Existing data sources for clinical epidemiology: The North Denmark Bacteremia Research Database

Bacteremia is associated with high morbidity and mortality. Improving prevention and treatment requires better knowledge of the disease and its prognosis. However, in order to study the entire spectrum of bacteremia patients, we need valid sources of information, prospective data collection, and complete follow-up. In North Denmark Region, all patients diagnosed with bacteremia have been registered in a population-based database since 1981. The information has been recorded prospectively since 1992 and the main variables are: the patient’s unique civil registration number, date of sampling the first positive blood culture, date of admission, clinical department, date of notification of growth, place of acquisition, focus of infection, microbiological species, antibiogram, and empirical antimicrobial treatment. During the time from 1981 to 2008, information on 22,556 cases of bacteremia has been recorded. The civil registration number makes it possible to link the database to other medical databases and thereby build large cohorts with detailed longitudinal data that include hospital histories since 1977, comorbidity data, and complete follow-up of survival. The database is suited for epidemiological research and, presently, approximately 60 studies have been published. Other Danish departments of clinical microbiology have recently started to record the same information and a population base of 2.3 million will be available for future studies

Dépistage du cancer de la prostate analyse décisionnelle

Le cancer le plus répandu et le deuxième plus meurtrier chez les hommes est le cancer de la prostate. Afin d'améliorer les chances de survie des patients, il est nécessaire de faire un dépistage tôt dans la maladie. La stratégie principale de dépistage utilise différents marqueurs qui identifient la maladie chez le patient. Cependant, le choix des marqueurs est très variable. Depuis le début des années 90, moment où une grande évolution s'effectue au niveau des marqueurs, le choix de quels marqueurs sont les plus performants est devenue une tâche fastidieuse. Nous proposons donc une modélisation décisionnelle qui permettra de faire l'évaluation des différentes stratégies et marqueurs existants. Nous avons utilisé la représentation conceptuelle du problème du cancer de la prostate pour faire un modèle en trois phases : dépistage, déterminer le stade de la maladie, traitement. Les données utilisées proviennent d'études systématiques publiées et d'une étude systématique particulière qui vise le dépistage du cancer de la prostate par de nouveaux marqueurs biochimiques. Différentes stratégies alternatives ont été évaluées : l'antigène spécifique de la prostate totale (tASP), ASP complexe (cASP), ASP libre (lASP), le rapport de ASP libre sur ASP totale (l/tASP), le rapport ASP complexe/totale (c/tASP) ainsi que toutes avec/sans touché rectal (TR). Un niveau de sensibilité a été établit à 90% pour tous les tests de dépistage. L'utilité prévisionnelle des stratégies alternatives a été calculée en utilisant la simulation de Monte-Carlo. De plus, nous avons utilisé le test de Student pour comparer les différentes stratégies de dépistage. Finalement, une analyse de sensibilité avec représentation en diagramme de tornade a été appliquée à la survie des patients en ce qui concerne les caractéristiques de la population. Deux logiciels pour la construction du modèle de décision (ReasonEdge et Data 3.5) ont été utilisés. Différentes méthodologies (modélisation décisionnelle et revue systématique) ont été examinées pour l'évaluation du dépistage du cancer de la prostate. Le processus de modélisation a été basé sur la création du modèle conceptuel du problème et le choix d'informations probabilistes basées sur la relation structurale entre les éléments du modèle de décision. Des lignes directrices de représentation ont été utilisées afin d'éviter les problèmes de transparence et d'augmenter la réutilisation du modèle. De plus, le modèle résultant est généralisable car il est possible de lui poser différentes questions. Finalement, les stratégies de dépistage et l'examen des facteurs importants pour les décisions ont été évaluées [i.e. évalués]. L'examen des influences du dépistage sur la détection du stade du cancer aidera l'estimation de l'impact de ce dépistage sur la survie de la population

