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2020 | OriginalPaper | Chapter

DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations

Authors : Markus Zlabinger, Sebastian Hofstätter, Navid Rekabsaz, Allan Hanbury

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

The effective extraction of ranked disease-symptom relationships is a critical component in various medical tasks, including computer-assisted medical diagnosis or the discovery of unexpected associations between diseases. While existing disease-symptom relationship extraction methods are used as the foundation in the various medical tasks, no collection is available to systematically evaluate the performance of such methods. In this paper, we introduce the Disease-Symptom Relation Collection (dsr-collection), created by five physicians as expert annotators. We provide graded symptom judgments for diseases by differentiating between relevant symptoms and primary symptoms. Further, we provide several strong baselines, based on the methods used in previous studies. The first method is based on word embeddings, and the second on co-occurrences of MeSH-keywords of medical articles. For the co-occurrence method, we propose an adaption in which not only keywords are considered, but also the full text of medical articles. The evaluation on the dsr-collection shows the effectiveness of the proposed adaption in terms of nDCG, precision, and recall.

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Metadata
Title
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
Authors
Markus Zlabinger
Sebastian Hofstätter
Navid Rekabsaz
Allan Hanbury
Copyright Year
2020
Book
Advances in Information Retrieval

Print ISBN: 978-3-030-45441-8

Electronic ISBN: 978-3-030-45442-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-45442-5_54