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Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology

In conducting medical research, a system which can objectively predict the future trends of the given research field is awaited. This study aims to establish a novel and versatile algorithm that predicts the latest trends in neuro-oncology. Seventy-nine neuro-oncological research fields were selecte...

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Autores principales: Hana, Taijun, Tanaka, Shota, Nejo, Takahide, Takahashi, Satoshi, Kitagawa, Yosuke, Koike, Tsukasa, Nomura, Masashi, Takayanagi, Shunsaku, Saito, Nobuhito
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406908/
https://www.ncbi.nlm.nih.gov/pubmed/30717468
http://dx.doi.org/10.3390/cancers11020178
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author Hana, Taijun
Tanaka, Shota
Nejo, Takahide
Takahashi, Satoshi
Kitagawa, Yosuke
Koike, Tsukasa
Nomura, Masashi
Takayanagi, Shunsaku
Saito, Nobuhito
author_facet Hana, Taijun
Tanaka, Shota
Nejo, Takahide
Takahashi, Satoshi
Kitagawa, Yosuke
Koike, Tsukasa
Nomura, Masashi
Takayanagi, Shunsaku
Saito, Nobuhito
author_sort Hana, Taijun
collection PubMed
description In conducting medical research, a system which can objectively predict the future trends of the given research field is awaited. This study aims to establish a novel and versatile algorithm that predicts the latest trends in neuro-oncology. Seventy-nine neuro-oncological research fields were selected with computational sorting methods such as text-mining analyses. Thirty journals that represent the recent trends in neuro-oncology were also selected. As a novel concept, the annual impact (AI) of each year was calculated for each journal and field (number of articles published in the journal × impact factor of the journal). The AI index (AII) for the year was defined as the sum of the AIs of the 30 journals. The AII trends of the 79 fields from 2008 to 2017 were subjected to machine learning predicting analyses. The accuracy of the predictions was validated using actual past data. With this algorithm, the latest trends in neuro-oncology were predicted. As a result, the linear prediction model achieved relatively good accuracy. The predicted hottest fields in recent neuro-oncology included some interesting emerging fields such as microenvironment and anti-mitosis. This algorithm may be an effective and versatile tool for prediction of future trends in a particular medical field.
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spelling pubmed-64069082019-03-21 Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology Hana, Taijun Tanaka, Shota Nejo, Takahide Takahashi, Satoshi Kitagawa, Yosuke Koike, Tsukasa Nomura, Masashi Takayanagi, Shunsaku Saito, Nobuhito Cancers (Basel) Article In conducting medical research, a system which can objectively predict the future trends of the given research field is awaited. This study aims to establish a novel and versatile algorithm that predicts the latest trends in neuro-oncology. Seventy-nine neuro-oncological research fields were selected with computational sorting methods such as text-mining analyses. Thirty journals that represent the recent trends in neuro-oncology were also selected. As a novel concept, the annual impact (AI) of each year was calculated for each journal and field (number of articles published in the journal × impact factor of the journal). The AI index (AII) for the year was defined as the sum of the AIs of the 30 journals. The AII trends of the 79 fields from 2008 to 2017 were subjected to machine learning predicting analyses. The accuracy of the predictions was validated using actual past data. With this algorithm, the latest trends in neuro-oncology were predicted. As a result, the linear prediction model achieved relatively good accuracy. The predicted hottest fields in recent neuro-oncology included some interesting emerging fields such as microenvironment and anti-mitosis. This algorithm may be an effective and versatile tool for prediction of future trends in a particular medical field. MDPI 2019-02-03 /pmc/articles/PMC6406908/ /pubmed/30717468 http://dx.doi.org/10.3390/cancers11020178 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hana, Taijun
Tanaka, Shota
Nejo, Takahide
Takahashi, Satoshi
Kitagawa, Yosuke
Koike, Tsukasa
Nomura, Masashi
Takayanagi, Shunsaku
Saito, Nobuhito
Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology
title Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology
title_full Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology
title_fullStr Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology
title_full_unstemmed Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology
title_short Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology
title_sort mining-guided machine learning analyses revealed the latest trends in neuro-oncology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406908/
https://www.ncbi.nlm.nih.gov/pubmed/30717468
http://dx.doi.org/10.3390/cancers11020178
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