| Autor: |
Devika Subramanian, Rona Sonabend, Ila Singh |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
JMIR Diabetes, Vol 9, p e53338 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2371-4379 |
| DOI: |
10.2196/53338 |
| Popis: |
BackgroundDiabetic ketoacidosis (DKA) is the leading cause of morbidity and mortality in pediatric type 1 diabetes (T1D), occurring in approximately 20% of patients, with an economic cost of $5.1 billion/year in the United States. Despite multiple risk factors for postdiagnosis DKA, there is still a need for explainable, clinic-ready models that accurately predict DKA hospitalization in established patients with pediatric T1D. ObjectiveWe aimed to develop an interpretable machine learning model to predict the risk of postdiagnosis DKA hospitalization in children with T1D using routinely collected time-series of electronic health record (EHR) data. MethodsWe conducted a retrospective case-control study using EHR data from 1787 patients from among 3794 patients with T1D treated at a large tertiary care US pediatric health system from January 2010 to June 2018. We trained a state-of-the-art; explainable, gradient-boosted ensemble (XGBoost) of decision trees with 44 regularly collected EHR features to predict postdiagnosis DKA. We measured the model’s predictive performance using the area under the receiver operating characteristic curve–weighted F1-score, weighted precision, and recall, in a 5-fold cross-validation setting. We analyzed Shapley values to interpret the learned model and gain insight into its predictions. ResultsOur model distinguished the cohort that develops DKA postdiagnosis from the one that does not (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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