Design of an Intelligent Nursing Information Management System for Critically Ill Patients in Neurosurgery

In hospitals, one of the dominant issues is the development of an accurate and precise nursing management system which is hard to implement due to the various problems in the implementation of the traditional manual system. For this purpose, we are to solve the imperfect functions of the traditional nursing information management system and the strong subjectivity and low accuracy of the way of manually judging the patient’s condition. Firstly, the Immune Genetic Algorithm (IGA) is used to optimize the Backpropagation Neural Network (BPNN). A mortality prediction model using IGA-BPNN is proposed. Secondly, a nursing information management system for critically ill patients in neurosurgery is designed. The IGA-BPNN prediction model is used as a part of the system to predict the mortality of critically ill patients. Finally, the performance of the predictive model and the system is tested using the Medical Information Mart for Intensive Care (MIMIC)-III data set design experiment. The results show the following: (1) Precision, Recall, and F1-score of mortality prediction using the IGA-BPNN model are 7.2%, 7.2%, and 7.3% higher than those of other prediction models. The designed model has better performance. (2) The comprehensive performance of the system during operation can reach the standard. The researched content aims to provide important technical support for the nursing information management of critically ill patients in neurosurgery and the intelligent analysis of patients’ condition.