Recovering Loss to Followup Information Using Denoising Autoencoders
This addresses a critical issue in healthcare research by improving data recovery for studies, though it appears incremental as it builds on existing autoencoder methods.
The paper tackles the problem of recovering loss to followup information in healthcare studies by proposing a model based on overcomplete denoising autoencoders, which outperforms state-of-the-art methods by up to 20% in some scenarios while preserving dataset utility.
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.