TY - GEN
T1 - Extracting predictive indicator for prognosis of cerebral infarction using machine learning techniques
AU - Nohara, Yasunobu
AU - Matsumoto, Koutarou
AU - Nakashima, Naoki
N1 - Publisher Copyright:
© 2017 International Medical Informatics Association (IMIA) and IOS Press.
PY - 2017
Y1 - 2017
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.
UR - http://www.scopus.com/inward/record.url?scp=85040511402&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040511402&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-830-3-1280
DO - 10.3233/978-1-61499-830-3-1280
M3 - Conference contribution
C2 - 29295365
AN - SCOPUS:85040511402
T3 - Studies in Health Technology and Informatics
SP - 1280
BT - MEDINFO 2017
A2 - Gundlapalli, Adi V.
A2 - Marie-Christine, Jaulent
A2 - Dongsheng, Zhao
PB - IOS Press BV
T2 - 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
Y2 - 21 August 2017 through 25 August 2017
ER -