Recovering loss to followup information using denoising autoencoders


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 (≥ 20% in some scenarios) while preserving the dataset utility for final analysis.

In 2017 IEEE International Conference on Big Data (Big Data)
Lovedeep Gondara
Lovedeep Gondara
Research Scientist

My research interests include machine learning (deep learning) and statistics, with current research focus on large language models, differential privacy, and their applications to healthcare.