Classification of Hypoglycemic Events in Type 1 Diabetes Using Machine Learning Algorithms.

ABSTRACT:

Introduction

To improve the utilization of continuous- and flash glucose monitoring (CGM/FGM) data we have tested the hypothesis that a machine learning (ML) model can be trained to identify the most likely root causes for hypoglycemic events.

Methods

CGM/FGM data were collected from 449 patients with type 1 diabetes. Of the 42,120 identified hypoglycemic events, 5041 were randomly selected for classification by two clinicians. Three causes of hypoglycemia were deemed possible to interpret and later validate by insulin and carbohydrate recordings: (1) overestimated bolus (27%), (2) overcorrection of hyperglycemia (29%) and (3) excessive basal insulin presure (44%). The dataset was split into a training (n = 4026 events, 304 patients) and an internal validation dataset (n = 1

SUBMITTER: Cederblad L 

PROVIDER: S-EPMC10203083 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature