Closing Gaps: An Imputation Analysis of ICU Vital Signs

Robin van de Water, Bert Arnrich

Keywords: Imputation, ICU, EHR, MIMIC, eICU, HiRID, vital signs, Benchmark, clinical, ml

TL;DR: We benchmark 15 different, including generative, imputation methods with 4 missingness patterns, on vital sign data from 3 ICU datasets..

Abstract:

As more ICU EHR data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, lacking 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 to 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 clinical prediction model performance by choosing the most accurate imputation technique. We introduce an extensible, 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.

__Software Repository: https://github.com/rvandewater/YAIB__

Submission Number: 52