HPI-DHC at TREC 2018 Precision Medicine Track

Michel Oleynik, Erik Faessler, Arpita Kappattanavar, Benjamin Bergner, Harry Freitas da Cruz, Jan Philipp Sachs, Suparno Datta, Erwin Böttinger

Research output: Contribution to conferencePaperpeer-review

12 Scopus citations

Abstract

The TREC-PM challenge aims for advances in the field of information retrieval applied to precision medicine. Here we describe our experimental setup and the achieved results in its 2018 edition. We explored the use of unsupervised topic models, supervised document classification, and rule-based query-time search term boosting and expansion. We participated in the biomedical articles and clinical trials subtasks and were among the three highest-scoring teams. Our results showed that query expansion associated with hand-crafted rules contribute to better values of information retrieval metrics. However, the use of a precision medicine classifier did not show the expected improvement for the biomedical abstracts subtask. In the future, we plan to add different terminologies to replace hand-crafted rules and experiment with negation detection.

Original languageEnglish
StatePublished - 2018
Externally publishedYes
Event27th Text REtrieval Conference, TREC 2018 - Gaithersburg, United States
Duration: 14 Nov 201816 Nov 2018

Conference

Conference27th Text REtrieval Conference, TREC 2018
Country/TerritoryUnited States
CityGaithersburg
Period14/11/1816/11/18

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