OWL-based acquisition and editing of computer-interpretable guidelines with the CompGuide editor

Computer-Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence-based nature of their source material. Therefore, these machine-readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine-readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition.COMPETE, Grant/Award Number: POCI-01-0145-FEDER-007043; FCT - Fundacao para a Ciencia e Tecnologia, Grant/Award Number: UID/CEC/00319/201

Compguide: Acquisition and editing of computer-interpretable guidelines

The formalization of Clinical Practice Guidelines (CPGs) as Computer-Interpretable Guidelines (CIGs) has the potential to positively influence the behaviour of health practitioners by being available at the point and time of care. Existing tools for acquiring and editing CIGs for automatic interpretation present limitations in their ease of use and the support they offer to a CIG encoder. Besides characterizing these limitations and identifying improvements to include in future tools, this work describes the CompGuide Editor, a Protégé tool for the management of CIGs that guides a user throughout the several steps of CIG encoding, without requiring the user to have programming knowledge, and through the use of interfaces that are simple and intuitive.FCT - Fuel Cell Technologies Program (SFRH/BD/85291/2012)info:eu-repo/semantics/publishedVersio

A unified system for clinical guideline management and execution

There are several approaches to Computer-Interpretable Guidelines (CIG) representation and execution that offer the possibility of acquiring, executing and editing CPGs. Many CIG approaches aim to represent Clinical Practice Guidelines (CPGs) by computationally formalising the knowledge that they enclose, in order to be suitable for the integration in Clinical Decision Support Systems (CDSS). However, the current approaches for this purpose lack in providing a unified workflow for management and execution of CIGs. Besides characterising these limitations and identifying improvements to include in future tools, this work describes the unified architecture for CIG management and execution, a unified approach that allows the CIG acquisition, editing and execution.This work has been supported by COMPETE: POCI-01-0145-FEDER-0070 43 and FCT – Funda ̧c ̃aopara a Ciˆencia e Tecnologia within the Project Scope UID/CEC/ 00319/2013

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

Conflict resolution in clinical treatments

Dissertação de mestrado integrado em Engenharia InformáticaCurrently, in the health area, there is a need for systems that provide support for the decision of health professionals through specific recommendations for each patient based on Clinical Practice Guidelines (CPGs) for automatic interpretation. CPGs are documents that have enormous importance in the daily life of health professionals, playing a key role in reducing variations in medical practice, improving the quality of health care, and reducing health care costs. These documents reflect knowledge about how best to diagnose and treat diseases in the form of a list of clinical recommendations. However, there may be conflicts and interactions in the application of these clinical recommendations, that which in their maximum exponent may impair the patient’s clinical condition. These conflicts are transported to decision support systems, creating the need to develop computational methods to solve these same conflicts. In the case of multimorbid patients, this resolution of conflicts can be very problematic because these patients suffer from several pathologies at the same time, and that the use of a drug for one particular pathology may have a detrimental effect on the application of another drug in another pathology. Therefore, the objective of this dissertation topic is the determination of conflicts and interactions between drugs and the determination of these same alternatives.Atualmente na área da saúde, existe uma necessidade de existirem sistemas que forneçam apoio à decisão dos profissionais de saúde através de recomendações específicas para cada paciente com base em protocolos clínicos para interpretação automática. Os protocolos clínicos são documentos que têm enorme importância no dia-a-dia dos profissionais de saúde, desempenhando um papel fundamental na redução das variações na prática médica, na melhoria da qualidade dos cuidados de saúde e na redução dos custos de saúde. Estes documentos reflectem o conhecimento sobre a melhor forma de diagnosticar e tratar doenças na forma de uma lista de recomendações clínicas. Contudo, podem existir conflitos e interações na aplicação destas recomendações clínicas, que no seu expoente máximo poderão levar a um agravamento do estado clínico do paciente, nomeadamente no caso da aplicação de diferentes fármacos. Estes conflitos são transportados para os sistemas de apoio à decisão, criando a necessidade de desenvolver métodos computacionais de resolução destes mesmos conflitos. No caso dos pacientes multimórbidos esta resolução de conflitos pode ser bastante problemática devido ao facto destes pacientes sofrerem de várias patologias ao mesmo tempo, e que a utilização de um fármaco para uma determinada patologia possa vir a ter um efeito nocivo na aplicação de outro fármaco noutra patologia. Sendo assim, o objetivo deste tema de dissertação é a determinação dos conflitos e interações entre fármacos e a determinação dessas mesmas alternativas

Betere inzet van klinische richtlijnen in het Prinses Máxima Centrum door standaardisatie en formalisatie in computer interpreteerbare richtlijnen:Innovaties voor de LATER-richtlijn Follow-up kinderkanker: Ontwikkeling van een beslissingsondersteunend systeem

