Evidence map›Paper›PMID 37067865›Full record

ArticleJournal of medical Internet research2023

Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study.

Jeremy Costello, Manpreet Kaur, Marek Z Reformat, Francois V Bolduc

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Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

4 authors.

Jeremy CostelloDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0003-4671-3914
Manpreet KaurDepartment of Pediatrics, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0003-0435-3877
Marek Z ReformatDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0003-4783-0717
Francois V BolducDepartment of Pediatrics, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0002-6109-5467

Funding

CIHR
6 · The paper itself

Abstract

backgroundPatients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such group is individuals with neurodevelopmental disorders (NDDs) and their families. NDDs affect up to 18% of the population and have major social and economic impacts. The current limitations in communicating information for individuals with NDDs include the absence of shared terminology and the lack of efficient labeling processes for web resources. Because of these limitations, health professionals, support groups, and families are unable to share, combine, and access resources.

objectiveWe aimed to develop a natural language-based pipeline to label resources by leveraging standard and free-text vocabularies obtained through text analysis, and then represent those resources as a weighted knowledge graph.

methodsUsing a combination of experts and service/organization databases, we created a data set of web resources for NDDs. Text from these websites was scraped and collected into a corpus of textual data on NDDs. This corpus was used to construct a knowledge graph suitable for use by both experts and nonexperts. Named entity recognition, topic modeling, document classification, and location detection were used to extract knowledge from the corpus.

resultsWe developed a resource annotation pipeline using diverse natural language processing algorithms to annotate web resources and stored them in a structured knowledge graph. The graph contained 78,181 annotations obtained from the combination of standard terminologies and a free-text vocabulary obtained using topic modeling. An application of the constructed knowledge graph is a resource search interface using the ordered weighted averaging operator to rank resources based on a user query.

conclusionsWe developed an automated labeling pipeline for web resources on NDDs. This work showcases how artificial intelligence-based methods, such as natural language processing and knowledge graphs for information representation, can enhance knowledge extraction and mobilization, and could be used in other fields of medicine.

Indexed as

Natural Language ProcessingNeurodevelopmental DisordersAlgorithmsArtificial IntelligenceHumansKnowledge BasesPattern Recognition, Automatedaggregation operatorattention deficit hyperactivity disorderautism spectrum disorderintellectual disabilityknowledge graphnamed entity recognitionnatural language processingneurodevelopmental disorderstopic modeling

Identifiers

PMID37067865
PMCPMC10152329

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Registered trials

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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.