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An electronic health record (EHR) provides electronically maintained information about an individual's lifetime health status and health care. This replaces, or supplements, the paper medical record. Electronic Health Record (EHF) systems need to offer a flexible framework for recording the consultation process, and accommodate the individuality of the clinician as well as the patient (Dipak Kalra, UCL). For further details, and an overview of EHR systems, please refer to the tutorial below.
Throughout Europe statistical data on health and health care is collected at various organisational levels i.e. local, regional, and national. The comparison of data over organisational and national borders emphasises concerns over data reliability which may be associated with inconsistent definitions, inconsistent interpretations and practices, and variable coding systems. This NoE (Network of Excellence) held a Workshop in July 2004, which covered various aspects including the following presentation on Coding, Aggregation, and Agreement - information quality.
Ontology is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. To shed light on the things that make up the world and how they interrelate is to do the work of Ontology, and such work leads to greater and more accurate information about the world.
Text Mining is the discovery of new, previously unknown information, by automatic extraction from different resources. The extracted information is linked to form new facts, or to develop new hypotheses. By comparison, in web searches (e.g. Google searches), the user is looking for something that is already known, or partly known, but the location is unknown — web searches make use of text mining techniques. Data mining, on the other hand, finds patterns in large databases, whereas, in text mining the patterns are extracted from natural ’free’ language e.g. scientific papers. The goal of text mining is to discover hitherto unknown information.