcare environments are very demanding. In these environments healthcare professionals are exposed to many stressful situations that affect negatively the quality of clinical practice. Occupational stress is among the causes of medical errors, undesirable variations in clinical practice and defensive medicine. The use of Clinical Guidelines may be a solution to these issues. They are evidence based recommendations that support good clinical practice and may compensate for knowledge gaps of healthcare professionals. However, their current format does not meet the requirements of real time decision support in the clinical process. Implementing Computer-Interpretable Guidelines (CIGs) in clinical decision support systems shows promises of both changing the process of healthcare delivery and improving its outcomes. The existing CIG approaches focus mainly on task modeling, temporal constraints to the execution of guidelines and integration with local information systems. Yet, they fail to address the issue of Imperfect Information, which is common in many clinical cases. There is a need for a guideline representation, which combines the task modeling capabilities of the existing CIG approaches with programing languages that enable the expression of cases of Imperfect Information and methods to quantify them. For this purpose we collected the main features of the existing approaches and proposed a model that uses the Extension to Logic Programming (ELP) and the method of Quality of Information. The applicability of this model was studied with a guideline for detection and treatment of elevated levels of cholesterol. Although the model needs improvements in the support for the patient state and the structuring of information, it has the potential to improve clinical results

Clinical decision support: Knowledge representation and uncertainty management

Programa Doutoral em Engenharia BiomédicaDecision-making in clinical practice is faced with many challenges due to the inherent risks of being a health care professional. From medical error to undesired variations in clinical practice, the mitigation of these issues seems to be tightly connected to the adherence to Clinical Practice Guidelines as evidence-based recommendations The deployment of Clinical Practice Guidelines in computational systems for clinical decision support has the potential to positively impact health care. However, current approaches to Computer-Interpretable Guidelines evidence a set of issues that leave them wanting. These issues are related with the lack of expressiveness of their underlying models, the complexity of knowledge acquisition with their tools, the absence of support to the clinical decision making process, and the style of communication of Clinical Decision Support Systems implementing Computer-Interpretable Guidelines. Such issues pose as obstacles that prevent these systems from showing properties like modularity, flexibility, adaptability, and interactivity. All these properties reflect the concept of living guidelines. The purpose of this doctoral thesis is, thus, to provide a framework that enables the expression of these properties. The modularity property is conferred by the ontological definition of Computer-Interpretable Guidelines and the assistance in guideline acquisition provided by an editing tool, allowing for the management of multiple knowledge patterns that can be reused. Flexibility is provided by the representation primitives defined in the ontology, meaning that the model is adjustable to guidelines from different categories and specialities. On to adaptability, this property is conferred by mechanisms of Speculative Computation, which allow the Decision Support System to not only reason with incomplete information but to adapt to changes of state, such as suddenly knowing the missing information. The solution proposed for interactivity consists in embedding Computer-Interpretable Guideline advice directly into the daily life of health care professionals and provide a set of reminders and notifications that help them to keep track of their tasks and responsibilities. All these solutions make the CompGuide framework for the expression of Clinical Decision Support Systems based on Computer-Interpretable Guidelines.A tomada de decisão na prática clínica enfrenta inúmeros desafios devido aos riscos inerentes a ser um profissional de saúde. Desde o erro medico até às variações indesejadas da prática clínica, a atenuação destes problemas parece estar intimamente ligada à adesão a Protocolos Clínicos, uma vez que estes são recomendações baseadas na evidencia. A operacionalização de Protocolos Clínicos em sistemas computacionais para apoio à decisão clínica apresenta o potencial de ter um impacto positivo nos cuidados de saúde. Contudo, as abordagens atuais a Protocolos Clínicos Interpretáveis por Maquinas evidenciam um conjunto de problemas que as deixa a desejar. Estes problemas estão relacionados com a falta de expressividade dos modelos que lhes estão subjacentes, a complexidade da aquisição de conhecimento utilizando as suas ferramentas, a ausência de suporte ao processo de decisão clínica e o estilo de comunicação dos Sistemas de Apoio à Decisão Clínica que implementam Protocolos Clínicos Interpretáveis por Maquinas. Tais problemas constituem obstáculos que impedem estes sistemas de apresentarem propriedades como modularidade, flexibilidade, adaptabilidade e interatividade. Todas estas propriedades refletem o conceito de living guidelines. O propósito desta tese de doutoramento é, portanto, o de fornecer uma estrutura que possibilite a expressão destas propriedades. A modularidade é conferida pela definição ontológica dos Protocolos Clínicos Interpretáveis por Maquinas e pela assistência na aquisição de protocolos fornecida por uma ferramenta de edição, permitindo assim a gestão de múltiplos padrões de conhecimento que podem ser reutilizados. A flexibilidade é atribuída pelas primitivas de representação definidas na ontologia, o que significa que o modelo é ajustável a protocolos de diferentes categorias e especialidades. Quanto à adaptabilidade, esta é conferida por mecanismos de Computação Especulativa que permitem ao Sistema de Apoio à Decisão não só raciocinar com informação incompleta, mas também adaptar-se a mudanças de estado, como subitamente tomar conhecimento da informação em falta. A solução proposta para a interatividade consiste em incorporar as recomendações dos Protocolos Clínicos Interpretáveis por Maquinas diretamente no dia a dia dos profissionais de saúde e fornecer um conjunto de lembretes e notificações que os auxiliam a rastrear as suas tarefas e responsabilidades. Todas estas soluções constituem a estrutura CompGuide para a expressão de Sistemas de Apoio à Decisão Clínica baseados em Protocolos Clínicos Interpretáveis por Máquinas.The work of the PhD candidate Tiago José Martins Oliveira is supported by a grant from FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) with the reference SFRH/BD/85291/ 2012

