Multi-Way adaptive Time Aware LSTM for irregularly collected sequential ICU data

CISSOKO Mamadou Ben Hamidou, Nicolas Lachiche

Abstract:

In personalized predictive medicine, accurately modeling a patient’s illness and care processes is essential,
given their inherent long-term temporal dependencies. However, electronic medical records contain episodic
and irregularly timed data due to patients visiting hospitals based on treatment needs, resulting in unique
patterns for each hospital stay. Consequently, when constructing a personalized predictive model, it is crucial
to consider these factors in order to accurately capture the patient’s health journey.

To address this challenge, we present a novel deep dynamic memory neural network called Multi-Way
adaptive Time Aware LSTM (MWTA-LSTM). The primary objective of MWTA-LSTM is to leverage medical
records, memorize illness trajectories and care processes, estimate current illness states, and predict future risks,
thereby providing a high level of precision and predictive power. To enhance its capabilities, MWTA-LSTM
extends the conventional Long Short-Term Memory (LSTM) model in two key ways. Firstly, it incorporates
frequency measurement and the most recent observation(last observation) to enhance personalized predictive
modeling of patient illnesses, enabling a more accurate understanding of the patient’s condition. Secondly,
it parameterizes the cell state to handle irregular timing effectively, utilizing both frequency measurement
and elapsed times. Furthermore, the model capitalizes on both to comprehend the impact of interventions
on the course of illness on the cell state, facilitating the memorization of illness courses and improving its
ability to capture the temporal dynamics of healthcare data, accommodating variations and irregularities in
event and observation timing. Lastly, we introduce a novel adaptive pooling strategy that specifically targets
and resolves outlier issues that can potentially occur during the analysis of EHR data. By incorporating these
features, MWTA-LSTM significantly improves its ability to capture the temporal dynamics of healthcare data,
accommodating variations and irregularities in event and observation timing.

We validate the effectiveness of our proposed model through empirical experiments conducted on two
real-world clinical datasets and three real-world time series datasets. Our results demonstrate the superiority
of MWTA-LSTM over current state-of-the-art models and other robust baselines. This showcases the potential
of MWTA-LSTM in advancing personalized predictive medicine by offering a more accurate and comprehensive
approach to modeling patient health trajectories