Avec la montée en charge de l’informatisation des systèmes d’information hospitaliers, une quantité croissante de données est produite tout au long de la prise en charge des patients. L’utilisation secondaire de ces données constitue un enjeu essentiel pour la recherche ou l’évaluation en santé. Dans le cadre de cette thèse, nous discutons les verrous liés à la représentation et à la sémantique des données, qui limitent leur utilisation secondaire en cancérologie. Nous proposons des méthodes basées sur des ontologies pour l’intégration sémantique des données de diagnostics. En effet, ces données sont représentées par des terminologies hétérogènes. Nous étendons les modèles obtenus pour la représentation de la maladie tumorale, et les liens qui existent avec les diagnostics. Enfin, nous proposons une architecture combinant entrepôts de données, registres de métadonnées et web sémantique. L’architecture proposée permet l’intégration syntaxique et sémantique d’un grand nombre d’observations. Par ailleurs, l’intégration de données et de connaissances (sous la forme d’ontologies) a été utilisée pour construire un algorithme d’identification de la maladie tumorale en fonction des diagnostics présents dans les données de prise en charge. Cet algorithme basé sur les classes de l’ontologie est indépendant des données effectivement enregistrées. Ainsi, il fait abstraction du caractère hétérogène des données diagnostiques initialement disponibles. L’approche basée sur une ontologie pour l’identification de la maladie tumorale, permet une adaptation rapide des règles d’agrégation en fonction des besoins spécifiques d’identification. Ainsi, plusieurs versions du modèle d’identification peuvent être utilisées avec des granularités différentes. / With the increasing adoption of Electronic Health Records (EHR), the amount of data produced at the patient bedside is rapidly increasing. Secondary use is there by an important field to investigate in order facilitate research and evaluation. In these work we discussed issues related to data representation and semantics within EHR that need to be address in order to facilitate secondary of structured data in oncology. We propose and evaluate ontology based methods for heterogeneous diagnosis terminologies integration in oncology. We then extend obtained model to enable tumoral disease representation and links with diagnosis as recorded in EHR. We then propose and implement a complete architecture combining a clinical data warehouse, a metadata registry and web semantic technologies and standards. This architecture enables syntactic and semantic integration of a broad range of hospital information System observation. Our approach links data with external knowledge (ontology), in order to provide a knowledge resource for an algorithm for tumoral disease identification based on diagnosis recorded within EHRs. As it based on the ontology classes, the identification algorithm is uses an integrated view of diagnosis (avoiding semantic heterogeneity). The proposed architecture leading to algorithm on the top of an ontology offers a flexible solution. Adapting the ontology, modifying for instance the granularity provide a way for adapting aggregation depending on specific needs

