Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records.
Stud Health Technol Inform
; 316: 736-740, 2024 Aug 22.
Article
em En
| MEDLINE
| ID: mdl-39176900
ABSTRACT
This study leverages data from a Canadian database of primary care Electronic Medical Records to develop machine learning models predicting type 2 diabetes mellitus (T2D), prediabetes, or normoglycemia. These models are used as a basis for extracting counterfactual explanations and derive personalized changes in biomarkers to prevent T2D onset, particularly in the still reversible prediabetic state. The models achieve satisfactory performance. Furthermore, feature importance analysis underscores the significance of fasting blood sugar and glycated hemoglobin, while counterfactuals explanations emphasize the centrality of keeping body mass index and cholesterol indicators within or close to the clinically desirable ranges. This research highlights the potential of machine learning and counterfactual explanations in guiding preventive interventions that may help slow down the progression from prediabetes to T2D on an individual basis, eventually fostering a recovery from prediabetes to a normoglycemic state.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Estado Pré-Diabético
/
Diabetes Mellitus Tipo 2
/
Registros Eletrônicos de Saúde
/
Aprendizado de Máquina
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Stud Health Technol Inform
Ano de publicação:
2024
Tipo de documento:
Article