Closing Gaps: An Imputation Analysis of ICU Vital Signs

TL;DR

This paper compares various imputation methods for ICU vital signs to identify the most effective techniques, aiming to enhance the accuracy of clinical prediction models and promote better healthcare outcomes.

Contribution

It introduces a comprehensive benchmark with 15 imputation and 4 amputation methods for ICU vital signs, aiding researchers in selecting optimal data imputation techniques.

Findings

Identifies the most accurate imputation methods for ICU vital signs.

Provides a reusable benchmark for future research.

Highlights the impact of imputation choice on prediction performance.

Abstract

As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the data that could negatively impact prediction performance. Previous works have introduced numerous time-series imputation techniques. Nevertheless, more comprehensive work is needed to compare a representative set of methods for imputing ICU vital signs and determine the best practice. In reality, ad-hoc imputation techniques that could decrease prediction accuracy, like zero imputation, are still used. In this work, we compare established imputation techniques to guide researchers in improving the performance of clinical prediction models by selecting the most accurate imputation technique. We introduce an extensible and reusable benchmark with currently 15 imputation and 4 amputation methods, created for benchmarking on major ICU datasets. We hope to provide a comparative basis and facilitate further ML development to bring more models into clinical practice.