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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fontana, R.J.: Pathogenesis of idiosyncratic drug-induced liver injury and clinical perspectives. Gastroenterology 146, 914–928 (2014)
Lewis, J.H.: The art and science of diagnosing and managing drug-induced liver injury in 2015 and beyond. Clin. Gastroenterol Hepatol. 13, 2173–2189 (2015)
Licata, A.: Adverse drug reactions and organ damage: the liver. Eur. J. Intern. Med. 28, 9–16 (2016)
Hoofnagle, J.H., Serrano, J., Knoben, J.E., Navarro, V.J.: LiverTox: a website on drug-induced liver injury. Hepatology 57, 873–874 (2013)
Serrano, J.: LiverTox: an online information resource and a site for case report submission on drug-induced liver injury. Clin. Liver Dis. 4, 22–25 (2014)
Jinjuvadia, K., Kwan, W., Fontana, R.J.: Searching for a needle in a haystack: use of ICD-9- CM codes in drug-induced liver injury. Am. J. Gastroenterol. 102, 2437–2443 (2007)
Overby, C.L., Pathak, J., Gottesman, O., Haerian, K., Perotte, A., Murphy, S., Bruce, K., Johnson, S., Talwalkar, J., Shen, Y., et al.: A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury. J. Am. Med. Inform. Assoc. 20, e243–e252 (2013)
Overby, C.L., Weng, C., Haerian, K., Perotte, A., Friedman, C., Hripcsak, G.: Evaluation considerations for EHR-based phenotyping algorithms: a case study for drug-induced liver injury. AMIA J. Summits Transl. Sci. Proc. 2013, 130–134 (2013)
Yu, K., Zhang, J., Chen, M., Xu, X., Suzuki, A., Ilic, K., Tong, W.: Mining hidden knowledge for drug safety assessment: topic modeling of LiverTox as a case study. BMC Bioinformatics 15(Suppl. 17), S6 (2016)
Groza, T., Kohler, S., Moldenhauer, D., Vasilevsky, N., Baynam, G., Zemojtel, T., Schriml, L.M., Kibbe, W.A., Schofield, P.N., Beck, T., et al.: The human phenotype ontology: semantic unification of common and rare disease. Am. J. Hum. Genet. 97, 111–124 (2015)
Kohler, S., Doelken, S.C., Mungall, C.J., Bauer, S., Firth, H.V., Bailleul-Forestier, I., Black, G.C., Brown, D.L., Brudno, M., Campbell, J., et al.: The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, D966–D974 (2014)
Robinson, P.N., Kohler, S., Bauer, S., Seelow, D., Horn, D., Mundlos, S.: The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83, 610–615 (2008)
Robinson, P.N., Mundlos, S.: The human phenotype ontology. Clin. Genet. 77, 525–534 (2010)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of IJCAI-1995, Montreal, Canada, pp. 448–453 (1995)
Kohler, S., Schulz, M.H., Krawitz, P., Bauer, S., Dolken, S., Ott, C.E., Mundlos, C., Horn, D., Mundlos, S., Robinson, P.N.: Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am. J. Hum. Genet. 85, 457–464 (2009)
Masino, A.J., Dechene, E.T., Dulik, M.C., Wilkens, A., Spinner, N.B., Krantz, I.D., Pennington, J.W., Robinson, P.N., White, P.S.: Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology. BMC Bioinformatics 15, 248 (2014)
Schlicker, A., Domingues, F.S., Rahnenfuhrer, J., Lengauer, T.: A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics 7, 302 (2006)
Deng, Y., Gao, L., Wang, B., Guo, X.: HPOSim: an R package for phenotypic similarity measure and enrichment analysis based on the human phenotype ontology. PLoS One 10, e0115692 (2015)
Hoehndorf, R., Dumontier, M., Gkoutos, G.V.: Towards quantitative measures in applied ontology. CoRR, abs/1202.3602 (2012)
Hoofnagle, J.H.: Drug-Induced Liver Injury Network (DILIN). Hepatology 40, 773 (2004)
Wang, J.Z., Du, Z., Payattakool, R., Philip, S.Y., Chen, C.-F.: A new method to measure the semantic similarity of GO terms. Bioinformatics 23, 1274–1281 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-57741-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57740-1
Online ISBN: 978-3-319-57741-8
eBook Packages: Computer ScienceComputer Science (R0)