Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease

Abstract

Background This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). Methods This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. Results There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. Conclusions In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

A Clinical Decision Support System for Edge/Cloud ICU Readmission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence

“…LR recorded the highest AUC. In [30], the authors applied ML models with different feature selection methods to predict ICU mortality risk. A large hospital in Anhui provided data for 1628 patients with cardio-macrovascular disease in the ICU.…”

“…In [29], the authors used LR, and AUC recorded 70.6. In [30], the authors used RF, and AUC recorded 87. The authors applied fuzzy modeling in [31], and AUC recorded 72.…”

“…AUC=91 [26] Ensemble model 3326 from MMIC AUC= 70.95 [14] Gradient-boosted machine 24885 from US hospital AUC=70 [27] XGboost from US hospital AUC=65.91 [28] XGBoost 5791 COVID-19 hospitalizations AUC=91 [29] LR 58036 patients from hospital in Asia. AUC= 70.6 [30] RF 1628 patients from hospital in Anhui AUC=87 [31] fuzzy modeling 26655 patients from MIMICII AUC= 72 [33] CNN-LSTM MMICII AUC=82.1 tients, the model may not be able to accurately predict their risk of readmission. Therefore, researchers need to carefully consider these factors and take steps to mitigate their effects when redesigning our model in their studies.…”

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

“…In the health care field, artificial intelligence (AI) is characterized by data management and processing, offering new possibilities to the health care paradigm [ 24 ]. Some applications of AI in the health care domain include assessing tumor interaction processes [ 25 ], serving as a tool for image-based diagnostics [ 26 , 27 ], participating in virus detection [ 28 ], and, most importantly, as a statistical and predictive method [ 29 - 32 ].…”

Application of artificial intelligence in the management of patients with renal dysfunction

“…Additionally, Li et al. [ 17 ] successfully developed and validated machine learning models for predicting mortality in critically ill patients with Chronic Kidney Disease (CKD). Employing six machine learning approaches, the XGBoost model, with the highest AUC, stood out.…”