TY - GEN
T1 - Exploiting HPO to predict a ranked list of phenotype categories for livertox case reports
AU - Overby, Casey Lynnette
AU - Raschid, Louiqa
AU - Liu, Hongfang
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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 semanticbased methods for case report retrieval and ranking.
AB - 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 semanticbased methods for case report retrieval and ranking.
KW - Drug-induced liver injury
KW - Phenotype category
KW - Phenotypes
KW - Semantic similarity
UR - http://www.scopus.com/inward/record.url?scp=85018674699&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018674699&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-57741-8_1
DO - 10.1007/978-3-319-57741-8_1
M3 - Conference contribution
AN - SCOPUS:85018674699
SN - 9783319577401
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 9
BT - Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers
A2 - Yao, Lixia
A2 - Wang, Fusheng
A2 - Luo, Gang
PB - Springer Verlag
T2 - 2nd International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2016 held in conjunction with 42nd International Conference on Very Large Data Bases, VLDB 2016
Y2 - 5 September 2016 through 9 September 2016
ER -