In:
Neuro-Oncology, Oxford University Press (OUP), Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi61-vi61
Abstract:
The systems that can objectively predict the future trends of a particular research field are always anticipated while conducting medical research. Such systems also provide a considerable aid to researchers while determining and acquiring appropriate research budgets. This study intended to establish a novel and versatile algorithm that can predict the latest trends in neuro-oncology. Seventy-nine neuro-oncological research fields were selected using computational sorting methods, such as text-mining analyses, along with 30 journals that represent the recent trends in the neuro-oncology field. Further, the annual impact (AI) for each year with respect to each journal and field (number of articles published in the journal × the impact factor of the journal) was calculated as a novel concept. Subsequently, the AI index (AII) for the year was defined as the sum of the AIs for the aforementioned 30 journals. With respect to the aforementioned neuro-oncological research fields, the AII trends from 2008 to 2017 were subjected to machine learning predicting analyses. The prediction accuracy of the latest trends in neuro-oncology was validated using actual data obtained from previous studies. In particular, the linear prediction model achieved a relatively good accuracy. The most notable and latest predicted fields in neuro-oncology included some interesting emerging fields, such as microenvironment and anti-mitosis, as well as the already renowned fields, such as immunology and epigenetics. Furthermore, we retrospectively attempted an analysis of the fields different from neuro-oncology. Interestingly, as of 2008, the future emergence of the CRISPR-Cas9 gene editing system has been predicted using this system. Overall, the presented algorithm displays potential to be an effective and versatile tool for the prediction of future trends in a particular medical field.
Type of Medium:
Online Resource
ISSN:
1522-8517
,
1523-5866
DOI:
10.1093/neuonc/noz175.245
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2019
detail.hit.zdb_id:
2094060-9
