Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.

ABSTRACT:

Background

Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications.

Objective

This study aimed to develop a model that accurately predicts what drug an endocrinologist would prescribe based on the current measurements. The goal was to create a system that would assist nonspecialists in choosing medications, thereby potentially improving diabetes treatment outcomes. Based on the performance of previous studies, we set a performance target of achieving a receiver operating characteristic area under the curve (ROC-AUC) above 0.95.

Methods

A transformer-based encoder-decoder model predicts whether 44 types of diabetes drugs will be prescribed. The model uses sequence

SUBMITTER: Kurasawa H 

PROVIDER: S-EPMC12148250 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature