ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2016
Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Review
- Design considerations for a hierarchical semantic compositional framework for medical natural language understanding.PloS one · 2023Article
- An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records.BMC medical informatics and decision making · 2019Article
- Extended Analysis of Topological-Pattern-Based Ontology Enrichment.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.BMC medical informatics and decision making · 2018Article
- OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017Article
- Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.Journal of biomedical informatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Obesity is associated with increased risks of various types of cancer, as well as a wide range of other chronic diseases. On the other hand, access to health information activates patient participation, and improve their health outcomes. However, existing online information on obesity and its relationship to cancer is heterogeneous ranging from pre-clinical models and case studies to mere hypothesis-based scientific arguments. A formal knowledge representation (i.e., a semantic knowledge base) would help better organizing and delivering quality health information related to obesity and cancer that consumers need. Nevertheless, current ontologies describing obesity, cancer and related entities are not designed to guide automatic knowledge base construction from heterogeneous information sources. Thus, in this paper, we present methods for named-entity recognition (NER) to extract biomedical entities from scholarly articles and for detecting if two biomedical entities are related, with the long term goal of building a obesity-cancer knowledge base. We leverage both linguistic and statistical approaches in the NER task, which supersedes the state-of-the-art results. Further, based on statistical features extracted from the sentences, our method for relation detection obtains an accuracy of 99.3% and a f-measure of 0.993.
Identifiers
What OpenQuestion holds
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.