Healthcare Risk Prediction on Data Streams Employing Cross Ensemble Deep Learning

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K11955
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 61030:Intelligent informatics-related
• Research Institution: Iwate Prefectural University
• Principal Investigator: FUJITA HAMIDO 岩手県立大学, 公私立大学の部局等, 特命教授 (30244990)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 機械学習 / 知能システム

Outline of Final Research Achievements

In this project, have used ensemble deep learning techniques by constructing Deep Neural Networks (DNNs) based on assembled CNN in architecture of two GPUs in cross layered connection. In addition, we have one GPU system, running as backup for training experiments using large scale data for comparison purpose. I could achieve good research results using zero shot learning on multi-variate data. Also, I have trained the deep learning architecture on dynamic data, and image data. The result was promising and therefore, we published it in International Journals.

Free Research Field

機械学習、知能システム、医療分析、予測

Academic Significance and Societal Importance of the Research Achievements

本研究の研究結果は、機械学習技術を用いた健康管理システムにおける早期予測、特に、心不全の早期予測に対して新たな知見を与えるものである。