Presentation information

Interactive Session

General Session » Interactive Session

[2Xin5] インタラクティブ1

Wed. Jun 9, 2021 5:20 PM - 7:00 PM Room X (Poster room 1)

[2Xin5-08] Building a model to predict proper journals for clinical researches by using machine learning platform, DataRobot

〇Yukari Tezuka1, Kaoru Hashizume1, Sadaoki Sakai1 (1.Chugai Pharmaceutical Co., Ltd.)

Keywords:Machine Learning, publication, lung cancer, DataRobot

It is important that clinical research results are published in appropriate medical journals at the appropriate time and widely disseminated to the medical community, but objective indicators for selecting the submission destination of medical journals are absent. Therefore, we aimed to generate a model by machine learning to select the most suitable predictive science journal for the submission of papers based on the characteristics of clinical research. We have created a database of information on the characteristics and articles of clinical studies in the 194 lung cancer fields published in the past. Data of 26 items such as research design and research scale were set as explanatory variables. The impact factor (IF) and the article rank (A / B / C / D) based on the IF were set as the information of the medical journals to be explained. Using DataRobot, a machine learning platform, we generated a model that predicts the IF or treatise rank of medical journals from the characteristics of clinical research. This made it possible to predict the IF and article rank of medical journals suitable for submission based on the characteristics of clinical research. The factors that determine the impact of the study are thought to change over time, so it is necessary to continue to verify the prediction model.

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