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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
Creator	Hanbury, Allan Hofstätter, Sebastian Zlabinger, Markus Rekabsaz, Navid
Source	PMC
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.
has issue date	2020-03-24(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45442-5_54
has license	no-cc
sha1sum (hex)	e8bdd3f7ac05774a68f3edae1a13f5ce2941e960
schema:url	https://doi.org/10.1007/978-3-030-45442-5_54
resource representing a document's title	DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
has PubMed Central identifier	PMC7148057
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:e8bdd3f7ac05774a68f3edae1a13f5ce2941e960#body_text
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