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Stroke Risk Prediction With Hybrid Deep Transfer Learning Framework
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DOI：10.1109/JBHI.2021.3088750.png)
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Abstract
En 中文
Stroke has become a leading cause of death and long-term disability in the world with no effective treatment. Deep learning-based approaches have the potential to outperform existing stroke risk prediction models, but they rely on large well-labeled data. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small pieces. In addition, the positive and negative instances of such data are extremely imbalanced. Transfer learning can solve small data issue by exploiting the knowledge of a correlated domain, especially when multiple source of data are available. In this work, we propose a novel Hybrid Deep Transfer Learning-based Stroke Risk Prediction (HDTL-SRP) scheme to exploit the knowledge structure from multiple correlated sources (i.e., external stroke data, chronic diseases data, such as hypertension and diabetes). The proposed framework has been extensively tested in synthetic and real-world scenarios, and it outperforms the state-of-the-art stroke risk prediction models. It also shows the potential of real-world deployment among multiple hospitals aided with 5 G/B5G infrastructures.
Keywords:
Hospitals
Stroke (medical condition)
Data models
Optimization
Hypertension
Predictive models
Generative adversarial networks
stroke risk prediction
transfer learning
generative adversarial networks
active learning
Bayesian optimization
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