A comprehensive clinical guideline model and a reasoning mechanism for AAL systems

The progressive ageing of population combined with the need for comfort in situations of disease and disability are pushing healthcare organizations and governments to find new solutions to enable people to live longer in their preferred environment, while having access to quality healthcare services. iGenda is an Ambient Assisted Living platform that provides constant monitoring to people with this type of needs. The use of a Computer-Interpretable Guideline model for decision making is one of the features of this platform. The model used to represent Clinical Practice Guidelines gathers a set of features that make guidelines more dynamic and easily implementable. The model is defined using Ontology Web Language, profiting from the existing constructors provided by this language. It is based on a set of primitive tasks, namely Plans, Actions, Questions and Decisions. Focusing on decision support, a method for dealing with incomplete information about the clinical parameters of a health record is presented. The objective is to keep a continuous flow of information through the decision process and assuring that an outcome is always achieved. The usefulness of the integration of guideline recommendations with a reason mechanism capable of handling incomplete information is demonstrated through a case study about the diagnosis of metabolic syndrome.(undefined

Speculative Computation with constraint processing for the generation of clinical scenarios

Clinical decision making often involves making decisions in situations of uncertainty. Clinical Decision Support Systems are tools devised to help in such moments, but the information may not be available during the decision process. Be it because of communication failure or errors in data input, the truth is that it would be beneficial to present the most likely clinical scenarios to a physician, given the incompleteness of the information. Speculative Computation offers a way to structure such a scenario generation process. This work presents a framework for clinical decision support with disjunctive constraint processing that acts as an interface with computer-interpretable versions of Clinical Practice Guidelines. Being a reasoning process based on defaults, it has to rely on a default generation process. For that we propose Bayesian Networks. The interaction between the different components of the system resulted in a process capable of generating clinical scenarios.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT ( Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project PEst-OE/EEI/UI0752/2014. The work of Tiago Oliveira is supported by a doctoral grant by FCT (SFRH/BD/85291/2012)

Orientation system based on speculative computation and trajectory mining

Assistive technologies help users with disabilities (physical, sensory, intellectual) to perform tasks that were difficult or impossible to execute. Thus, the user autonomy is increased through this technology. Although some adaptation of the user might be needed, the effort should be minimum in order to use devices that convey assistive functionalities. In cognitive disabilities a common diminished capacity is orientation, which is crucial for the autonomy of an individual. There are several research works that tackle this problem, however they are essentially concerned with user guidance and application interface (display of information). The work presented herein aims to overcome these systems through a framework of Speculative Computation, which adds a prediction feature for the next move of the user. With an anticipation feature and a trajectory mining module the user is guided through a preferred path receiving anticipated alerts before a possible shift in the wrong direction.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundaçãao para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of João Ramos is supported by a doctoral the FCT grant SFRH/BD/89530/2012. The work of Tiago Oliveira is also supported by the FCT grant with the reference SFRH/BD/85291/2012info:eu-repo/semantics/publishedVersio

Clinical careflows aided by uncertainty representation models

Serie : Lecture notes in computer science, ISSN 0302-9743, vol. 8073Choosing an appropriate support for Clinical Decision Support Systems is a complicated task, and dependent on the domain in which the system will intervene. The development of wide solutions, which are transversal to different clinical specialties, is impaired by the existence of complex decision moments that reflect the uncertainty and imprecision that are often present in these processes. The need for solutions that combine the relational nature of declarative knowledge with other models, capable of handling that uncertainty, is a necessity that current systems may be faced with. Following this line of thought, this work introduces an ontology for the representation of Clinical Practice Guidelines, with a case-study regarding colorectal cancer. It also presents two models, one based on Bayesian Networks, and another one on Artificial Neural Networks, for colorectal cancer prognosis. The objective is to observe how well these two ways of obtaining and representing knowledge are complementary, and how the machine learning models perform, attending to the available information.This work is funded by National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011. The work of Tiago Oliveira is supported by a doctoral grant by FCT (SFRH/BD/85291/2012)

Studying the effects of stress on negotiation behaviour

"Special issue : Computational approaches for conflict resolution in decision making : new advances and developments"Negotiation is a collaborative activity that requires the participation of different parties whose behaviors influence the outcome of the whole process. The work presented here focuses on the identification of such behaviors and their impact on the negotiation process. The premise for this study is that identifying and cataloging the behavior of parties during a negotiation may help to clarify the role that stress plays in the process. To do so, an experiment based on a negotiation game was implemented. During this experiment, behavioral and contextual information about participants was acquired. The data from this negotiation game were analyzed in order to identify the conflict styles used by each party and to extract behavioral patterns from the interactions, useful for the development of plans and suggestions for the associated participants. The work highlights the importance of the knowledge about social interactions as a basis for informed decision support in situations of conflict.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012). The work of Tiago Oliveira is supported by doctoral grant by FCT (SFRH/BD/85291/2012)