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Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs
- Source :
- JAMIA Open
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 2020.
-
Abstract
- Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.
- Subjects :
- 0301 basic medicine
AcademicSubjects/SCI01060
Computer science
precision medicine
Health Informatics
Specific knowledge
Machine learning
computer.software_genre
Domain (software engineering)
03 medical and health sciences
0302 clinical medicine
Named-entity recognition
Intelligence amplification
Similarity (psychology)
natural language processing
business.industry
Cognition
artificial intelligence
Term (time)
Subject-matter expert
machine learning
030104 developmental biology
030220 oncology & carcinogenesis
Artificial intelligence
AcademicSubjects/SCI01530
Brief Communications
AcademicSubjects/MED00010
business
computer
Natural language processing
Subjects
Details
- ISSN :
- 25742531
- Volume :
- 3
- Database :
- OpenAIRE
- Journal :
- JAMIA Open
- Accession number :
- edsair.doi.dedup.....d1382a42a09ad08ff07443319c7d0aa9