Extracting Predictive Indicator for Prognosis of Cerebral Infarction Using Machine Learning Techniques

Yasunobu Nohara, Koutarou Matsumoto, Naoki Nakashima

研究成果: ジャーナルへの寄稿記事

抄録

Identifying important predicative indicators for prognosis is useful since these factors help for understanding diseases and determining treatments for patients. We extracted important factors for prognosis of cerebral infarction from EHR. We analyzed EHR data of 1,697 patients with 1,602 variables using gradient boosting decision tree. Extracted factors include not only well-known factors such as NIHSS but also new factors such as albumin-globulin ratio.

元の言語英語
ページ(範囲)1280
ジャーナルStudies in Health Technology and Informatics
245
出版物ステータス出版済み - 1 2018

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Cerebral Infarction
Decision trees
Learning systems
Decision Trees
Globulins
Albumins
Machine Learning
Therapeutics

これを引用

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abstract = "Identifying important predicative indicators for prognosis is useful since these factors help for understanding diseases and determining treatments for patients. We extracted important factors for prognosis of cerebral infarction from EHR. We analyzed EHR data of 1,697 patients with 1,602 variables using gradient boosting decision tree. Extracted factors include not only well-known factors such as NIHSS but also new factors such as albumin-globulin ratio.",
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