Hana Taijun, Tanaka Shota, Nejo Takahide, Takahashi Satoshi, Kitagawa Yosuke, Koike Tsukasa, Nomura Masashi, Takayanagi Shunsaku, Saito Nobuhito
Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Cancers (Basel). 2019 Feb 3;11(2):178. doi: 10.3390/cancers11020178.
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.
在进行医学研究时，人们期待有一个能够客观预测特定研究领域未来趋势的系统。本研究旨在建立一种新颖且通用的算法，用于预测神经肿瘤学的最新趋势。通过文本挖掘分析等计算排序方法，选取了79个神经肿瘤学研究领域。还选取了代表神经肿瘤学近期趋势的30种期刊。作为一个新概念，计算了每种期刊和领域每年的年度影响力（AI）（期刊发表的文章数量×期刊的影响因子）。该年度的AI指数（AII）定义为30种期刊的AI总和。对2008年至2017年79个领域的AII趋势进行了机器学习预测分析。使用实际过去的数据验证了预测的准确性。通过这种算法，预测了神经肿瘤学的最新趋势。结果，线性预测模型取得了相对较好的准确性。近期神经肿瘤学中预测最热门的领域包括一些有趣的新兴领域，如微环境和抗有丝分裂。这种算法可能是预测特定医学领域未来趋势的一种有效且通用的工具。
