Modelagem do gerenciamento de doenças cronicas ou de longa duração em ST-guide

Orientador: Jacques WainerTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaResumo: o gerenciamento de um paciente com uma doença crônica ou de longa duração é um processo que envolve a coleta e a interpretação de quantidades consideráveis de dados ao longo do tempo, a tomada de decisões em função dessa interpretação e do conhecimento disponível, a aplicação de planos de diagnóstico ou terapêuticos de maneira periódica e a revisão desses planos quando for necessário. Considerando a complexidade do gerenciamento de um paciente e sua importância humana e econômica, sistemas que possam auxiliar este trabalho podem ajudar esses profissionais a realizar seu trabalho de uma forma mais eficiente e econômica. Nós desenvolvemos um formalismo, chamado ST-guide, que permite construir sistemas baseados em conhecimento para ajudar os profissionais de saúde no gerenciamento de pacientes com doenças crônicas ou de longa duração. Este formalismo leva em consideração as características próprias do gerenciamento dessas doenças e sua maior vantagem é que essas características podem ser traduzidos diretamente nas primitivas fornecidas por ST-guide. Dessa forma, ST-guide permite uma abordagem mais intuitiva tanto para os encarregados de representar o conhecimento referente ao gerenciamento de uma doença quanto para os usuários do sistema associadoAbstract: The management of a patient with a chronic or long term disease is a process that involves gathering and interpreting a great amount of data along the treatment time, taking decisions based on these interpretations and on the available knowledge, applying diagnosis and therapeutics plans periodically and revising these plans when necessary. Taking into account the complexity of the patient's management an its human and economic importance, systems that assist this job may help these professionals to do their work in a more efficient and economical way. We have developed a formalism, called ST-guide, that allows the construction of knowledge-based systems to assist health professionals in the management of patients with chronic or long term diseases. This formalism takes into account the characteristics of the management o these diseases and its main advantage is that these characteristics can be directly translated into the primitives provided by ST-guide .In this way, ST-guide allows a more intuitive approach for the persons in charge of representing the management knowledge of a disease as well as for the users of the associated systemDoutoradoDoutor em Ciência da Computaçã

Tackling Dierent Business Process Perspectives

Business Process Management (BPM) has emerged as a discipline to design, control, analyze, and optimize business operations. Conceptual models lie at the core of BPM. In particular, business process models have been taken up by organizations as a means to describe the main activities that are performed to achieve a specific business goal. Process models generally cover different perspectives that underlie separate yet interrelated representations for analyzing and presenting process information. Being primarily driven by process improvement objectives, traditional business process modeling languages focus on capturing the control flow perspective of business processes, that is, the temporal and logical coordination of activities. Such approaches are usually characterized as \u201cactivity-centric\u201d. Nowadays, activity-centric process modeling languages, such as the Business Process Model and Notation (BPMN) standard, are still the most used in practice and benefit from industrial tool support. Nevertheless, evidence shows that such process modeling languages still lack of support for modeling non-control-flow perspectives, such as the temporal, informational, and decision perspectives, among others. This thesis centres on the BPMN standard and addresses the modeling the temporal, informational, and decision perspectives of process models, with particular attention to processes enacted in healthcare domains. Despite being partially interrelated, the main contributions of this thesis may be partitioned according to the modeling perspective they concern. The temporal perspective deals with the specification, management, and formal verification of temporal constraints. In this thesis, we address the specification and run-time management of temporal constraints in BPMN, by taking advantage of process modularity and of event handling mechanisms included in the standard. Then, we propose three different mappings from BPMN to formal models, to validate the behavior of the proposed process models and to check whether they are dynamically controllable. The informational perspective represents the information entities consumed, produced or manipulated by a process. This thesis focuses on the conceptual connection between processes and data, borrowing concepts from the database domain to enable the representation of which part of a database schema is accessed by a certain process activity. This novel conceptual view is then employed to detect potential data inconsistencies arising when the same data are accessed erroneously by different process activities. The decision perspective encompasses the modeling of the decision-making related to a process, considering where decisions are made in the process and how decision outcomes affect process execution. In this thesis, we investigate the use of the Decision Model and Notation (DMN) standard in conjunction with BPMN starting from a pattern-based approach to ease the derivation of DMN decision models from the data represented in BPMN processes. Besides, we propose a methodology that focuses on the integrated use of BPMN and DMN for modeling decision-intensive care pathways in a real-world application domain

Use of Decision Tables to Model Assistance Knowledge to Train Medical Residents

En aquesta tesi es presenta un model de coneixement clínic basat en taules de decisió que permet representar les fases de diagnòstic, tractament i pronòstic de diferents malalties. Les taules de decisió que s'obtenen per a cada fase del model han estat utilitzades per representar malalties reals a partir de guies de pràctica clínica. En el cas del diagnòstic s'han representat les vuit causes secundàries més comuns de la hipertensió arterial. En el cas del tractament i pronòstic s'han representat set diferents xocs en emergències. Les taules de decisió que hem obtingut per a cadascuna de les malalties s'han utilitzat com a base per crear dues eines d'entrenament mèdic, dirigides a residents. Totes dues eines s'han provat a l'Hospital Clínic de Barcelona amb diferents grups de residents. Després de les proves s'ha conclòs que les taules de decisió són adequades per a la representació del coneixement mèdic en totes tres fases. A més, les eines d'aprenentatge han estat efectives a l'hora d'ensenyar els procediments mèdics, especialment als residents amb menys experiència prèvia.En esta tesis se presenta un modelo de conocimiento clínico basado en tablas de decisión que permite representar las fases de diagnostico, tratamiento y pronostico de distintas enfermedades. Las tablas de decisión que se obtienen para cada fase del modelo han sido utilizadas para representar enfermedades reales a partir de guías de práctica clínica. En el caso del diagnóstico se han representado las ocho causas secundarias más comunes de la hipertensión arterial. En el caso del tratamiento y pronóstico se han representado siete diferentes shocks en emergencias. Las tablas de decisión que hemos obtenido para cada una de las enfermedades se han usado como base para crear dos herramientas de entrenamiento médico, dirigido a residentes. Ambas herramientas se han probado en el Hospital Clínic de Barcelona con distintos grupos de residentes. Tras las pruebas se ha concluido que las tablas de decisión son adecuadas para la representación del conocimiento medico en las tres fases. Además, las herramientas de aprendizaje han sido efectivas a la hora de enseñar los procedimientos médicos, en especial a los residentes con menos experiencia previa.In this thesis a clinical knowledge model based on decision tables is presented. This model allows us to represent the stages of diagnosis, treatment, and prognosis of different diseases. The decision tables obtained for each phase of the model have been used to represent real diseases from clinical practice guidelines. In the case of diagnosis, we represented eight of the most common secondary causes of hypertension. For the treatment and prognosis we represented seven different emergency shocks. The decision tables obtained for each disease have been used as the basis for two medical training tools aimed to residents. Both tools have been tested in the Hospital Clínic de Barcelona with different groups of residents. After testing, it was concluded that decision tables are suitable for the representation of medical knowledge in all three phases. In addition, the learning tools have been effective in teaching medical procedures, especially for untrained residents

An online belief rule-based group clinical decision support system

Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.EThOS - Electronic Theses Online ServiceManchester Business SchoolGBUnited Kingdo