基于决策树分析模型大数据治理的危重病情AI预警OA

Objective To introduce 5G and big data governance technology to build an AI early warning system capable of processing multi-level and multi-type medical data in a timely manner,aiming to detect and warn critical illnesses at an early stage.Methods 5G modules were used to collect real-time bedside ICU data,which underwent standardized processing.Key features were selected using a decision tree algorithm,and both general and personalized early warning models for critical conditions were constructed by integrating recurrent neural networks(RNNs).The model's alerts were pushed in real-time to healthcare staff terminals via an integrated platform.Results A total of 4 053 patients were included(3 022 in the Manual Group and 1 031 in the AI Group).Compared to manual warning,the overall AI warning model significantly shortened the intervention initiation time(median:2.8 minutes vs.4.0 minutes,P<0.01),improved the intervention success rate(median:72.9%vs.52.6%,P<0.01),and reduced the complication rate(median:28.2%vs.42.7%,P<0.01).The model achieved an area under the curve(AUC)of 0.845,with sensitivity of 87.7%and specificity of 68.0%.After six months of learning and optimization,the model's AUC increased to 0.857.Conclusion The AI-based early warning model,built upon 5G technology and big data governance,can effectively achieve early warning of critical conditions in the ICU and improve clinical outcomes,and thus holds potential for clinical promotion.