Prediction of complications of type 2 Diabetes: A Machine learning approach.
Diabetes Res Clin Pract
; 190: 110013, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-35870573
ABSTRACT
AIM:
To construct predictive models of diabetes complications (DCs) by big data machine learning, based on electronic medical records.METHODS:
Six groups of DCs were considered eye complications, cardiovascular, cerebrovascular, and peripheral vascular disease, nephropathy, diabetic neuropathy. A supervised, tree-based learning approach (XGBoost) was used to predict the onset of each complication within 5 years (task 1). Furthermore, a separate prediction for early (within 2 years) and late (3-5 years) onset of complication (task 2) was performed. A dataset of 147.664 patients seen during 15 years by 23 centers was used. External validation was performed in five additional centers. Models were evaluated by considering accuracy, sensitivity, specificity, and area under the ROC curve (AUC).RESULTS:
For all DCs considered, the predictive models in task 1 showed an accuracy > 70 %, and AUC largely exceeded 0.80, reaching 0.97 for nephropathy. For task 2, all predictive models showed an accuracy > 70 % and an AUC > 0.85. Sensitivity in predicting the early occurrence of the complication ranged between 83.2 % (peripheral vascular disease) and 88.5 % (nephropathy).CONCLUSIONS:
Machine learning approach offers the opportunity to identify patients at greater risk of complications. This can help overcoming clinical inertia and improving the quality of diabetes care.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Vasculares Periféricas
/
Diabetes Mellitus Tipo 2
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Diabetes Res Clin Pract
Assunto da revista:
ENDOCRINOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article