Het Prinses Máxima Centrum voor kinderoncologie is een zorg- en onderzoekscentrum waar zorg en research nauw met elkaar verbonden zijn. Alle kinderen in Nederland met (verdenking op) kinderkanker worden sinds 2018 gediagnosticeerd en behandeld in het Prinses Máxima Centrum. Overlevenden van kinderkanker, zogenaamde survivors, worden na hun behandeling nog blijvend gemonitord en bij late effecten behandeld op de LATER-polikliniek.Dagelijks passen zorgverleners (artsen en verpleegkundigen) klinisch redeneren toe bij het leveren van zorg. Daarbij maken ze gebruik van onder andere klinische richtlijnen en (onderzoeks-)protocollen. Deze zijn veelal beschikbaar als natuurlijk tekst, in PDF-formaat en deels verwerkt in software-systemen. De huidige manier waarop richtlijnen in de zorg beschikbaar zijn en worden ingezet kent problemen. Ze zijn vaak niet eenduidig, hebben geen standaard structuur, zijn achterhaald op het moment dat ze in de praktijk worden toegepast, zijn impliciet, niet computer interpreteerbaar, niet interoperabel en onvoldoende effectief.Het Prinses Máxima Centrum heeft als doel om het gebruik van richtlijnen en protocollen in het Máxima te optimaliseren door deze te standaardiseren en formaliseren in computer interpreteerbare richtlijnen (CIRs). Zo kan informatie in de richtlijnen eenvoudiger gevonden worden en kunnen de regels en adviezen uit de richtlijnen verwerkt worden in een beslissingsondersteunend systeem (BOS). Door de richtlijn en bijbehorende logica op een gestandaardiseerde manier vast te leggen kan deze eenvoudiger gedeeld en geïnterpreteerd worden door andere systemen (eenmalige registratie, meervoudig gebruik) en kennis eenvoudiger/ sneller doorgevoerd worden op het moment dat zorg wordt geleverd.In deze thesis wordt een ontwerp gepresenteerd voor de formalisatie van de LATER-richtlijn naar een CIR op basis van openEHR standaarden en de ontwikkeling van een beslissingsondersteunend systeem (BOS) op de LATER-poli van het Prinses Máxima Centrum.De ontwerpopdracht heeft inzicht gegeven in de te nemen stappen, beschikbare formalisatie-talen en tools om te kunnen komen tot een beslissingsondersteunend systeem voor de LATER-poli. Daarbij is aan de hand van een Proof of Concept (PoC) aangetoond dat het mogelijk is om met internationaal geaccepteerde openEHR standaarden schaalbare, semantisch en syntactisch interoperabele computer interpreteerbaar richtlijnen te ontwikkelen, waarmee adviezen gegenereerd kunnen worden voor de individuele survivor op basis van patiëntdata en klinische richtlijnen.De gekozen programmeertaal (Python) en modulaire opbouw van het beslissingsondersteunend systeem (BOS) maken het mogelijk om de software door te ontwikkelen tot een beslissingsondersteunend systeem dat naast de beslisregels, in de toekomst ook gevoed kan worden met machine learning en artificial intelligence algoritmen ten behoeve van betere beslissingsondersteuning.Bij de gekozen standaarden en ontwikkeling van de software is rekening gehouden met een actief, open (source), goed gedocumenteerd ecosysteem, wat de toekomstbestendigheid van het beslissingsondersteunend systeem ten goede komt. Hierdoor is het aannemelijk dat de onderliggende standaarden en talen voor langere tijd zullen blijven bestaan en het eenvoudiger zal zijn ontwikkelaars en beheerders te vinden die óf al kennis / ervaring hebben óf dit op kunnen doen aan de hand van de beschikbare documentatie.De ontwerpopdracht is succesvol afgerond en heeft waardevolle input geleverd voor het Prinses Máxima Centrum en de LATER use case om verder vervolg te kunnen geven aan de ontwikkeling van een beslissingsondersteunend systeem, waarbij de logica van richtlijnen eenmalig geregistreerd en meervoudig gebruikt kan worden. Met het uitgevoerde onderzoek en ontwerp is een belangrijke bijdrage geleverd aan het vakgebied klinische informatica op het vlak van beslissingsondersteuning

Anotação automática de informação clínica

Dissertação de mestrado integrado em Engenharia InformáticaA proximidade entre a Informática e a Saúde é cada vez maior a cada dia que passa. Nos dias que correm é comum os hospitais guardarem eletronicamente todo o historial e relatórios clínicos dos utentes. O armazenamento digital destes dados traz vantagens aos sistemas de saúde como a acessibilidade, a otimização de recursos e redução de custos, a diminuição do erro médico e o auxílio nas tomadas de decisões. Grande parte desses dados está em formato de texto livre, ou seja, são dados não estruturados. Para os sistemas computacionais, este tipo de dados representa um maior desafio quer na análise, quer no seu processamento. Sendo que, para este tipo de informação ser processada automaticamente é necessário recorrer ao Processamento de Linguagem Natural, uma subárea da Inteligência Artificial. Tarefas como classificação ou reconhecimento de entidades em textos requerem quase sempre textos anotados. O processo de anotação dos textos é demorado e pouco atrativo para o ser humano levando a que a quantidade disponível de dados anotados não seja em grande volume e consequentemente a que a aplicação de modelos de Machine Learning não seja a mais eficiente, resultado em problemas de over fitting e não generalizando como seria de desejar. Devido a isto, a procura por uma solução de anotação automática dos dados em massa é necessária e extremamente útil. A principal contribuição desta dissertação é o desenvolvimento de uma aplicação para a anotação automática de informação clínica. Esta aplicação permitirá a anotação de grandes quantidades de dados de forma automática comparativamente a outras ferramentas e abordagens existentes.The proximity between Informatics and Health is growing day by day. Nowadays, it is common for hospitals to store all the history and clinical data electronically. The digital storage of these data brings advantages to health systems such as accessibility, optimization of resources and cost reduction, reduction of medical errors and help in decision-making. However, most of this data is in free-text format, that is, unstructured data. For computer systems, this type of data represents an enormous challenge both in analysis and processing. For this type of information to be processed automatically, it is necessary to resort to Natural Language Processing, a sub-area of Artificial Intelligence. Tasks such as classification or name entity recognition almost always require annotated text. The process of annotating texts is time-consuming and unattractive for human beings, leading to the fact that the available amount of annotated data is not large. Consequently, the application of Machine Learning models is not the most efficient, resulting in overfitting problems and not generalizing as we would like. Due to this, the search for a solution of automatic annotation of clinical data is necessary and extremely useful. The main contribution of this dissertation is the development of an application for the automatic annota tion of clinical information. This application will allow the annotation of large amounts of data automatically compared to other existing tools and approaches