Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

BACKGROUND Heart failure (HF) is a leading cause of morbidity and mortality among patients in intensive care units (ICUs), particularly those with chronic critical illness (CCI). OBJECTIVE We aimed to develop and validate a machine learning (ML) model to predict in-hospital mortality for in CCI patients with CCI and HF. METHODS Retrospective data encompassing medical records from over 200 hospitals were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU Collaborative Research Database (eICU-CRD). Patients diagnosed with CCI and HF at their first ICU admission were included. The MIMIC-III and -IV datasets were used as a derivation cohort, while that from eICU-CRD was employed as a validation cohort. Key predictive features were identified utilizing the recursive feature elimination with 10-fold cross-validation method. Subsequently, multiple ML algorithms were evaluated, including Random Forest, K-Nearest Neighbors, Support Vector Machine (SVM), Extreme Gradient Boosting, Naive Bayes, Light Gradient Boosting Machine, and Adaptive Boosting. The performance of the models was assessed via metrics such as area under the receiver operating characteristic curve (AUROC), decision curve analysis, accuracy, sensitivity, specificity, and F1 score. Furthermore, model interpretability was enhanced by applying the SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) methods, providing insights into the contribution of individual features to the predictive outcomes. RESULTS A total of 780 (males: 451 [57.8%]) and 610 (males: 343 [56.2%]) patients with CCI and HF were allocated to the derivation and validation cohorts, respectively. Eleven features were selected to develop the prediction models. Among all models, the SVM algorithm-based model demonstrated high predictive accuracy (derivation cohort: AUROC, 0.781; sensitivity, 0.739; specificity, 0.691; and F1 score, 0.613; validation cohort: AUROC, 0.683; accuracy, 0.645; sensitivity, 0.607; specificity, 0.656; and F1 score, 0.44). The SHAP and LIME analyses evaluated the feature contributions, highlighting Sequential Organ Failure Assessment score, oxyhemoglobin saturation, diastolic blood pressure, and systolic blood pressure as significant predictors of in-hospital mortality. CONCLUSIONS The SVM model developed in this study effectively predicts in-hospital mortality in patients with CCI and HF and can serve as a reliable tool for early intervention and improved patient management. Furthermore, this ML model combines high accuracy with interpretability, thereby substantially contributing to clinical predictive analytics.

show abstract

Monitoring of the trough concentration of valproic acid in pediatric epilepsy patients: a machine learning-based ensemble model

Chen,

Lin,

Chen

et al. 2024

Front. Pharmacol.

View full text Add to dashboard Cite

AimsFew personalized monitoring models for valproic acid (VPA) in pediatric epilepsy patients (PEPs) incorporate machine learning (ML) algorithms. This study aimed to develop an ensemble ML model for VPA monitoring to enhance clinical precision of VPA usage.MethodsA dataset comprising 366 VPA trough concentrations from 252 PEPs, along with 19 covariates and the target variable (VPA trough concentration), was refined by Spearman correlation and multicollinearity testing (366 × 11). The dataset was split into a training set (292) and testing set (74) at a ratio of 8:2. An ensemble model was formulated by Gradient Boosting Regression Trees (GBRT), Random Forest Regression (RFR), and Support Vector Regression (SVR), and assessed by SHapley Additive exPlanations (SHAP) analysis for covariate importance. The model was optimized for R2, relative accuracy, and absolute accuracy, and validated against two independent external datasets (32 in-hospital and 28 out-of-hospital dataset).ResultsUsing the R2 weight ratio of GBRT, RFR and SVR optimized at 5:2:3, the ensemble model demonstrated superior performance in terms of relative accuracy (87.8%), absolute accuracy (78.4%), and R2 (0.50), while also exhibiting a lower Mean Absolute Error (9.87) and Root Mean Squared Error (12.24), as validated by the external datasets. Platelet count (PLT) and VPA daily dose were identified as pivotal covariates.ConclusionThe proposed ensemble model effectively monitors VPA trough concentrations in PEPs. By integrating covariates across various ML algorithms, it delivers results closely aligned with clinical practice, offering substantial clinical value for the guided use of VPA.

show abstract

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

Cited by 3 publications

References 29 publications

XGBoost-Driven Insights: Enhancing Chronic Kidney Disease Detection

XGBoost-Driven Insights: Enhancing Chronic Kidney Disease Detection

Interpretable Machine Learning Models for Predicting In-Hospital Mortality in Patients with Chronic Critical Illness and Heart Failure: A Multicenter Study (Preprint)

Monitoring of the trough concentration of valproic acid in pediatric epilepsy patients: a machine learning-based ensemble model

Contact Info

Product

Resources

About