Research on Acute Stroke Prediction Based on Multi-source Temporal Data from Information Systems in Hospitals
Objective To develop a model capable of providing real-time predictions for acute stroke and assist doctors to improve the early identification of stroke.Methods We constructed real-time stroke prediction models by integrating continuous monitoring data from non-invasive smart beds with temporal data from multi-source hospital information systems.Artificial intelligence algorithms such as logistic regression,random forest,and gated recurrent units(GRU)were employed to build these models.The Shapley additive explanations values were used to rank the importance of predictive factors.Results The best performance for predicting transient ischemic attacks and acute ischemic stroke were achieved by the GRU model,with area under the receiver operating characteristic curve(AUROC)values of 0.79 and 0.94,respectively.For acute large vessel occlusive ischemic stroke,the random forest model demonstrated the best performance,with an AUROC of 0.89.Age,gender,medical history,along with blood pressure,respiration,and pulse rate were identified as significant factors influencing the occurrence of stroke.Conclusion This study effectively identified acute stroke by combining multimodal temporal data from hospital information systems,and revealed important predictive factors beyond common risk factors for stroke.
multi-source information systemsartificial intelligencestrokeprediction
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万方数据
