Abstract:
:Data mining methods in biomedical research might benefit by combining genetic algorithms with domain-specific knowledge. The objective of this research is to show how the evolution of treatment rules for autism might be guided. The semantic distance between two concepts in the taxonomy is measured by the number of relationships separating the concepts in the taxonomy. The hypothesis is that replacing a concept in a treatment rule will change the accuracy of the rule in direct proportion to the semantic distance between the concepts. The method uses a patient database and autism taxonomies. Treatment rules are developed with an algorithm that exploits the taxonomies. The results support the hypothesis. This research should both advance the understanding of autism data mining in particular and of knowledge-guided evolutionary search in biomedicine in general.
journal_name
Health Informatics Jjournal_title
Health informatics journalauthors
Engle K,Rada Rdoi
10.1177/1460458215627187subject
Has Abstractpub_date
2017-03-01 00:00:00pages
56-68issue
1eissn
1460-4582issn
1741-2811pii
1460458215627187journal_volume
23pub_type
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更新日期:2009-03-01 00:00:00
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doi:10.1177/1460458210377468
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更新日期:2018-09-01 00:00:00
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journal_title:Health informatics journal
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journal_title:Health informatics journal
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