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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/15262
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| タイトル: | Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View |
| 著者: | Luo, Wei
Phung, Dinh
Tran, Truyen
Gupta, Sunil
Rana, Santu
Karmakar, Chandan
Shilton, Alistair
Yearwood, John
Dimitrova, Nevenka
Tu Bao Ho, Svetha Venkatesh, Michael Berk |
| キーワード: | machine learning
clinical prediction rule
guideline |
| 発行日: | 2016-12-16 |
| 出版者: | JMIR Publications |
| 誌名: | Journal of Medical Internet Research |
| 巻: | 18 |
| 号: | 12 |
| 開始ページ: | e323 |
| DOI: | 10.2196/jmir.5870 |
| 抄録: | Background: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective: To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results: The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. |
| Rights: | (c) Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
| URI: | http://hdl.handle.net/10119/15262 |
| 資料タイプ: | publisher |
| 出現コレクション: | a10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
| ファイル |
記述 |
サイズ | 形式 |
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| 23952.pdf | | 720Kb | Adobe PDF | 見る/開く |
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