Abstract:
:Recently, to speed up the differential-diagnosis process based on symptoms and signs observed from an affected individual in the diagnosis of rare diseases, researchers have developed and implemented phenotype-driven differential-diagnosis systems. The performance of those systems relies on the quantity and quality of underlying databases of disease-phenotype associations (DPAs). Although such databases are often developed by manual curation, they inherently suffer from limited coverage. To address this problem, we propose a text-mining approach to increase the coverage of DPA databases and consequently improve the performance of differential-diagnosis systems. Our analysis showed that a text-mining approach using one million case reports obtained from PubMed could increase the coverage of manually curated DPAs in Orphanet by 125.6%. We also present PubCaseFinder (see Web Resources), a new phenotype-driven differential-diagnosis system in a freely available web application. By utilizing automatically extracted DPAs from case reports in addition to manually curated DPAs, PubCaseFinder improves the performance of automated differential diagnosis. Moreover, PubCaseFinder helps clinicians search for relevant case reports by using phenotype-based comparisons and confirm the results with detailed contextual information.
journal_name
Am J Hum Genetjournal_title
American journal of human geneticsauthors
Fujiwara T,Yamamoto Y,Kim JD,Buske O,Takagi Tdoi
10.1016/j.ajhg.2018.08.003subject
Has Abstractpub_date
2018-09-06 00:00:00pages
389-399issue
3eissn
0002-9297issn
1537-6605pii
S0002-9297(18)30269-6journal_volume
103pub_type
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