The increasing demand for healthcare services, coupled with the challenges of managing budgets and navigating complex regulations, has underscored the need for sustainable and efficient healthcare delivery. In response to this pressing issue, this thesis aims to optimize hospital efficiency using Artificial Intelligence (AI) techniques. The focus extends beyond improving surgical intraoperative time to encompass preoperative and postoperative periods as well. The research presents a novel Prescriptive Analytics System (PAS) designed to enhance the Surgical Success Rate (SSR) in surgeries and specifically in high volume arthroplasty. The SSR is a critical metric that reflects the successful completion of 4-surgeries during an 8-hour timeframe. By leveraging AI, the developed PAS has the potential to significantly improve the SSR from its current rate of 39% at The Ottawa Hospital to a remarkable 100%. The research is structured around five peer-reviewed journal papers, each addressing a specific aspect of the optimization of surgical efficiency. The first paper employs descriptive analytics to examine the factors influencing delays and overtime pay during surgeries. By identifying and analyzing these factors, insights are gained into the underlying causes of surgery inefficiencies. The second paper proposes three frameworks aimed at improving Operating Room (OR) throughput. These frameworks provide structured guidelines and strategies to enhance the overall efficiency of surgeries, encompassing preoperative, intraoperative, and postoperative stages. By streamlining the workflow and minimizing bottlenecks, the proposed frameworks have the potential to significantly optimize surgical operations. The third paper outlines a set of actions required to transform a selected predictive system into a prescriptive one. By integrating AI algorithms with decision support mechanisms, the system can offer actionable recommendations to surgeons during surgeries. This transformative step holds tremendous potential in enhancing surgical outcomes while reducing time. The fourth paper introduces a benchmarking and monitoring system for the selected framework that predicts SSR. Leveraging historical data, this system utilizes supervised machine learning algorithms to forecast the likelihood of successful outcomes based on various surgical team and procedural parameters. By providing real-time monitoring and predictive insights, surgeons can proactively address potential risks and improve decision-making during surgeries. Lastly, an application paper demonstrates the practical implementation of the prescriptive analytics system. The case study highlights how the system optimizes the allocation of resources and enables the scheduling of additional surgeries on days with a high predicted SSR. By leveraging the system's capabilities, hospitals can maximize their surgical capacity and improve overall patient care.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-05-31T18:20:47Z No. of bitstreams: 1 iurimalinoskiteixeira.pdf: 1437690 bytes, checksum: c11d45074fef83b3318f92c12b425557 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-01T11:50:34Z (GMT) No. of bitstreams: 1 iurimalinoskiteixeira.pdf: 1437690 bytes, checksum: c11d45074fef83b3318f92c12b425557 (MD5) / Made available in DSpace on 2017-06-01T11:50:34Z (GMT). No. of bitstreams: 1 iurimalinoskiteixeira.pdf: 1437690 bytes, checksum: c11d45074fef83b3318f92c12b425557 (MD5) Previous issue date: 2013-02-26 / FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais / A modelagem de dados clínicos para Sistemas de Informação em Saúde (SIS) demanda expertise de domínio. Técnicas de Desenvolvimento Dirigido por Modelos (DDM) permi tem uma melhor articulação entre especialistas de domínio e desenvolvedores de SISs e possibilitam reduzir o custo de desenvolvimento desses sistemas. Modelos de dados clí- nicos baseados em especificações padronizadas e abertas como a do openEHR facilitam sobremaneira a aplicação de técnicas de DDM para SISs. Contudo, o uso de modelos de dados clínicos não resolve sozinho o problema fundamental do alto custo de desenvolvi- mento de SISs. Uma das causas desse problema é a falta de informações arquiteturais nos modelos de dados clínicos. Sem essas informações arquiteturais, o custo de desenvolvi- mento é deslocado para a especificação das regras de transformação de modelos de dados clínicos em código de SIS (regras estas fundamentais nas técnicas de DDM), uma vez que cada novo SIS a ser gerado implica na especificação de um novo conjunto de regras). Neste contexto, este trabalho apresenta uma estratégia para geração de código de SISs ba seada na combinação entre modelos de dados clínicos e informações arquiteturais. Nessa estratégia, o desenvolvedor é capaz de categorizar SISs em diferentes famílias e definir um conjunto de regras de transformação comum a todos os SISs de uma família. Cada família é definida por um conjunto de SISs com estruturas arquiteturais semelhantes e modelos de dados clínicos distintos. O resultado esperado dessa estratégia é um melhor reuso das regras de transformação de modelos. Essa estratégia é empregada para se alcançar o ob jetivo principal deste trabalho, que é a concepção de um sistema de transformação para a síntese automática de Interfaces Gráficas de Usuário (GUI - Graphic User Interface) para SISs, considerando as especificações openEHR e algumas construções presentes em Linguagens de Descrição Arquitetural (ADL), como Acme. Como prova de conceito, esse framework é aplicado em algumas famílias de SIS. / The modeling of clinical data for Health Information Systems (HIS) requires domain expertise. Model-Driven Development (MDD) techniques provide a better articulation between domain experts and developers of HISes and enable the reduction in the develop ment cost of these systems. Clinical data models based on open standard specifications such as the openEHR facilitates the application of MDD techniques for HISes. Neverthe less, the use of clinical data models alone does not solve the fundamental problem of high development cost for HISes. One cause for this problem is the lack of architectural information in clinical data models. Without such architectural information, the develop ment cost is shifted to the specification of transformation rules from clinical data models to HIS code (these rules are fundamental in MDD techniques), since each new HIS to be generated involves the specification of a new set of rules. In this context, this work presents a strategy for code generation of HISes that combines clinical data models and architectural information. In this strategy, the developer is able to categorize HISes in distinct families and define a set of transformation rules that are common to all HISes in a family. Each family is defined by a set of systems with similar architectural structures and distinct clinical data models. The expected result of such a strategy is a better reuse of model transformation rules. This strategy is employed to achieve the main objective of this work, which is to design a transformation system for the automatic synthesis of graphical user interfaces (GUI) for HISes, considering openEHR specifications and some constructs present in architectural description languages (ADLs), such as Acme. As a proof of concept, this framework is applied to some HIS families.

