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Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape.
Chatterjee, Avishek; Nardi, Cosimo; Oberije, Cary; Lambin, Philippe.
  • Chatterjee A; The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands.
  • Nardi C; Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50134 Florence, Italy.
  • Oberije C; The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands.
  • Lambin P; The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands.
J Pers Med ; 11(4)2021 Apr 14.
Article in English | MEDLINE | ID: covidwho-1186995
Semantic information from SemMedBD (by NLM)
1. patient care COEXISTS_WITH Isolation procedure
Subject
patient care
Predicate
COEXISTS_WITH
Object
Isolation procedure
2. patient care COEXISTS_WITH Isolation procedure
Subject
patient care
Predicate
COEXISTS_WITH
Object
Isolation procedure
ABSTRACT

BACKGROUND:

Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective.

METHODS:

We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise.

RESULTS:

Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis.

CONCLUSIONS:

Our synopses of these works make a compelling case for the utility of this nascent field of research.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials / Reviews / Risk factors Language: English Year: 2021 Document Type: Article Affiliation country: Jpm11040300

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials / Reviews / Risk factors Language: English Year: 2021 Document Type: Article Affiliation country: Jpm11040300