Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol.

ABSTRACT:

Introduction

Type 2 diabetes mellitus (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability.

Objective

The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient-level characteristics retrieved from a population health linked dataset.<

SUBMITTER: Neves AL 

PROVIDER: S-EPMC8327849 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature