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A Case-Based Assistant for Diagnosis and Analysis of Dysmorphic Syndromes

Evans, Carl David; (1995) A Case-Based Assistant for Diagnosis and Analysis of Dysmorphic Syndromes. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Dysmorphology is a field of medicine which has as one of its concerns the diagnosis of children born with multiple malformations. A pattern of malformations recognised as occurring together and thought to be pathogenetically related is collectively called a dysmorphic 'syndrome'. Abnormalities (dysmorphic features) may pertain to clinical, radiological, biochemical, histological or chromosomal defects. This diversity is reflected in the source of diagnostic expertise, which is relatively sparse and is provided by specialists from varying disciplines such as clinical medicine, genetics and radiology. Approximately 8 in 1000 of children are born with multiple malformations, and about half of these infants will be linked with a chromosomal disorder (diagnosed by performing a karyotype). Of the rest, diagnosis (to recognised syndromes) is more difficult and is a task performed by experienced specialists. Diagnosis is not always possible. About forty per cent of cases remain undiagnosed with respect to known disorders, and recognition of new syndromes is an important facet of dysmorphology. To assist such investigation, the physician has at hand reference sources such as journals, syndrome compendia, and more recently, computer databases. Whilst databases may assist diagnosis, the functions performed by specialists that invovle learning new syndromes have not been automated to any degree. This aspect provides the focus for this research. The diagnosis and learning tasks of dysmorphology map intuitively with ideas from artificial intelligence: case-based reasoning (CBR) and learning (CBL). The thesis reports on the utilisation of a case-based approach in order to develop a diagnostic aid with an explicit goal of automating the learning aspect of dysmorphology. The initial focus of the research concerns the development of a case-based learning algorithm which simulates the learning processes that exist in dysmorphology, and which provides the basis for a dynamic case-based architecture. The thesis proceeds in view of relevant problems and issues highlighted by these experiments when viewed in the context of developing a realistic model for a CBR system within dysmorphology. This includes an investigation of a model for similarity assessment and the interdependent design issues of a case representation and case memory. An interactive CBR model is proposed that by default assists in diagnosis through CBR, but further extends the scope of syndrome database through its learning capability.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: A Case-Based Assistant for Diagnosis and Analysis of Dysmorphic Syndromes
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Thesis digitised by ProQuest.
URI: https://discovery.ucl.ac.uk/id/eprint/10103811
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