Proof-of-Concept Study of Using Supervised Machine Learning Algorithms to Predict Self-Care and Glycemic Control in Type 1 Diabetes Patients on Insulin Pump Therapy.
Endocr Pract
; 29(6): 448-455, 2023 Jun.
Article
en En
| MEDLINE
| ID: mdl-36898528
ABSTRACT
OBJECTIVE:
Using supervised machine learning algorithms (SMLAs), we built models to predict the probability of type 1 diabetes mellitus patients on insulin pump therapy for meeting insulin pump self-management behavioral (IPSMB) criteria and achieving good glycemic response within 6 months.METHODS:
This was a single-center retrospective chart review of 100 adult type 1 diabetes mellitus patients on insulin pump therapy (≥6 months). Three SMLAs were deployed multivariable logistic regression (LR), random forest (RF), and K-nearest neighbor (k-NN); validated using repeated three-fold cross-validation. Performance metrics included area under the curve-Receiver of characteristics for discrimination and Brier scores for calibration.RESULTS:
Variables predictive of adherence with IPSMB criteria were baseline hemoglobin A1c, continuous glucose monitoring, and sex. The models had comparable discriminatory power (LR = 0.74; RF = 0.74; k-NN = 0.72), with the RF model showing better calibration (Brier = 0.151). Predictors of the good glycemic response included baseline hemoglobin A1c, entering carbohydrates, and following the recommended bolus dose, with models comparable in discriminatory power (LR = 0.81, RF = 0.80, k-NN = 0.78) but the RF model being better calibrated (Brier = 0.099).CONCLUSION:
These proof-of-concept analyses demonstrate the feasibility of using SMLAs to develop clinically relevant predictive models of adherence with IPSMB criteria and glycemic control within 6 months. Subject to further study, nonlinear prediction models may perform better.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Diabetes Mellitus Tipo 1
/
Insulinas
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Endocr Pract
Asunto de la revista:
ENDOCRINOLOGIA
Año:
2023
Tipo del documento:
Article