ArticleBMC medical informatics and decision making2018
OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Crowdsourcing in health and medical research: a systematic review.Infectious diseases of poverty · 2020Pooled it
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- User-centered design of a web-based crowdsourcing-integrated semantic text annotation tool for building a mental health knowledge base.Journal of biomedical informatics · 2020Article
- Knowledge-Based Biomedical Data Science.Annual review of biomedical data science · 2020Article
- Factors Influencing Doctors' Participation in the Provision of Medical Services Through Crowdsourced Health Care Information Websites: Elaboration-Likelihood Perspective Study.JMIR medical informatics · 2020Article
- Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts.Bioinformatics (Oxford, England) · 2020Article
- ALOHA: developing an interactive graph-based visualization for dietary supplement knowledge graph through user-centered design.BMC medical informatics and decision making · 2019Article
- STAT: A Web-based Semantic Text Annotation Tool to Assist Building Mental Health Knowledge Base.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Introduction: selected extended articles from the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017).BMC medical informatics and decision making · 2018Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundThere is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature. In this work, we investigated crowdsourcing strategies to increase the number of ground truth annotations and improve the quality of the KB.
methodsWe developed a new release of the OC-2-KB system addressing key challenges in automatic KB construction. OC-2-KB automatically extracts semantic triples in the form of subject-predicate-object expressions from PubMed abstracts related to the obesity and cancer literature. The accuracy of the facts extracted from scientific literature heavily relies on both the quantity and quality of the available ground truth triples. Thus, we incorporated a crowdsourcing process to improve the quality of the KB.
resultsWe conducted two rounds of crowdsourcing experiments using a new corpus with 82 obesity and cancer-related PubMed abstracts. We demonstrated that crowdsourcing is indeed a low-cost mechanism to collect labeled data from non-expert laypeople. Even though individual layperson might not offer reliable answers, the collective wisdom of the crowd is comparable to expert opinions. We also retrained the relation detection machine learning models in OC-2-KB using the crowd annotated data and evaluated the content of the curated KB with a set of competency questions. Our evaluation showed improved performance of the underlying relation detection model in comparison to the baseline OC-2-KB.
conclusionsWe presented a new version of OC-2-KB, a system that automatically builds an evidence-based obesity and cancer KB from scientific literature. Our KB construction framework integrated automatic information extraction with crowdsourcing techniques to verify the extracted knowledge. Our ultimate goal is a paradigm shift in how the general public access, read, digest, and use online health information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.