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| Technical Name | Multi-Objective Time Series Early Prediction Techniquesthe Alarm System for Critical Care | ||
|---|---|---|---|
| Project Operator | National Yang Ming Chiao Tung University | ||
| Project Host | 曾新穆 | ||
| Summary | We have developed Multi-Objective Time Series Early Prediction Techniquesthe Alarm System for Critical Care, including early prediction algorithms based on reinforcement learningmulti-objective optimization. Our techniques have addressed the balance of accuracyearliness in critical carehave been published in top international journals. The proposed techniques have been transferred to a hospital with clinical trials, demonstrating technical innovationfeasibility. |
||
| Scientific Breakthrough | Our Multi-Objective Time Series Early Prediction TechniquesAlarm System for Critical Care have utilized snippet features from physiological signals to build Snippet Policy Network. With multi-objective optimization algorithmsintelligent agents, we have balanced the demand for accuracyearliness, prioritizing vital targets in critical care. Our techniques have surpassed existing methods, establishing global leadership. Clinical tests have confirmed feasibility with an AUC of 0.91. |
||
| Industrial Applicability | Our Multi-Objective Time Series Early Prediction TechniquesAlarm System for Critical Care can benefit the industry from various aspects. For medical institutions, it can assist physicians in executing medical interventions more efficiently for patients, it can improve survivalrecovery rates for the information service industry, it can be integrated with smart medicine systems for various applications, promoting the industrial valuescompetitiveness. |
||
| Keyword | Time Series Early Prediction Critical Care Smart Medicine Deep Learning Reinforcement Learning Multi-Objective Optimization Physiological Signal Electrocardiogram Anomaly Prediction | ||
| Notes | |||
- Contact
- Cindy Ko
- cindyko@nycu.edu.tw
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