Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

WELLNESS PROFILING ON SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH

An ontological framework for the formal representation and management of human stress knowledge

There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain

Generierung menschlicher Verhaltensprofile mittels unüberwachter Methoden zur Bewertung des Gesundheitszustandes

In the context of ambient assisted living, implementation of human behavior profiling is expected to occur through pervasive computing. As for information extraction from measured data, the typical way are supervised methods. However, due to the low adaptivity and high dependency on lab-setting, and the necessity of data labeling and model training, these types of methods are limited in human behavior profiling in real-life scenarios. Therefore, simple and unobtrusive sensors are relied upon to obtain daily behavior information. In spite of the incomplete observation, these sensors are able to provide key information. Thus, unsupervised methods have to be designed based on this measurement. In contrast to supervised data analysis, unsupervised methods have inherent advantages: Firstly, data labeling and training are not necessary. Secondly, they are more adaptive, making them suitable for use by different individuals. Thirdly, unknown knowledge might be discovered. In order to propose unsupervised methods for human behavior profiling that can be practically applied, the following research is conducted in this doctoral thesis: First, abstractions of events and patterns of in-home behavior scenario are defined. Second, the discovering algorithm is derived, whereby regularly occurring sensor events that can represent lifestyle patterns can be discovered. Third, with the lifestyle depicted, the change of human behavior is modeled to present the variance of lifestyle. Aiming to investigate the effectiveness of these methods, they are applied to the datasets obtained in GAL-NATARS study, which is carried out in the setting of real-life, and their effectiveness is evaluated through comparison with medical assessment results.Im Rahmen von Ambient Assisted Living sollen menschliche Verhaltensprofile durch den Pervasive Computing generiert werden. Zur Extraktion von Informationen aus Messdaten werden typischerweise überwachte Methoden verwendet. In Bezug sind diese Methoden wegen ihrer geringen Anpassungsfähigkeit, hohen Abhängigkeit von Laborumgebungen, der Notwendigkeit der Kennzeichnung und der Lernphase in realen Szenarien zur Generierung von menschliche Verhaltensprofile sehr eingeschränkt. Daher sollten einfache und unauffällige Sensoren verwendet werden, um täglich Verhaltensinformationen zu erhalten. Trotz der unvollständigen Beobachtung sind diese Sensoren in der Lage, die wichtige Informationen zu liefern. Hierfür sind unüberwachte Methoden notwendig, die auf der Grundlage dieser Messungen ausgeführt werden. Im Gegensatz zur überwachten Datenanalyse, haben unüberwachte Methoden folgende Vorteile: Zum einen sind keine Kennzeichnung von Daten und keine Lernphase erforderlich. Zweitens sind sie anpassungsfähiger, so dass sie für die Verwendung bei verschiedenen Individuen geeignet sind. Drittens können siebisher unbekanntes Wissen entdecken. Zur Entwicklung von praktisch anwendbaren unüberwachten Methoden für die Generierung menschlicher Verhaltensprofile, wird in dieser Doktorarbeit die folgende Forschung durchgeführt: Erstens, Definition von Abstraktionen für Ereignisse und Muster häuslichen Verhaltens. Zweitens wird ein Entdeckungsalgorithmus abgeleitet, der regelmäßig auftretende Sensorereignisse, die Lebensgewohnheiten darstellen können, entdecken kann. Drittens, wird mit den so gewonnenen Lebensgewohnheiten, die Änderung des menschlichen Verhaltens modelliert, um die Varianz des Lebensstils abzubilden. Mit dem Ziel, die Wirksamkeit dieser Methoden zu untersuchen, werden sie auf Datensätze aus dem Feld, gesammelt in der GAL-NATARS Studie durchgeführt wird, angewendet. Ihre Wirksamkeit wird durch den Vergleich mit den Ergebnissen der medizinischen Beurteilung bewertet

eXplainable AI for trustworthy healthcare applications

Acknowledging that AI will inevitably become a central element of clinical practice, this thesis investigates the role of eXplainable AI (XAI) techniques in developing trustworthy AI applications in healthcare. The first part of this thesis focuses on the societal, ethical, and legal aspects of the use of AI in healthcare. It first compares the different approaches to AI ethics worldwide and then focuses on the practical implications of the European ethical and legal guidelines for AI applications in healthcare. The second part of the thesis explores how XAI techniques can help meet three key requirements identified in the initial analysis: transparency, auditability, and human oversight. The technical transparency requirement is tackled by enabling explanatory techniques to deal with common healthcare data characteristics and tailor them to the medical field. In this regard, this thesis presents two novel XAI techniques that incrementally reach this goal by first focusing on multi-label predictive algorithms and then tackling sequential data and incorporating domainspecific knowledge in the explanation process. This thesis then analyzes the ability to leverage the developed XAI technique to audit a fictional commercial black-box clinical decision support system (DSS). Finally, the thesis studies AI explanation’s ability to effectively enable human oversight by studying the impact of explanations on the decision-making process of healthcare professionals

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

Surgical Management of Gastroesophageal Reflux in Children: Risk Stratification and Prediction of Outcomes

Introduction: Since the 1980s fundoplication, an operation developed for adults with hiatus hernia and reflux symptoms, has been performed in children with gastroesophageal reflux disease (GORD). When compared to adult outcomes, paediatric fundoplication has resulted in higher failure and revision rates. In the first chapter we explore differences in paradigm, patient population and outcomes. Firstly, symptoms are poorly defined and are measured by instruments of varying quality. Secondly, neurological impairment (NI), prematurity and congenital anomalies (oesophageal atresia, congenital diaphragmatic hernia) are prevalent in children. / Purpose: To develop methods for stratifying paediatric fundoplication risk and predicting outcomes based on symptom profile, demographic factors, congenital and medical history. / Methods: Study objectives are addressed in three opera: a symptom questionnaire development (TARDIS:REFLUX), a randomised controlled trial (RCT) and a retrospective database study (RDS). TARDIS: REFLUX: In the second chapter, digital research methods are used to design and validate a symptom questionnaire for paediatric GORD. The questionnaire is a market-viable smartphone app hosted on a commercial platform and trialed in a clinical pilot study. / RCT: In the third chapter, the REMOS trial is reported. The trial addresses the subset of children with NI and feeding difficulties. Participants are randomized to gastrostomy with or without fundoplication. Notably, pre- and post-operative reflux is quantified using pH-impedance. / RDS: In the fourth chapter, data mining and machine learning strategies are applied to a retrospective paediatric GORD database. Predictive modelling techniques applied include logistic regression, decision trees, random forests and market basket analysis. / Results and conclusion: This work makes two key contributions. Firstly, an effective methodology for development of digital research tools is presented here. Secondly, a synthesis is made of literature, the randomised controlled trial and retrospective database modelling. The resulting product is an evidence-based algorithm for the surgical management of children with GORD

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin