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

Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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Cancers 2019, 11(2), 178; https://doi.org/10.3390/cancers11020178
Received: 25 December 2018 / Revised: 24 January 2019 / Accepted: 30 January 2019 / Published: 3 February 2019
(This article belongs to the Special Issue Glioblastoma: State of the Art and Future Perspectives)
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Abstract

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. View Full-Text
Keywords: impact factor; machine learning; neuro-oncology; regression analysis; trend prediction; text-mining impact factor; machine learning; neuro-oncology; regression analysis; trend prediction; text-mining
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hana, T.; Tanaka, S.; Nejo, T.; Takahashi, S.; Kitagawa, Y.; Koike, T.; Nomura, M.; Takayanagi, S.; Saito, N. Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology. Cancers 2019, 11, 178.

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