Deep State Space Models for Computational Phenotyping
Soumya Ghosh, Yu Cheng, et al.
ICHI 2016
Atrial fibrillation (AF) is a common cardiac arrhythmias, which increases the risk and severity of ischemic stroke. For predicting ischemic stroke in AF patients, a risk prediction model that can achieve both good model discrimination (e.g., A UC) and statistical significance ofpredictors is required in real clinical practices. In this paper, we propose a new bootstrap-based wrapper (Boots-wrapper) method of feature selection, and apply this method on Chinese Atrial Fibrillation Registry data to develop 1-year stroke prediction models in AF. The proposed method can heuristically search a subset of features to maximize the discrimination of the prediction model and minimize the penalty for the non-significant features. To achieve robust feature selection, we perform bootstrap sampling to get a more reliable estimate of the variation and significance statistics. The experimental results show that Boots-wrapper can balance model discrimination and statistical significance offeatures for developing AF stroke prediction models.
Soumya Ghosh, Yu Cheng, et al.
ICHI 2016
Kai Yang, Xiang Li, et al.
AAAI 2017
Tingyu Chen, Xiang Li, et al.
American Journal of Kidney Diseases
Bum Chul Kwon, Vibha Anand, et al.
IEEE TVCG