Cardiovascular disease (CVD) is the leading cause of morbidity, mortality, premature death and reduced quality of life for the citizens of the EU. It has been reported that CVD represents a major economic load on health care sys- tems in terms of hospitalizations, rehabilitation services, physician visits and medication. Data Mining techniques with clinical data has become an interesting tool to prevent, diagnose or treat CVD. In this thesis, Knowledge Dis- covery and Data Mining (KDD) was employed to analyse clinical and demographic data, which could be used to diagnose coronary artery disease (CAD). The exploratory data analysis (EDA) showed that female patients at an el- derly age with a higher level of cholesterol, maximum achieved heart rate and ST-depression are more prone to be diagnosed with heart disease. Furthermore, patients with atypical angina are more likely to be at an elderly age with a slightly higher level of cholesterol and maximum achieved heart rate than asymptotic chest pain patients. More- over, patients with exercise induced angina contained lower values of maximum achieved heart rate than those who do not experience it. We could verify that patients who experience exercise induced angina and asymptomatic chest pain are more likely to be diagnosed with heart disease. On the other hand, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Tree, Bagging and Boosting methods were evaluated by adopting a stratified 10 fold cross-validation approach. The learning models provided an average of 78-83% F-score and a mean AUC of 85-88%. Among all the models, the highest score is given by Radial Basis Function Kernel Support Vector Machines (RBF-SVM), achieving 82.5% ± 4.7% of F-score and an AUC of 87.6% ± 5.8%. Our research con- firmed that data mining techniques can support physicians in their interpretations of heart disease diagnosis in addition to clinical and demographic characteristics of patients.

El objetivo de esta memoria se centra en primer lugar en la modelización del comportamiento de enfermedades estacionales mediante sistemas de ecuaciones diferenciales y en el estudio de las propiedades dinámicas tales como positividad, periocidad, estabilidad de las soluciones analíticas y la construcción de esquemas numéricos para las aproximaciones de las soluciones numéricas de sistemas de ecuaciones diferenciales de primer orden no lineales, los cuales modelan el comportamiento de enfermedades infecciosas estacionales tales como la transmisión del virus Respiratory Syncytial Virus (RSV). Se generalizan dos modelos matemáticos de enfermedades estacionales y se demuestran que tiene soluciones periódicas usando un Teorema de Coincidencia de Jean Mawhin. Para corroborar los resultados analíticos, se desarrollan esquemas numéricos usando las técnicas de diferencias finitas no estándar desarrolladas por Ronald Michens y el método de la transformada diferencial, los cuales permiten reproducir el comportamiento dinámico de las soluciones analíticas, tales como positividad y periocidad. Finalmente, las simulaciones numéricas se realizan usando los esquemas implementados y parámetros deducidos de datos clínicos De La Región de Valencia de personas infectadas con el virus RSV. Se confrontan con las que arrojan los métodos de Euler, Runge Kutta y la rutina de ODE45 de Matlab, verificándose mejores aproximaciones para tamaños de paso mayor a los que usan normalmente estos esquemas tradicionales. / Arenas Tawil, AJ. (2009). Mathematical modelling of virus RSV: qualitative properties, numerical solutions and validation for the case of the region of Valencia [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8316 / Palancia

Dizertační práce se zabývá predikcí vysokodimenzionálních dat genových expresí. Množství dostupných genomických dat významně vzrostlo v průběhu posledního desetiletí. Kombinování dat genových expresí s dalšími daty nachází uplatnění v mnoha oblastech. Například v klinickém řízení rakoviny (clinical cancer management) může přispět k přesnějšímu určení prognózy nemocí. Hlavní část této dizertační práce je zaměřena na kombinování dat genových expresí a klinických dat. Používáme logistické regresní modely vytvořené prostřednictvím různých regularizačních technik. Generalizované lineární modely umožňují kombinování modelů s různou strukturou dat. V dizertační práci je ukázáno, že kombinování modelu dat genových expresí a klinických dat může vést ke zpřesnění výsledku predikce oproti vytvoření modelu pouze z dat genových expresí nebo klinických dat. Navrhované postupy přitom nejsou výpočetně náročné.  Testování je provedeno nejprve se simulovanými datovými sadami v různých nastaveních a následně s~reálnými srovnávacími daty. Také se zde zabýváme určením přídavné hodnoty microarray dat. Dizertační práce obsahuje porovnání příznaků vybraných pomocí klasifikátoru genových expresí na pěti různých sadách dat týkajících se rakoviny prsu. Navrhujeme také postup výběru příznaků, který kombinuje data genových expresí a znalosti z genových ontologií.

<p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p>