Contributions to Proceedings:
M. Zlabinger, S. Hofstätter, N. Rekabsaz, A. Hanbury:
"DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations";
in: "Advances in Information Retrieval 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II",
Lecture Notes in Computer Science, vol 12036;
Springer Nature Switzerland AG 2021,
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.
Disease-symptom relationship Medical expert data annotation Disease-symptom information extraction
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