Flexible-Window Predictions on Electronic Health Records
Keywords:Electronic Health Records, Recurrent Neural Network, Generative Adversarial Networks
AbstractVarious types of machine learning techniques are available for analyzing electronic health records (EHRs). For predictive tasks, most existing methods either explicitly or implicitly divide these time-series datasets into predetermined observation and prediction windows. Patients have different lengths of medical history and the desired predictions (for purposes such as diagnosis or treatment) are required at different times in the future. In this paper, we propose a method that uses a sequence-to-sequence generator model to transfer an input sequence of EHR data to a sequence of user-defined target labels, providing the end-users with ``flexible'' observation and prediction windows to define. We use adversarial and semi-supervised approaches in our design, where the sequence-to-sequence model acts as a generator and a discriminator distinguishes between the actual (observed) and generated labels. We evaluate our models through an extensive series of experiments using two large EHR datasets from adult and pediatric populations. In an obesity predicting case study, we show that our model can achieve superior results in flexible-window prediction tasks, after being trained once and even with large missing rates on the input EHR data. Moreover, using a number of attention analysis experiments, we show that the proposed model can effectively learn more relevant features in different prediction tasks.
How to Cite
Gupta, M., Poulain, R., Phan, T.-L. T., Bunnell, H. T., & Beheshti, R. (2022). Flexible-Window Predictions on Electronic Health Records. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12510-12516. https://doi.org/10.1609/aaai.v36i11.21520
IAAI Technical Track on Emerging Applications of AI