Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks

TL;DR

This paper investigates the use of recurrent neural networks, especially Gated Recurrent Units with Decay, for classifying postoperative surgical site infections from blood measurements with missing data, highlighting effective imputation strategies.

Contribution

It introduces the application of a specialized RNN architecture designed for missing data in the context of infection detection from blood time series.

Findings

Gated Recurrent Units with Decay improve classification accuracy.

Imputation strategies significantly affect model performance.

RNN-based classifiers outperform traditional methods.

Abstract

Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete. Recurrent neural networks are a special class of neural networks that are particularly suitable to process time series data but, in their original formulation, cannot explicitly deal with missing data. In this paper, we explore imputation strategies for handling missing values in classifiers based on recurrent neural network (RNN) and apply a recently proposed recurrent architecture, the Gated Recurrent Unit with Decay, specifically designed to handle missing data. We focus on the problem of detecting surgical site infection in patients by analyzing time series of their blood sample measurements and we compare the results obtained with different RNN-based classifiers.