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

Yasunobu Nohara; Koutarou Matsumoto; Naoki Nakashima

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

Yasunobu Nohara, Koutarou Matsumoto, Naoki Nakashima

メディカル・インフォメーションセンター

研究成果: ジャーナルへの寄稿 › 学術誌 › 査読

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.

本文言語	英語
ページ（範囲）	1280
ジャーナル	Studies in Health Technology and Informatics
巻	245
出版ステータス	出版済み - 1月 2018

引用スタイル

@article{51d280f315bf49a9a692cce3a7073dd4,

title = "Extracting Predictive Indicator for Prognosis of Cerebral Infarction Using Machine Learning Techniques",

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.",

author = "Yasunobu Nohara and Koutarou Matsumoto and Naoki Nakashima",

year = "2018",

month = jan,

language = "English",

volume = "245",

pages = "1280",

journal = "Studies in Health Technology and Informatics",

issn = "0926-9630",

publisher = "IOS Press",

}

TY - JOUR

T1 - Extracting Predictive Indicator for Prognosis of Cerebral Infarction Using Machine Learning Techniques

AU - Nohara, Yasunobu

AU - Matsumoto, Koutarou

AU - Nakashima, Naoki

PY - 2018/1

Y1 - 2018/1

N2 - 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.

AB - 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.

M3 - Article

C2 - 29295365

SN - 0926-9630

VL - 245

SP - 1280

JO - Studies in Health Technology and Informatics

JF - Studies in Health Technology and Informatics

ER -

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

抄録

フィンガープリント

引用スタイル