Earticle

초록

영어

Hospitals have accumulated large amounts of patient data, with each hospital’s data having unique characteristics and distributions. By leveraging this vast amount of data for machine learning, we can develop predictive models, such as those for predicting long-term outcomes in ischemic stroke patients and provide valuable information for treatment decisions. However, data privacy concerns prevent hospital data from being put together on a centralized server. This study investigates the applicability of federated learning for predicting long-term outcomes in ischemic stroke patients using data from Hallym University Sacred Heart Hospital in Pyeongchon and Hallym University Sacred Heart Hospital in Chuncheon. Patient outcomes are defined as favorable if the modified Rankin Scale (mRS) score is 0-2 and poor if the mRS score is 3-6. There are two tasks: one predicting patient outcomes at 3 months after stroke and the other predicting patient outcomes at 1 year after stroke. A simple deep neural networks model is used for implementation of the prediction model and the federated learning environment. In conclusion, the federated learning models using basic FedAVG and weighted averaging FedAVG achieved 99.4%-99.9% performance of traditional centralized learning models.

목차

Abstract
I. INTRODUCTION
II. METHODS
A. Deep Neural Network
B. Federated Learning
III. EXPERIMENTS
A. Datasets
B. Implementation Details
C. Experimental Results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Yun-Young Chang [ School of Computing Gachon University Gyeonggi-do, Korea ]
• Chaeyeon Lee [ School of Computing Gachon University Gyeonggi-do, Korea ]
• Minwoo Lee [ College of Medicine Hallym University Gyeonggi-do, Korea ]
• Sang-Woong Lee [ School of Computing Gachon University Gyeonggi-do, Korea ] Corresponding Author
• Wonjong Noh [ College of Information Science Hallym University Chuncheon, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004