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2681. Enhancing Pandemic Evolution: A Simulation Modeling Study Utilizing German Multicenter Data to Unveil the Value of Federated Machine Learning
Invited abstract in session WB-15: COVID-19, stream OR in Health Services (ORAHS).
Wednesday, 10:30-12:00Room: 18 (building: 116)
Authors (first author is the speaker)
1. | Stefan Kempter
|
Technology, Management and Economics, DTU |
Abstract
The basic idea of Federated Machine Learning is to collect models rather than data centrally. We study the feasibility and the potential of the rather young research area, which has been proposed by Google in 2016, for digital Covid-19 diagnosis based on German multicenter data of 3,670 patients. Therefore, we compare Federated Machine Learning to traditional testing methods such as antigen or PCR (Polymerase chain reaction) tests on economical and operational dimensions. We aim to inform essential decisions regarding the choice of diagnostic methodology during the progression of a pandemic. Accordingly, we run a time dependent simulation for Federated Machine Learning to digitally diagnose Covid-19 and find a significant potential. The federated deep learning model with six clients and full access to all datapoints achieves an F1-score of 89.9 percent. For comparison, the centrally trained model reaches up to 92.5 percent. Our results highlight the potential of applying Federated Machine Learning to Covid-19 diagnosis. The study might therefore function as a benchmark for hospital managers to contribute to future research while maintaining governance of their data.
Keywords
- Machine Learning
- Health Care
- Management Information Systems
Status: accepted
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