Flexible-Window Predictions on Electronic Health Records

Abstract

Various 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.

Reliable prediction of childhood obesity using only routinely collected EHRs is possible

“…Our predictive model also demonstrates high accuracy for estimating the risk of obesity for the next 3 years across a wide age range from 2 to 7 years of age (Table 2). Indeed, our model provides the flexibility of learning from as much data as available for patients before the time of screening, which is a method referred to as a "flexible window design" that our team developed in a prior paper [27]. Because of this flexibility, it provides a tool to screen children at different ages and with enough of a time window before the development of obesity for preventive interventions to be effective.…”

“…We adopted our encoder-decoder 1 deep neural network model and the training procedure presented in detail in prior work [27, 18]. The encoder part consists of long short-term memory (LSTM) 2 cells and the decoder consists of a feed-forward network with two fully-connected layers.…”