Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning

TL;DR

This paper introduces novel aggregation methods for deep learning models that process raw ICU electronic health record data without variable selection or cleaning, improving patient mortality prediction accuracy.

Contribution

The authors develop and demonstrate new aggregation techniques enabling deep models to learn from raw, unprocessed EHR data, eliminating the need for variable selection or cleaning.

Findings

Achieved AUROC 0.87 at 48 hours on MIMIC-III dataset

Outperformed recent deep learning models for ICU mortality prediction

Effectively used 13,233 unprocessed variables in an interpretable manner

Abstract

Dynamic assessment of patient status (e.g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation. Extraction and cleaning of expert-selected clinical variables discards information and protracts collaborative efforts to introduce machine learning in medicine. We present improved aggregation methods for a flexible deep learning architecture which learns a joint representation of patient chart, lab and output events. Our models outperform recent deep learning models for patient mortality classification using ICU timeseries, by embedding and aggregating all events with no pre-processing or variable selection. Our model achieves a strong performance of AUROC 0.87 at 48 hours on the MIMIC-III dataset while using 13,233 unique un-preprocessed variables in an interpretable manner via hourly softmax aggregation. This demonstrates how our method can be easily combined with existing electronic health record systems for automated, dynamic patient risk analysis.