Genetic disorder classification based on existing patient data
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2022-01-30
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en
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Abstract
Diagnosing genetic disorders can benefit patients in various ways. Most
importantly, a diagnosis makes it more possible to understand the symptoms
of a patient and find an appropriate treatment. However, a large portion
of the process of diagnosing is done manually by physicians, which makes
misclassification a considerable risk. Accurately automating part of the
process can reduce this risk significantly.
A tool that attempts to do so is the Phenomizer. This widely used
tool ranks diseases that describe the symptom set of the patient best, along
with a corresponding p-value. For this, comparisons are made between the
patient and disease profiles, which consist of all phenotypic abnormalities
associated with the corresponding disease. The similarity between these
patients is calculated using the Resnik score.
While the Phenomizer is widely used, its performance can still greatly be
improved. In order to do so, a new approach is introduced in this research. In
this new approach, new patients are compared to already diagnosed patients,
using the same similarity score as is used for the Phenomizer. This approach
thus needs patient data in order to function. This data is not yet publicly
available.
Results show that the new approach performs significantly better than
the Phenomizer. Where the Phenomizer has an accuracy of .400, the new
approach has an accuracy of .834. However, the data needed to apply this
approach to all syndromes is not yet available. This research shows the
importance of collecting the data needed in order to diagnose and treat
patients as good and early as possible.
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Faculteit der Sociale Wetenschappen