Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.

<h4>Introduction</h4>Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of...

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Autores principales:	Noah Grodzinski, Ben Grodzinski, Benjamin M Davies
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ad61dd9efeb74e1d8b9866c822978d42
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spelling	oai:doaj.org-article:ad61dd9efeb74e1d8b9866c822978d422021-12-02T20:08:32ZCan co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.1932-620310.1371/journal.pone.0256997https://doaj.org/article/ad61dd9efeb74e1d8b9866c822978d422021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256997https://doaj.org/toc/1932-6203<h4>Introduction</h4>Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers' impact within the research field.<h4>Methods</h4>Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set.<h4>Results</h4>DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p<0.0001) between the true and predicted H-Index of each author. Thus, author impact can be accurately predicted using only the nature of an author's collaborations.<h4>Discussion</h4>Analysis of the neural network shows that the nature of collaboration strongly impacts an author's research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy.Noah GrodzinskiBen GrodzinskiBenjamin M DaviesPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0256997 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Noah Grodzinski Ben Grodzinski Benjamin M Davies Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.
description	<h4>Introduction</h4>Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers' impact within the research field.<h4>Methods</h4>Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set.<h4>Results</h4>DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p<0.0001) between the true and predicted H-Index of each author. Thus, author impact can be accurately predicted using only the nature of an author's collaborations.<h4>Discussion</h4>Analysis of the neural network shows that the nature of collaboration strongly impacts an author's research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy.
format	article
author	Noah Grodzinski Ben Grodzinski Benjamin M Davies
author_facet	Noah Grodzinski Ben Grodzinski Benjamin M Davies
author_sort	Noah Grodzinski
title	Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.
title_short	Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.
title_full	Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.
title_fullStr	Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.
title_full_unstemmed	Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.
title_sort	can co-authorship networks be used to predict author research impact? a machine-learning based analysis within the field of degenerative cervical myelopathy research.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ad61dd9efeb74e1d8b9866c822978d42
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Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.

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