摘要
Abstract
Objective To construct a health risk prediction model for patients in intensive care units(ICU)after discharge based on artificial intelligence and evaluate its performance as well as clinical application value.Methods The ICU patient health management information system was developed by adopting artificial intelligence technology.Clinical data of 164 patients who received treatment and were successfully discharged from the ICU of the First Affiliated Hospital of Xiamen University from January to December 2024 were collected through hospital information system interfaces and intelligent mobile terminals.The Python programming language was used for development,and machine learning methods were used for feature screening and model training.Data from January to June 2024 were used for developing an training set,while data from July to December 2024 were used for model validation.Logistic regression analysis was applied to screen forecasting factors,and a nomogram prediction model was constructed.Internal validation was performed using a self-service method,and the model performance was evaluated through calibration curves,receiver operating characteristic curves,and decision curves.Results The occurrence of infection in the ICU,tracheostomy during ICU stay,use of sedatives,use of vasoactive drugs,the Acute Physiology and Chronic Health EvaluationⅡscore within 24 hours of admission to the ICU,and the total length of stay in the ICU were all related to the health risks of ICU patients after discharge(P<0.05).The forecasting model demonstrated the areas under the curve of 0.935(95%CI:0.912～0.959)for the training set and 0.875(95%CI:0.822～0.927)for the validation set.The calibration curve indicated that the model had good calibration,and decision curve analysis confirmed that the model had high clinical application value.Conclusion The health risk prediction model for ICU patients after discharge based on artificial intelligence exhibits robust forecasting performance and clinical practicality,which can provide decision-making support for the health management of patients after discharge.关键词
重症监护室/人工智能/预测模型/健康风险/预后Key words
intensive care unit/artificial intelligence/prediction model/health risk/prognosis分类
医药卫生
