Telemonitoring is the use of electronic devices to remotely monitor patients. Taking the Parkinson's disease (PD) as an example, the use of at-home testing device (AHTD) enables remote, internet-based measurement of PD vocal symptoms. Translating AHTD measurement into a unified PD rating scale (UPDRS) through predictive analytics enables cost-effective, convenient, and close tracking of PD progression. Building a predictive model between AHTD measurement and UPDRS is not straightforward because PD patients are highly heterogeneous, which requires patient-specific models. Learning a patient-specific model faces the challenge of limited data. Transfer learning (TL) tackles this challenge by leveraging other patients' information to make up the data shortage when modeling a target patient. Among different TL methods, the category of parameter transfer methods is more appropriate for the telemonitoring application because it transfers patient-specific model parameters but not patients' data. However, existing parameter transfer methods fall short because not every other patient's information is helpful and blind transfer causes the problem of negative transfer. To tackle this limitation, we propose a positive TL (PTL) method. We provide an in-depth theoretical study on the risk and condition for negative transfer to happen, which further drive the development of novel PTL algorithms that are robust to negative transfer. We apply PTL to predict UPDRS of 42 PD patients using their AHTD vocal measurement. PTL achieves significantly better accuracy compared with single learning and one-model-fits-all approaches.
|Original language||English (US)|
|Journal||IEEE Transactions on Automation Science and Engineering|
|State||Accepted/In press - Jan 1 2018|
- Data models
- Machine learning
- Machine learning
- negative transfer
- Prediction algorithms
- Predictive models
- transfer learning (TL).
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering