NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

Natural language processing (NLP) for clinical information extraction and healthcare research

Introduction: Epilepsy is a common disease with multiple comorbidities. Routinely collected health care data have been successfully used in epilepsy research, but they lack the level of detail needed for in-depth study of complex interactions between the aetiology, comorbidities, and treatment that affect patient outcomes. The aim of this work is to use natural language processing (NLP) technology to create detailed disease-specific datasets derived from the free text of clinic letters in order to enrich the information that is already available. Method: An NLP pipeline for the extraction of epilepsy clinical text (ExECT) was redeveloped to extract a wider range of variables. A gold standard annotation set for epilepsy clinic letters was created for the validation of the ExECT v2 output. A set of clinic letters from the Epi25 study was processed and the datasets produced were validated against Swansea Neurology Biobank records. A data linkage study investigating genetic influences on epilepsy outcomes using GP and hospital records was supplemented with the seizure frequency dataset produced by ExECT v2. Results: The validation of ExECT v2 produced overall precision, recall, and F1 score of 0.90, 0.86, and 0.88, respectively. A method of uploading, annotating, and linking genetic variant datasets within the SAIL databank was established. No significant differences in the genetic burden of rare and potentially damaging variants were observed between the individuals with vs without unscheduled admissions, and between individuals on monotherapy vs polytherapy. No significant difference was observed in the genetic burden between people who were seizure free for over a year and those who experienced at least one seizure a year. Conclusion: This work presents successful extraction of epilepsy clinical information and explores how this information can be used in epilepsy research. The approach taken in the development of ExECT v2, and the research linking the NLP outputs, routinely collected health care data, and genetics set the way for wider research

Natural language processing e tecniche semantiche per il supporto alla diagnosi: un esperimento

Lo studio si propone di realizzare la classificazione automatica di referti radiologici in due categorie, in base alla presenza o meno di una specifica patologia. Poichè i referti si presentano come testo non strutturato, è necessario estrarre features rilevanti dagli stessi attraverso un processo di Information Extraction. Tali features sono state ottenute mediante Natural Language Processing con GATE, un open source che permette di analizzare il testo e di inserire sullo stesso annotazioni con valenza semantica. Tali annotazioni sono state poi utilizzate come parametri in quattro algoritmi di machine learning, ottenendo la classificazione richiesta. Un confronto dei risultati ha permesso di valutare quale algoritmo, in questo specifico contesto e con le features considerate, ha ottenuto il miglior grado di accuratezz

Semantic Web Enabled Software Engineering

Ontologies allow the capture and sharing of domain knowledge by formalizing information and making it machine understandable. As part of an information system, ontologies can capture and carry the reasoning knowledge needed to fulfill different application goals. Although many ontologies have been developed over recent years, few include such reasoning information. As a result, many ontologies are not used in real-life applications, do not get reused or only act as a taxonomy of a domain. This work is an investigation into the practical use of ontologies as a driving factor in the development of applications and the incorporation of Knowledge Engineering as a meaningful activity into modern agile software development. This thesis contributes a novel methodology that supports an incremental requirement analysis and an iterative formalization of ontology design through the use of ontology reasoning patterns. It also provides an application model for ontology-driven applications that can deal with nonontological data sources. A set of case studies with various application specific goals helps to elucidate whether ontologies are in fact suitable for more than simple knowledge formalization and sharing, and can act as the underlying structure for developing largescale information systems. Tasks from the area of bug-tracker quality mining and clone detection are evaluated for this purpose