Evidence map›Paper›PMID 28699547›Full record

ArticleBMC medical informatics and decision making2017

Lightweight predicate extraction for patient-level cancer information and ontology development.

Muhammad Amith, Hsing-Yi Song, Yaoyun Zhang, Hua Xu, Cui Tao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Extended Analysis of Topological-Pattern-Based Ontology Enrichment.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Muhammad AmithSchool of Biomedical Informatics, University of Texas Health Science Center, Fannin Street, Houston, USA.
Hsing-Yi SongSchool of Biomedical Informatics, University of Texas Health Science Center, Fannin Street, Houston, USA.
Yaoyun ZhangSchool of Biomedical Informatics, University of Texas Health Science Center, Fannin Street, Houston, USA.
Hua XuSchool of Biomedical Informatics, University of Texas Health Science Center, Fannin Street, Houston, USA.
Cui TaoSchool of Biomedical Informatics, University of Texas Health Science Center, Fannin Street, Houston, USA. cui.tao@uth.tmc.edu.

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
NIAID NIH HHS R01 AI130460
6 · The paper itself

Abstract

backgroundKnowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. We focus towards the development of ontologies for the public health domain and use patient-centric sources from MedlinePlus related to HPV-causing cancers.

methodsThis paper demonstrates the use of a lightweight open information extraction (OIE) tool to derive accurate knowledge triples that can lead to the seeding of an ontological knowledgebase. We developed a custom application, which interfaced with an information extraction software library, to help facilitate the tasks towards producing knowledge triples from textual sources.

resultsThe results of our efforts generated accurate extractions ranging from 80-89% precision. These triples can later be transformed to OWL/RDF representation for our planned ontological knowledgebase.

conclusionsOIE delivers an effective and accessible method towards the development ontologies.

Indexed as

Biological OntologiesMedlinePlusNatural Language ProcessingNeoplasmsPublic HealthHumansNatural language processingOntology learningOpen information extractionPublic healthSemi-automated ontology development

Identifiers

PMID28699547
PMCPMC5506564

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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.