Abstract
Drug-induced liver injury (DILI) is an uncommon but important and challenging adverse drug event developed following the use of drugs, both prescription and over-the-counter. Early detection of DILI cases can greatly improve the patient care as discontinuing the offending drugs is essential for the care of DILI cases. An online resource, LiverTox, has been established to provide up-to-date, comprehensive clinical information on DILI in the form of case reports. In this study, we explored the use of the Human Phenotype Ontology (HPO) to annotate case reports with HPO terms and to predict a ranked list of phenotype categories (describing patient outcomes) that is most closely matched to the HPO annotations that are attached to the case report. The prediction performance based on our method was found to be good to excellent for 67% of case reports included in this study, i.e., the phenotype category that was assigned to the report was among the Top 3 predicted phenotype category descriptions. Future directions would be to incorporate other annotations, laboratory findings, and the exploration of other semantic-based methods for case report retrieval and ranking.
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Overby, C.L., Raschid, L., Liu, H. (2017). Exploiting HPO to Predict a Ranked List of Phenotype Categories for LiverTox Case Reports. In: Wang, F., Yao, L., Luo, G. (eds) Data Management and Analytics for Medicine and Healthcare. DMAH 2016. Lecture Notes in Computer Science(), vol 10186. Springer, Cham. https://doi.org/10.1007/978-3-319-57741-8_1
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DOI: https://doi.org/10.1007/978-3-319-57741-8_1
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