Hypothesis Testing with Classifier Systems

This thesis presents a new ML algorithm, HCS, taking inspiration from Learning Classifier Systems, Decision Trees and Statistical Hypothesis Testing, aimed at providing clearly understandable models of medical datasets. Analysis of medical datasets has some specific requirements not always fulfilled by standard Machine Learning methods. In particular, heterogeneous and missing data must be tolerated, the results should be easily interpretable. Moreover, often the combination of two or more attributes leads to non-linear effects not detectable for each attribute on its own. Although it has been designed specifically for medical datasets, HCS can be applied to a broad range of data types, making it suitable for many domains. We describe the details of the algorithm, and test its effectiveness on five real-world datasets

Personalised antimicrobial management in secondary care

Background: The growing threat of Antimicrobial Resistance (AMR) requires innovative methods to promote the sustainable effectiveness of antimicrobial agents. Hypothesis: This thesis aimed to explore the hypothesis that personalised decision support interventions have the utility to enhance antimicrobial management across secondary care. Methods: Different research methods were used to investigate this hypothesis. Individual physician decision making was mapped and patient experiences of engagement with decision making explored using semi-structured interviews. Cross-specialty engagement with antimicrobial management was investigated through cross-sectional analysis of conference abstracts and educational training curricula. Artificial intelligence tools were developed to explore their ability to predict the likelihood of infection and provide individualised prescribing recommendations using routine patient data. Dynamic, individualised dose optimisation was explored through: (i) development of a microneedle based, electrochemical biosensor for minimally invasive monitoring of beta-lactams; and (ii) pharmacokinetic (PK)-pharmacodynamic (PD) modelling of a new PK-PD index using C-Reactive protein (CRP) to predict the pharmacodynamics of vancomycin. Ethics approval was granted for all aspects of work explored within this thesis. Results: Mapping of individual physician decision making during infection management demonstrated several areas where personalised, technological interventions could enhance antimicrobial management. At specialty level, non-infection specialties have little engagement with antimicrobial management. The importance of engaging surgical specialties, who have relatively high rates of antimicrobial usage and healthcare associated infections, was observed. An individualised information leaflet, co-designed with patients, to provide personalised infection information to in-patients receiving antibiotics significantly improved knowledge and reported engagement with decision making. Artificial intelligence was able to enhance the prediction of infection and the prescribing of antimicrobials using routinely available clinical data. Real-time, continuous penicillin monitoring was demonstrated using a microneedle based electrochemical sensor in-vivo. A new PK-PD index, using C-Reactive Protein, was able to predict individual patient response to vancomycin therapy at 96-120 hours of therapy. Conclusion: Through co-design and the application of specific technologies it is possible to provide personalised antimicrobial management within secondary care.Open Acces

An experimental study and evaluation of a new architecture for clinical decision support - integrating the openEHR specifications for the Electronic Health Record with Bayesian Networks

Healthcare informatics still lacks wide-scale adoption of intelligent decision support methods, despite continuous increases in computing power and methodological advances in scalable computation and machine learning, over recent decades. The potential has long been recognised, as evidenced in the literature of the domain, which is extensively reviewed. The thesis identifies and explores key barriers to adoption of clinical decision support, through computational experiments encompassing a number of technical platforms. Building on previous research, it implements and tests a novel platform architecture capable of processing and reasoning with clinical data. The key components of this platform are the now widely implemented openEHR electronic health record specifications and Bayesian Belief Networks. Substantial software implementations are used to explore the integration of these components, guided and supplemented by input from clinician experts and using clinical data models derived in hospital settings at Moorfields Eye Hospital. Data quality and quantity issues are highlighted. Insights thus gained are used to design and build a novel graph-based representation and processing model for the clinical data, based on the openEHR specifications. The approach can be implemented using diverse modern database and platform technologies. Computational experiments with the platform, using data from two clinical domains – a preliminary study with published thyroid metabolism data and a substantial study of cataract surgery – explore fundamental barriers that must be overcome in intelligent healthcare systems developments for clinical settings. These have often been neglected, or misunderstood as implementation procedures of secondary importance. The results confirm that the methods developed have the potential to overcome a number of these barriers. The findings lead to proposals for improvements to the openEHR specifications, in the context of machine learning applications, and in particular for integrating them with Bayesian Networks. The thesis concludes with a roadmap for future research, building on progress and findings to date