Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology

International audienceThis article presents the Kasimir system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL. The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, e.g., because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning in the framework of description logics have been used. In addition, the domain of oncology is complex and involves several specialties, e.g. surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed description logic has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized case-based reasoning

a survey

Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern ontology engineering. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising field

Collaborative ontology engineering: a survey

Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern ontology engineering. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising fiel

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Combining ontologies and rules with clinical archetypes

Al igual que otros campos que dependen en gran medida de las funcionalidades ofrecidas por las tecnologías de la información y las comunicaciones (IT), la biomedicina y la salud necesitan cada vez más la implantación de normas y mecanismos ampliamente aceptados para el intercambio de datos, información y conocimiento. Dicha necesidad de compatibilidad e interoperabilidad va más allá de las cuestiones sintácticas y estructurales, pues la interoperabilidad semántica es también requerida. La interoperabilidad a nivel semántico es esencial para el soporte computarizado de alertas, flujos de trabajo y de la medicina basada en evidencia cuando contamos con la presencia de sistemas heterogéneos de Historia Clínica Electrónica (EHR). El modelo de arquetipos clínicos respaldado por el estándar CEN/ISO EN13606 y la fundación openEHR ofrece un mecanismo para expresar las estructuras de datos clínicos de manera compartida e interoperable. El modelo ha ido ganando aceptación en los últimos años por su capacidad para definir conceptos clínicos basados en un Modelo de Referencia común. Dicha separación a dos capas permite conservar la heterogeneidad de las implementaciones de almacenamiento a bajo nivel, presentes en los diferentes sistemas de EHR. Sin embargo, los lenguajes de arquetipos no soportan la representación de reglas clínicas ni el mapeo a ontologías formales, ambos elementos fundamentales para alcanzar la interoperabilidad semántica completa pues permiten llevar a cabo el razonamiento y la inferencia a partir del conocimiento clínico existente. Paralelamente, es reconocido el hecho de que la World Wide Web presenta requisitos análogos a los descritos anteriormente, lo cual ha fomentado el desarrollo de la Web Semántica. El progreso alcanzado en este terreno, con respecto a la representación del conocimiento y al razonamiento sobre el mismo, es combinado en esta tesis con los modelos de EHR con el objetivo de mejorar el enfoque de los arquetipos clínicos y ofrecer funcionalidades que se corresponden con nivel más alto de interoperabilidad semántica. Concretamente, la investigación que se describe a continuación presenta y evalúa un enfoque para traducir automáticamente las definiciones expresadas en el lenguaje de definición de arquetipos de openEHR (ADL) a una representación formal basada en lenguajes de ontologías. El método se implementa en la plataforma ArchOnt, que también es descrita. A continuación se estudia la integración de dichas representaciones formales con reglas clínicas, ofreciéndose un enfoque para reutilizar el razonamiento con instancias concretas de datos clínicos. Es importante ver como el acto de compartir el conocimiento clínico expresado a través de reglas es coherente con la filosofía de intercambio abierto fomentada por los arquetipos, a la vez que se extiende la reutilización a proposiciones de conocimiento declarativo como las utilizadas en las guías de práctica clínica. De esta manera, la tesis describe una técnica de mapeo de arquetipos a ontologías, para luego asociar reglas clínicas a la representación resultante. La traducción automática también permite la conexión formal de los elementos especificados en los arquetipos con conceptos clínicos equivalentes provenientes de otras fuentes como son las terminologías clínicas. Dichos enlaces fomentan la reutilización del conocimiento clínico ya representado, así como el razonamiento y la navegación a través de distintas ontologías clínicas. Otra contribución significativa de la tesis es la aplicación del enfoque mencionado en dos proyectos de investigación y desarrollo clínico, llevados a cabo en combinación con hospitales universitarios de Madrid. En la explicación se incluyen ejemplos de las aplicaciones más representativas del enfoque como es el caso del desarrollo de sistemas de alertas orientados a mejorar la seguridad del paciente. No obstante, la traducción automática de arquetipos clínicos a lenguajes de ontologías constituye una base común para la implementación de una amplia gama de actividades semánticas, razonamiento y validación, evitándose así la necesidad de aplicar distintos enfoques ad-hoc directamente sobre los arquetipos para poder satisfacer las condiciones de cada contexto

Distributed Knowledge Modeling and Integration of Model-Based Beliefs into the Clinical Decision-Making Process

