Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The...

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Bibliographic Details
Published in:Medicine (Baltimore) 2024-07, Vol.103 (30), p.e38747
Main Authors: Bu, Zhi-Jun, Jiang, Nan, Li, Ke-Cheng, Lu, Zhi-Lin, Zhang, Nan, Yan, Shao-Shuai, Chen, Zhi-Lin, Hao, Yu-Han, Zhang, Yu-Huan, Xu, Run-Bing, Chi, Han-Wei, Chen, Zu-Yi, Liu, Jian-Ping, Wang, Dan, Xu, Feng, Liu, Zhao-Lan
Format: Article
Language:English
Subjects:
Aged
Algorithms
Female
Hospital Mortality
Humans
Hyperkalemia - diagnosis
Hyperkalemia - mortality
Intensive Care Units - statistics & numerical data
Machine Learning
Male
Middle Aged
Neoplasms - complications
Neoplasms - mortality
Observational Study
Prognosis
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Summary:This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The dataset was split into a training set (75%) and a validation set (25%). We used the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential predictors, which included clinical laboratory indicators and vital signs. Pearson correlation analysis tested the correlation between predictors. In-hospital death was the prediction target. The Area Under the Curve (AUC) and accuracy of the training and validation sets of 7 ML algorithms were compared, and the optimal 1 was selected to develop the model. The calibration curve was used to evaluate the prediction accuracy of the model further. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model interpretability. 496 patients with MTH in the Intensive Care Unit (ICU) were included. After screening, 17 clinical features were included in the construction of the ML model, and the Pearson correlation coefficient was
ISSN:0025-7974
1536-5964
1536-5964
DOI:10.1097/MD.0000000000038747