Abstract
The Intensive Care Unit (ICU) is an important unit for the rescue of critically ill patients in hospitals, and patient mortality is an important indicator to measure the level of ICU treatment. Currently, a variety of clinical scoring systems are used to evaluate the patient's condition and predict survival, but these systems require a lot of resources. However, due to the rapid development of artificial intelligence and deep learning, machine learning based methods have been used to study the survival prediction of ICU patients. Additionally, these methods have made significant progress, but there is still a distance from clinical application, and equally metric interpretability of the deep learning method is not very mature. Therefore, in this paper, we have proposed a predicting model for the life and death of ICU patients, which is primarily based on the Fuzzy ARTMAP model. With a thorough analysis of the existing ICU patient condition assessment and life and death prediction methods, we have observed that patient's ICU monitoring information performs integrated analysis and extracts features according to the clinical characteristics of physiological indicators. Finally, fuzzy ARTMAP neural network is used to predict the life and death of patients. Likewise, prediction results are combined with the clinical scoring system and logistic regression, artificial neural network, support vector machine, and AdaBoost. Experimental results of these algorithms were compared, which verifies that the proposed method has outperformed the existing model. The main purpose of the proposed mode is to design a life and death prediction method for ICU patients, which has high predictive performance and is an acceptable method for clinical medical staff, where ICU monitoring data is used. Experimental results show that the method proposed has achieved better prediction performance and accuracy ratio, which provide theoretical reference for clinical application.
Highlights
Critical Care Medicine is a discipline that studies the laws and characteristics of the development process of the human body to death caused by various injuries or illnesses and treats critically ill patients according to these laws and characteristics
In order to verify the impact of the three data preprocessing methods proposed in this article on the prediction results, the following will initially use the fuzzy ARTMAP neural network to predict the effects of the three data preprocessing methods: normal value filling method, mean value filling method, and binary filling method
We analyzed advantages and disadvantages of existing models and combined the characteristics of Intensive Care Unit (ICU) clinical monitoring data to propose a method for predicting the life and death of ICU patients, which is based on the fuzzy ARTMAP model
Summary
Critical Care Medicine is a discipline that studies the laws and characteristics of the development process of the human body to death caused by various injuries or illnesses and treats critically ill patients according to these laws and characteristics. E ICU is a specialized department in the hospital that focuses on monitoring and treating critically ill patients. It can provide timely standardized and high-quality medical monitoring and treatment techniques to patients whose organ and system dysfunction caused by various factors are life-threatening or potentially highly dangerous. E ICU applies the most cutting-edge diagnostic, monitoring, and treatment instruments and technologies to conduct uninterrupted, real-time quantitative and qualitative observations of patients’ disease conditions, and through effective treatment measures, to provide patients with more effective and high-level life support, improves the quality of life of Journal of Healthcare Engineering patients [2]. One of the important indicators is the effective evaluation of critically ill patients and the effective prediction of their prognostic status
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