Linking patient data to scientific knowledge to support contextualized mining

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2022ICU readmissions are a critical problem associated with either serious conditions, ill nesses, or complications, representing a 4 times increase in mortality risk and a financial burden to health institutions. In developed countries 1 in every 10 patients discharged comes back to the ICU. As hospitals become more and more data-oriented with the adop tion of Electronic Health Records (EHR), there as been a rise in the development of com putational approaches to support clinical decision. In recent years new efforts emerged, using machine learning approaches to make ICU readmission predictions directly over EHR data. Despite these growing efforts, machine learning approaches still explore EHR data directly without taking into account its mean ing or context. Medical knowledge is not accessible to these methods, who work blindly over the data, without considering the meaning and relationships the data objects. Ontolo gies and knowledge graphs can help bridge this gap between data and scientific context, since they are computational artefacts that represent the entities in a domain and how the relate to each other in a formalized fashion. This opportunity motivated the aim of this work: to investigate how enriching EHR data with ontology-based semantic annotations and applying machine learning techniques that explore them can impact the prediction of 30-day ICU readmission risk. To achieve this, a number of contributions were developed, including: (1) An enrichment of the MIMIC-III data set with annotations to several biomedical ontologies; (2) A novel ap proach to predict ICU readmission risk that explores knowledge graph embeddings to represent patient data taking into account the semantic annotations; (3) A variant of the predictive approach that targets different moments to support risk prediction throughout the ICU stay. The predictive approaches outperformed both state-of-the-art and a baseline achieving a ROC-AUC of 0.815 (an increase of 0.2 over the state of the art). The positive results achieved motivated the development of an entrepreneurial project, which placed in the Top 5 of the H-INNOVA 2021 entrepreneurship award

Development of a Quantum-based Ontology for Describing NDE by Using Computerized Natural Language Processing

The Survival Hypothesis states that a person’s personality and consciousness survive the physical death of the body. Ontology is a well-established theoretical domain within philosophy dealing with models of reality. This report proposes the use of computer natural language processing and classification of perceived objects in Near Death Experience (NDE) stories for the validation of a Quantum Ontology based on the Quantum Hologram Theory of Physics and Consciousness. This proposes Quantum Ontology to represent the unintelligible aspects of near-death experiences. The research proposes a validation of ontology constructs within a Quantum Ontology to show the potential of this methodology in NDE research

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

Exploring perspectives of people with type-1 diabetes on goalsetting strategies within self-management education and care

Background. Collaborative goal-setting strategies are widely recommended for diabetes self-management support within healthcare systems. Creating self-management plans that fit with peoples’ own goals and priorities has been linked with better diabetic control. Consequently, goal-setting has become a core component of many diabetes selfmanagement programmes such as the ‘Dose Adjustment for Normal Eating (DAFNE) programme’. Within DAFNE, people with Type-1 Diabetes (T1D) develop their own goals along with action-plans to stimulate goal-achievement. While widely implemented, limited research has explored how goal-setting strategies are experienced by people with diabetes.Therefore, this study aims to explore the perspectives of people with T1D on theimplementation and value of goal-setting strategies within DAFNE and follow-up diabetes care. Furthermore, views on barriers and facilitators to goal-attainment are explored.Methods. Semi-structured interviews were conducted with 20 people with T1D who attended a DAFNE-programme. Following a longitudinal qualitative research design, interviews took place 1 week, and 6-8 months after completion of DAFNE. A recurrent cross-sectional approach is applied in which themes will be identified at each time-point using thematic analyses.Expected results. Preliminary identified themes surround the difference in value that participants place on goal-setting strategies, and the lack of support for goal-achievement within diabetes care.Current stage. Data collection complete; data-analysis ongoing.Discussion. Goal-setting strategies are increasingly included in guidelines for diabetes support and have become essential parts of many primary care improvement schemes. Therefore, exploring the perspectives of people with T1D on the value and implementation of goal-setting strategies is vital for their optimal application

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining, and machine learning to healthcare engineering systems. A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest, and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors, and content. From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field. The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors’ previous knowledge and the nature of the publications were used to select different platforms. To the best of the authors’ knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.N/