Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories

Autor:	Yahi, Alexandre, Vanguri, Rami, Elhadad, Noémie, Tatonetti, Nicholas P.
Rok vydání:	2017
Předmět:	Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Generative Adversarial Networks (GANs) represent a promising class of generative networks that combine neural networks with game theory. From generating realistic images and videos to assisting musical creation, GANs are transforming many fields of arts and sciences. However, their application to healthcare has not been fully realized, more specifically in generating electronic health records (EHR) data. In this paper, we propose a framework for exploring the value of GANs in the context of continuous laboratory time series data. We devise an unsupervised evaluation method that measures the predictive power of synthetic laboratory test time series. Further, we show that when it comes to predicting the impact of drug exposure on laboratory test data, incorporating representation learning of the training cohorts prior to training GAN models is beneficial. Comment: NIPS ML4H 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.00164 Zobrazit plný text záznamu View this record from Arxiv