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Jing Li, Arizona State University

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A Novel Positive Transfer Learning Model for Telemonitoring of Parkinson’s Disease
25 October 2018 from 4:00 PM to 5:00 PM
201 Thomas Building
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When learning a new skill, people can transfer their knowledge about other related skills they have grasped to expedite the learning. This extraordinary human ability has inspired the development of a class of statistical machine learning models called Transfer Learning (TL). When building a predictive model in a target domain, TL integrates data of the specific domain and knowledge transferred from other related source domains to mitigate sample size shortage of the target domain. TL provides an ideal model for Precision Medicine, in which patient-specific models are needed so that diagnosis and treatment can be customized for each patient’s unique characteristics. However, each patient only has limited data due to time or resource constraints. Transfer learning from other patients with a similar disease offers the possibility of building a robust model.

In this talk, I focus on presenting a Positive Transfer Learning (PTL) model developed to enable
patient-specific telemonitoring of the Parkinson’s Disease (PD) using remote sensing devices or

smartphones. An important problem that the existing TL literature has overlooked is “negative transfer”,referred to as the situation of worse performance of a TL model than a model without transferring from any source domain. We provide theoretical study on the risk of negative transfer, which further motivates the development of the PTL model that is robust to negative transfer. Telemonitoring is an emerging platform in health care that uses smart sensors to remotely monitor patient conditions. It provides logistic convenience and cost-effectiveness, allowing for close monitoring of disease progression and timely medical decision. I will present an application of PTL in telemonitoring of PD.

At the end of the talk, I will briefly present our developments of TL models in other health care
applications, including modality-wise missing data imputation for Alzheimer’s Disease early detection and learning discriminant subgraph classifiers for migraine diagnosis.
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