Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.

ABSTRACT:

Aims

It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes.

Methods

In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical h

SUBMITTER: Cheheltani R 

PROVIDER: S-EPMC10631495 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature