Construction of Disease-Symptom Knowledge Graph from Web-Board Documents
Applied Sciences
; 12(13):6615, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1933961
ABSTRACT
Featured ApplicationAuthors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.The research aim is to construct a disease-symptom knowledge graph (DSKG) as a cause-effect knowledge graph containing disease-symptom relations as a cause-effect relation type determined from downloaded documents on medical web-board resources. Each disease-symptom relation connects a disease-name concept node (a causative-concept node) to a corresponding node having a group of correlated symptom-concept/effect-concept features as common symptom-concept/effect-concept features among some disease-name concepts. The DSKG benefits non-professionals in preliminary diagnosis through a recommender web-board. There are three main problems how to determine symptom concepts from sentences without annotation on the documents having disease-name concepts as the documents’ topic-names;how to determine the disease-symptom relations from the documents with/without complications;and how to construct the DSKG involving high dimensional symptom-concept features after union of the correlated symptom-concept groups. Therefore, we apply a word co-occurrence pattern including medical-symptom expressions from Wikipedia including MeSH and the Lexitron Dictionary to determine the symptom concepts. The Cartesian product is applied for automatic-supervised machine learning to determine the disease-symptom relation. We propose using Principal Component Analysis for constructing the DSKG by dimensionality reduction in the symptom-concept features with minimized information loss. In contrast to previous works, the proposed approach enables the DSKG construction with precise and concise representation scores of 7.8 and 9, respectively.
Sciences: Comprehensive Works; cause-effect relation; disease-symptom knowledge graph; word co-occurrence pattern; Semantic relations; Dictionaries; Principal components analysis; Webs; Disease; Knowledge; Documents; Nodes; Complications; Annotations; Machine learning; Coronaviruses; Knowledge representation; Semantics; COVID-19; Cartesian coordinates; Symi
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Applied Sciences
Year:
2022
Document Type:
Article
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