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TopBackground
It has long been observed that a number of diseases and clinical conditions are more prevalent in some families than in others. In colloquial terms, it is said that such conditions “run in families”. It is now known that genetic are being passed from generation to generation. In fact, developments in Genetics have unveiled links between genetic factors and hundreds of common diseases, such as diabetes, Alzheimer, deafness, schizophrenia and many others (Kmiecik & Sanders, 2009; Rich et al., 2004). It is likely that efforts in human DNA sequencing and gene mutation identification, collection and interpretation, such as those carried out by Human Variome Project (2014) and Human Genome Project (U.S. Department of Energy Human Genome Project, 2014), will expose more and more links between genetic factors and medical conditions.
The significance of combining family history information with patients’ clinical data has also been recognized for a long time, before specific links between genetics and diseased were even established. Based solely on the observation of increased occurrence of certain medical conditions within families, Hippocrates (460 B.C. – 370 B.C.) reportedly included family history information in “case studies”, complementing the clinical evaluation of disease manifestations and providing an early form of risk stratification, (Hinton, 2008).
Naturally, a better understanding of the genetic nature of some conditions has increased the importance of family history information in modern healthcare. On the one hand, this information provides additional insight to patients’ clinical conditions, which may ease prognostics and promote optimal choice of treatment (Morales, Cowan, Dagua, & Hershberger, 2008; Rich et al., 2004). On the other hand, family history information allows the identification of at-risk individuals within the patient’s family, when genetic links exist, serving as a cost-effective risk assessment tool (R.L. Bennett, 2010). This discoverability of at-risk individuals fosters the application of predictive medicine, namely monitoring, counseling, genetic testing, suggestion of behavior changes or a combination of these, which can delay, diminish or completely avoid diseases or their symptoms (Frezzo, Rubinstein, Dunham, & Ormond, 2003).
Key Terms in this Chapter
Clinical Pedigree: A graphical representation of a clinical family history capable of representing the individuals belonging to a certain family and the family relations between them, as well as the relevant clinical and environmental information.
XML: Extensible Markup Language, a standard text-based format used to represent structured data.
DNA: Deoxyribonucleic acid, a molecule containing the genetic code of living organisms.
HL7: Health Level Seven International, a non-profit organization that develops standards for exchange, integration, sharing, and retrieval of electronic health information. The organization has created a number of standards suited for different health-related purposes, which are collectively known as HL7.
RDF: Resource Definition Framework, an XML-based representation of semantic data.
Ontology: A description of a knowledge domain in a machine-understandable format, using classes, attributes, relations, restrictions and logic rules.
OntoFam: An ontology-based clinical pedigree information system built using the principles described in this chapter. It allows the interactive creation and visualization of standards-based clinical pedigrees and uses Semantic Web technology to store, index, retrieve and reason with family history data.
Triple: The fundamental unit of semantic data representation. A triple is a statement asserting that an entity ( subject ) has a certain relation ( predicate ) with another entity ( object ).
Hemo@Care: A Healthcare Information System (HIS) built specifically for hemophilia care which has been integrated with OntoFam to provide clinical pedigree building tools.