Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU.
J Stroke Cerebrovasc Dis
; 33(7): 107729, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38657830
ABSTRACT
BACKGROUND:
Acute kidney injury (AKI) is not only a complication but also a serious threat to patients with cerebral infarction (CI). This study aimed to explore the application of interpretable machine learning algorithms in predicting AKI in patients with cerebral infarction.METHODS:
The study included 3920 patients with CI admitted to the Intensive Care Unit and Emergency Medicine of the Central Hospital of Lishui City, Zhejiang Province. Nine machine learning techniques, including XGBoost, logistics, LightGBM, random forest (RF), AdaBoost, GaussianNB (GNB), Multi-Layer Perceptron (MLP), support vector machine (SVM), and k-nearest neighbors (KNN) classification, were used to develop a predictive model for AKI in these patients. SHapley Additive exPlanations (SHAP) analysis provided visual explanations for each patient. Finally, model effectiveness was assessed using metrics such as average precision (AP), sensitivity, specificity, accuracy, F1 score, precision-recall (PR) curve, calibration plot, and decision curve analysis (DCA).RESULTS:
The XGBoost model performed better in the internal validation set and the external validation set, with an AUC of 0.940 and 0.887, respectively. The five most important variables in the model were, in order, glomerular filtration rate, low-density lipoprotein, total cholesterol, hemiplegia and serum kalium.CONCLUSION:
This study demonstrates the potential of interpretable machine learning algorithms in predicting CI patients with AKI.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Infarto Cerebral
/
Valor Preditivo dos Testes
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Injúria Renal Aguda
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Aprendizado de Máquina
/
Unidades de Terapia Intensiva
Limite:
Aged
/
Aged80
/
Female
/
Humans
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Male
/
Middle aged
País/Região como assunto:
Asia
Idioma:
En
Revista:
J Stroke Cerebrovasc Dis
Assunto da revista:
ANGIOLOGIA
/
CEREBRO
Ano de publicação:
2024
Tipo de documento:
Article