Exploiting HPO to predict a ranked list of phenotype categories for livertox case reports

Casey Lynnette Overby, Louiqa Raschid, Hongfang D Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 semanticbased methods for case report retrieval and ranking.

Original languageEnglish (US)
Title of host publicationData Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers
PublisherSpringer Verlag
Pages3-9
Number of pages7
Volume10186 LNCS
ISBN (Print)9783319577401
DOIs
StatePublished - 2017
Event2nd 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 - New Delhi, India
Duration: Sep 5 2016Sep 9 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10186 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd 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
CountryIndia
CityNew Delhi
Period9/5/169/9/16

    Fingerprint

Keywords

  • Drug-induced liver injury
  • Phenotype category
  • Phenotypes
  • Semantic similarity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Overby, C. L., Raschid, L., & Liu, H. D. (2017). Exploiting HPO to predict a ranked list of phenotype categories for livertox case reports. In Data Management and Analytics for Medicine and Healthcare - 2nd International Workshop, DMAH 2016 Held at VLDB 2016, Revised Selected Papers (Vol. 10186 LNCS, pp. 3-9). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10186 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-57741-8_1