  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.
