Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU.

ABSTRACT:

Objective

Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care.

Materials and methods

Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing.

Re

SUBMITTER: Fitzgerald O 

PROVIDER: S-EPMC8324237 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature