Management of data quality when integrating data with known provenance

Abstract unavailable please refer to PD

Personalising patient Internet searching using electronic patient records

The research reported in this thesis addresses a patient's information requirements when searching the Internet for health information. A patient's lack of information about his/her health condition and its care is officially acknowledged and traditional patient information sources do not address today's patient information needs. Internet health information resources have become the foremost health information platform. However, patient Internet searching is currently manual, uncustomised and hindered by health information vocabulary and quality challenges. Patient access to quality Internet health information is currently ensured through national health gateways, medical search engines, third-party accredited search engines and charity health websites. However, such resources are generic, i.e. do not cater for a patient particular information needs. In this study, we propose personalising patient Internet searching by enabling a patient's access to their Electronic Patient Records (EPRs) and using this EPR data in Internet information searching. The feasibility of patient access to EPRs has recently been promoted by national health information programmes. Very recently, in the literature, there are reports about pilot studies on personal Health Record (PHR) systems that offer a patient online access to their medical records and related health information. However, the extensive literature searching shows no reports about patient-personalised search engines, within the reported PHR prototypes, that utilise a patient's own data to personalise the search features for a patient especially with regard to health information vocabulary needs. The thesis presents a novel approach to personalising patient information searching based on linking EPR data with relevant Internet Information resources, integrating medical and lay perspectives in a diagnosis vocabulary that distinguishes between medical and lay information needs, and accommodating a variable perspective on online information quality. To demonstrate our research work, we have implemented a prototype online patient personal health information system, known as the Patient Health Base (PHB) that offers a patient a Summary Medical Record (SMR) and a Personal Internet Search (PerlS) service. PerlS addresses patient Internet search challenges identified in the project. Evaluation of PerlS's approach to improving a patient's medical Internet searching demonstrated improvements in terms of search capabilities, focusing techniques and results. This research explored a new direction for patient Internet searching and foresees a great potential for further customising Internet information searching for patients, families and the public as a whole

Leveraging Semantic Annotations for Event-focused Search & Summarization

Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.Im heutigen Big Data Zeitalters existieren überwältigende Mengen an Textinformationen, die über mehrere Quellen verteilt sind und ein hohes Maß an Redundanz haben. Durch diese Gegebenheiten ist eine Retroperspektive auf vergangene Ereignisse für Konsumenten nur schwer möglich. Eine plausible Lösung ist die Verknüpfung semantisch ähnlicher, aber über mehrere Quellen verteilter Informationen, um dadurch eine Struktur zu erzwingen, die mehrere Zugriffspfade auf relevante Informationen, bietet. Vor diesem Hintergrund benutzt diese Dissertation Wikipedia und Onlinenachrichten als zwei prominente, aber dennoch grundverschiedene Informationsquellen, um die folgenden drei Probleme anzusprechen: • Wir adressieren ein Verknüpfungsproblem, um Wikipedia-Auszüge mit Nachrichtenartikeln zu verbinden und das Problem in eine Information-Retrieval-Aufgabe umzuwandeln. Unser neuartiger Ansatz integriert Zeit- und Geobezüge sowie Entitäten mit Text, um relevante Dokumente, die mit einem gegebenen Auszug verknüpft werden können, zu identifizieren. • Wir befassen uns mit einer unüberwachten Extraktionsmethode zur automatischen Zusammenfassung von Texten aus mehreren Dokumenten um Ereigniszusammenfassungen mit fester Länge zu generieren, was eine effiziente Aufnahme von Informationen aus großen Dokumentenmassen ermöglicht. Unser neuartiger Ansatz schlägt eine ganzzahlige lineare Optimierungslösung vor, die globale Inferenzen über Text, Zeit, Geolokationen und mit Ereignis-verbundenen Entitäten zieht. • Um den zeitlichen Fokus kurzer Ereignisbeschreibungen abzuschätzen, stellen wir einen semi-überwachten Ansatz vor, der die Redundanz innerhalb einer langzeitigen Dokumentensammlung ausnutzt, um genaue probabilistische Zeitmodelle abzuschätzen. Umfangreiche experimentelle Auswertungen zeigen die Wirksamkeit und Tragfähigkeit unserer vorgeschlagenen Ansätze zur Erreichung des größeren Ziels