Extracting predictive indicator for prognosis of cerebral infarction using machine learning techniques

Yasunobu Nohara, Koutarou Matsumoto, Naoki Nakashima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMEDINFO 2017
Subtitle of host publicationPrecision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics
EditorsZhao Dongsheng, Adi V. Gundlapalli, Jaulent Marie-Christine
PublisherIOS Press
Number of pages1
ISBN (Electronic)9781614998297
DOIs
Publication statusPublished - Jan 1 2017
Event16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China
Duration: Aug 21 2017Aug 25 2017

Publication series

NameStudies in Health Technology and Informatics
Volume245
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
CountryChina
CityHangzhou
Period8/21/178/25/17

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All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Nohara, Y., Matsumoto, K., & Nakashima, N. (2017). Extracting predictive indicator for prognosis of cerebral infarction using machine learning techniques. In Z. Dongsheng, A. V. Gundlapalli, & J. Marie-Christine (Eds.), MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics (Studies in Health Technology and Informatics; Vol. 245). IOS Press. https://doi.org/10.3233/978-1-61499-830-3-1280