Machine-learning models utilizing CYP3A4*1G show improved prediction of hypoglycemic medication in Type 2 diabetes.
Per Med
; 20(1): 27-37, 2023 01.
Article
em En
| MEDLINE
| ID: mdl-36382674
About 10% of adults around the world are living with Type 2 Diabetes (T2D). Due to the huge number of patients and the complexity of individual makeup, it is a challenge for doctors to prescribe appropriate hypoglycemic drugs. To aid prescribing, machine-learning models were developed to predict medication schemes based on patients' demographic information and laboratory test results. These models treat prediction as a multilabel classification problem, with each class of medication as a label. This work was designed to determine whether the introduction of genetic information would improve prediction performance. The machine-learning models were trained using datasets with and without genetic information and their performance was compared. The performance of the machine-learning models was improved by incorporating the SNP CYP3A4*1G into the datasets. Thus, this work demonstrates a novel strategy to improve the prediction of T2D hypoglycemic medication performance and provides new ideas for how to support the T2D health system with machine-learning techniques.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Diabetes Mellitus Tipo 2
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article