Das Treffen komplexer medizinischer Entscheidungen wird durch die stetig steigende Menge an zu berücksichtigenden Informationen zunehmend komplexer. Dieser Umstand ist vor allem auf die Verfügbarkeit von immer präziseren diagnostischen Methoden zur Charakterisierung der Patienten zurückzuführen (z.B. genetische oder molekulare Faktoren). Hiermit einher geht die Entwicklung neuartiger Behandlungsstrategien und Wirkstoffe sowie die damit verbundenen Evidenzen aus klinischen Studien und Leitlinien. Dieser Umstand stellt die behandelnden Ärztinnen und Ärzte vor neuartige Herausforderungen im Hinblick auf die Berücksichtigung aller relevanten Faktoren im Kontext der klinischen Entscheidungsfindung. Moderne IT-Systeme können einen wesentlichen Beitrag leisten, um die klinischen Experten weitreichend zu unterstützen. Diese Assistenz reicht dabei von Anwendungen zur Vorverarbeitung von Daten für eine Reduktion der damit verbundenen Komplexität bis hin zur systemgestützten Evaluation aller notwendigen Patientendaten für eine therapeutischen Entscheidungsunterstützung. Möglich werden diese Funktionen durch die formale Abbildung von medizinischem Fachwissen in Form einer komplexen Wissensbasis, welche die kognitiven Prozesse im Entscheidungsprozess adaptiert. Entsprechend werden an den Prozess der IT-konformen Wissensabbildung erhöhte Anforderungen bezüglich der Validität und Signifikanz der enthaltenen Informationen gestellt. In den ersten beiden Kapiteln dieser Arbeit wurden zunächst wichtige methodische Grundlagen im Kontext der strukturierten Abbildung von Wissen sowie dessen Nutzung für die klinische Entscheidungsunterstützung erläutert. Hierbei wurden die inhaltlichen Kernthemen weiterhin im Rahmen eines State of the Art mit bestehenden Ansätzen abgeglichen, um den neuartigen Charakter der vorgestellten Lösungen herauszustellen. Als innovativer Kern wurde zunächst die Konzeption und Umsetzung eines neuartigen Ansatzes zur Fusion von fragmentierten Wissensbausteinen auf der formalen Grundlage von Bayes-Netzen vorgestellt. Hierfür wurde eine neuartige Datenstruktur unter Verwendung des JSON Graph Formats erarbeitet. Durch die Entwicklung von qualifizierten Methoden zum Umgang mit den formalen Kriterien eines Bayes-Netz wurden weiterhin Lösungen aufgezeigt, welche einen automatischen Fusionsprozess durch einen eigens hierfür entwickelten Algorithmus ermöglichen. Eine prototypische und funktionale Plattform zur strukturierten und assistierten Integration von Wissen sowie zur Erzeugung valider Bayes-Netze als Resultat der Fusion wurde unter Verwendung eines Blockchain Datenspeichers implementiert und in einer Nutzerstudie gemäß ISONORM 9241/110-S evaluiert. Aufbauend auf dieser technologischen Plattform wurden im Anschluss zwei eigenständige Entscheidungsunterstützungssysteme vorgestellt, welche relevante Anwendungsfälle im Kontext der HNO-Onkologie adressieren. Dies ist zum einen ein System zur personalisierten Bewertung von klinischen Laborwerten im Kontext einer Radiochemotherapie und zum anderen ein in Form eines Dashboard implementiertes Systems zur effektiveren Informationskommunikation innerhalb des Tumor Board. Beide Konzepte wurden hierbei zunächst im Rahmen einer initialen Nutzerstudie auf Relevanz geprüft, um eine nutzerzentrische Umsetzung zu gewährleisten. Aufgrund des zentralen Fokus dieser Arbeit auf den Bereich der klinischen Entscheidungsunterstützung, werden an zahlreichen Stellen sowohl kritische als auch optimistische Aspekte der damit verbundenen praktischen Lösungen diskutiert.:1 Introduction 1.1 Motivation and Clinical Setting 1.2 Objectives 1.3 Thesis Outline 2 State of the Art 2.1 Medical Knowledge Modeling 2.2 Knowledge Fusion 2.3 Clinical Decision Support Systems 2.4 Clinical Information Access 3 Fundamentals 3.1 Evidence-Based Medicine 3.1.1 Literature-Based Evidence 3.1.2 Practice-Based Evidence 3.1.3 Patient-Directed Evidence 3.2 Knowledge Representation Formats 3.2.1 Logic-Based Representation 3.2.2 Procedural Representation 3.2.3 Network or Graph-Based Representation 3.3 Knowledge-Based Clinical Decision Support 3.4 Conditional Probability and Bayesian Networks 3.5 Clinical Reasoning 3.5.1 Deterministic Reasoning 3.5.2 Probabilistic Reasoning 3.6 Knowledge Fusion of Bayesian Networks 4 Block-Based Collaborative Knowledge Modeling 4.1 Data Model 4.1.1 Belief Structure 4.1.2 Conditional Probabilities 4.1.3 Metadata 4.2 Constraint-Based Automatic Knowledge Fusion 4.2.1 Fusion of the Bayesian Network Structures 4.2.2 Fusion of the Conditional Probability Tables 4.3 Blockchain-Based Belief Storage and Retrieval 4.3.1 Blockchain Characteristics 4.3.2 Relevance for Belief Management 5 Selected CDS Applications for Clinical Practice 5.1 Distributed Knowledge Modeling Platform 5.1.1 Requirement Analysis 5.1.2 System Architecture 5.1.3 System Evaluation 5.1.4 Limitations of the Proposed Solution 5.2 Personalization of Laboratory Findings 5.2.1 Requirement Analysis 5.2.2 System Architecture 5.2.3 Limitations of the Proposed Solution 5.3 Dashboard for Collaborative Decision-Making in the Tumor Board 5.3.1 Requirement Analysis 5.3.2 System Architecture 5.3.3 Limitations of the Proposed Solution 6 Discussion 6.1 Goal Achievements 6.2 Contributions and Conclusion 7 Bibliograph

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care