Recovering Loss to Followup Information Using Denoising Autoencoders

Abstract

Loss to followup is a significant issue in healthcare and has serious consequences for a study's validity and cost. Methods available at present for recovering loss to followup information are restricted by their expressive capabilities and struggle to model highly non-linear relations and complex interactions. In this paper we propose a model based on overcomplete denoising autoencoders to recover loss to followup information. Designed to work with high volume data, results on various simulated and real life datasets show our model is appropriate under varying dataset and loss to followup conditions and outperforms the state-of-the-art methods by a wide margin ($\ge 20\%$ in some scenarios) while preserving the dataset utility for final analysis.

arXiv commentCopyright IEEE 2017, IEEE International Conference on Big Data (Big Data)

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Cited by in corpus (2)

• Deep Learning on Image Denoising: An overview
• On the Consistency of a Random Forest Algorithm in the Presence of Missing Entries