Work

An Algorithm for Retrospectively Detecting Missing Meal Records in Type 1 Diabetes Datasets

Year: 2025

Type: article

Abstract: Data-driven technologies are becoming widespread in Type 1 Diabetes (T1D) research to deliver personalized care to patients. Missing or erroneous meal records represent a major factor affecting the quality of T1D data and, therefore, the development of these tools. This work proposes an algorithm based on logistic regression to retrospectively detect meals in T1D data. The algorithm is developed using real data from supervised clinical trials. During testing, absolute detection delay was 15 min on average and recall was > 0.80 - results that remained consistent despite of the proportion of missed and erroneous meals. False positive detection increased from 0.33 events/day (when 33% of meals were missing) to 0.66 (when all meals were missing).

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.06.015

Authors: Jacopo Pavan, María F. Villa-Tamayo, Marc Breton

Institution: University of Virginia

Language: English

Topic: Diabetes Management and Research

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4412437054

FWCI: 0.606

Cites: 17

Cited by: 1

Related to: 10