Analysis of delirium prediction in the ICU based on the hybrid SGDCS-ANFIS approach

Yuan Tian, Yongquan Liang, Yufeng Chen, Hongyang Bian

Abstract: In recent years, intensive care unit (ICU) doctors have paid more attention to delirium. ICU patients have a high risk of delirium. Delirium can lead to serious adverse outcomes, but early diagnosis and prediction of delirium are very difficult and lack effective assessment tools. The causes of delirium are many and complex, and there is no definite prediction model. To solve this problem, this paper proposes a delirium prediction model based on a hybrid cuckoo search algorithm with stochastic gradient descent—the adaptive-network-based fuzzy inference system (SGDCS-ANFIS) approach. Thirty-five relevant indicators of 1072 ICU cases (536 delirium cases and 536 nondelirium cases) were selected to establish a delirium prediction model to judge whether patients tended to experience delirium. The experiments show that the delirium prediction model based on the hybrid SGDCS-ANFIS approach has better performance than traditional classification and prediction machine learning approaches, and the accuracy is improved to 73.02%. It can provide some reference for the prediction of delirium, promote early diagnosis, and provide knowledge for early intervention to improve the prognosis of ICU patients. Adding this delirium prediction model to the ICU protocol will potentially improve the treatment outcome, quality, and cost. Doctors can manage sudden symptoms more calmly, and patients will also benefit. By collecting the real-time data commonly used in electronic medical records of ICUs, the proposed delirium prediction model can be easily applied in hospitals.