Early prediction of end-stage kidney disease using electronic health record data: a machine learning approach with a 2-year horizon.
JAMIA Open
; 7(1): ooae015, 2024 Apr.
Article
em En
| MEDLINE
| ID: mdl-38414534
ABSTRACT
Objectives:
In the United States, end-stage kidney disease (ESKD) is responsible for high mortality and significant healthcare costs, with the number of cases sharply increasing in the past 2 decades. In this study, we aimed to reduce these impacts by developing an ESKD model for predicting its occurrence in a 2-year period. Materials andMethods:
We developed a machine learning (ML) pipeline to test different models for the prediction of ESKD. The electronic health record was used to capture several kidney disease-related variables. Various imputation methods, feature selection, and sampling approaches were tested. We compared the performance of multiple ML models using area under the ROC curve (AUCROC), area under the Precision-Recall curve (PR-AUC), and Brier scores for discrimination, precision, and calibration, respectively. Explainability methods were applied to the final model.Results:
Our best model was a gradient-boosting machine with feature selection and imputation methods as additional components. The model exhibited an AUCROC of 0.97, a PR-AUC of 0.33, and a Brier score of 0.002 on a holdout test set. A chart review analysis by expert physicians indicated clinical utility. Discussion andConclusion:
An ESKD prediction model can identify individuals at risk for ESKD and has been successfully deployed within our health system.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
/
6_ODS3_enfermedades_notrasmisibles
Base de dados:
MEDLINE
Idioma:
En
Revista:
JAMIA Open
Ano de publicação:
2024
Tipo de documento:
Article