Polyadic regression and its application to chemogenomics
Ioakeim Perros, Fei Wang, et al.
SDM 2017
Parkinson's disease (PD) is a chronic disease that develops over years and varies dramatically in its clinical manifestations. A preferred strategy to resolve this heterogeneity and thus enable better prognosis and targeted therapies is to segment out more homogeneous patient sub-populations. However, it is challenging to evaluate the clinical similarities among patients because of the longitudinality and temporality of their records. To address this issue, we propose a deep model that directly learns patient similarity from longitudinal and multi-modal patient records with an Recurrent Neural Network (RNN) architecture, which learns the similarity between two longitudinal patient record sequences through dynamically matching temporal patterns in patient sequences. Evaluations on real world patient records demonstrate the promising utility and efficacy of the proposed architecture in personalized predictions.
Ioakeim Perros, Fei Wang, et al.
SDM 2017
Jinghui Chen, Saket Sathe, et al.
SDM 2017
Zhi Qiao, Shiwan Zhao, et al.
IJCAI 2018
Bo Jin, Haoyu Yang, et al.
AAAI 2017