A Deep Learning Based Predictive Model for Healthcare Analytics
Nguyen Tran, Carson K. Leung, Evan W.R. Madill, Phan Thai Binh
Abstract
Healthcare informatics is an interdisciplinary area where computer science, data science, cognitive science, informatics principles, and information technology meet to address problems and support healthcare, medicine, public health, and/or everyday wellness. In many medical and healthcare applications, having models that can learn from historical healthcare data or instances to make predictions on future instances is helpful. However, partially due to privacy issues, the availability of healthcare data to be learned may be limited. Hence, in this paper, we present a deep learning based predictive model for healthcare analytics. In particular, our model consists of an autoencoder (comprising an encoder and a decoder) and a predictor to make accurate predictions. It can learn from a few shots of historical healthcare data to make either binary or multi-label predictions. Evaluation results on real-life datasets demonstrates the effectiveness of our deep learning-based predictive model in supporting healthcare analytics.