Objective This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications(PCCs)for older patients.Methods This secondary ...Objective This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications(PCCs)for older patients.Methods This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing,China.Participants aged 60–90 years undergoing major non-cardiac surgery under general anesthesia.The primary outcome metric of the study was the occurrence of PCCs,according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria.The analysis metrics contained 67 candidate variables,including baseline characteristics,laboratory tests,and scale assessments.Results Our feature selection process identified key variables that significantly impact patient outcomes,including the duration of ICU stay,surgery,and anesthesia;APACHE-II score;intraoperative average heart rate and blood loss;cumulative opioid use during surgery;patient age;VAS-Move-Median score on the 1st to 3rd day;Charlson comorbidity score;volumes of intraoperative plasma,crystalloid,and colloid fluids;cumulative red blood cell transfusion during surgery;and endotracheal intubation duration.Notably,our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872.Conclusion We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.展开更多
As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symboli...As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.展开更多
Airway management is closely related to the safety and quality of anesthesia.In 2015,the Difcult Airway Society suggested that endotracheal intubation should beberoptic bronchoscope(FOB)-guided and blind intubation sh...Airway management is closely related to the safety and quality of anesthesia.In 2015,the Difcult Airway Society suggested that endotracheal intubation should beberoptic bronchoscope(FOB)-guided and blind intubation should be avoided.The supraglottic airway device(SAD)BlockBuster^(TM)with matched tracheal tube(Block-Buster^(TM)tracheal tube,BTT;Tuoren Medical Instrument Co.,Ltd.,Changyuan,Henan,China)is an improved second-generation SAD with intubation function.[1,2]Theberscope-monitored intubation via SAD BlockBuster^(TM)can make the whole process visible.So,we investigated the feasibility,success rate,and complications of the new intubation technique in a well-positioned SAD BlockBuster^(TM)by sequential case observation.展开更多
基金The trial protocol was approved by the Institutional Review Board of Peking University(approval number 00001052-11048)and the ethics committees of the five participating centerswas registered with the China Clinical Trial Registry(www.chictr.org.cn,identifier:Chi CTR-TRC-09000543)ClinicalTrials.gov(identifier:NCT01661907).
文摘Objective This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications(PCCs)for older patients.Methods This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing,China.Participants aged 60–90 years undergoing major non-cardiac surgery under general anesthesia.The primary outcome metric of the study was the occurrence of PCCs,according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria.The analysis metrics contained 67 candidate variables,including baseline characteristics,laboratory tests,and scale assessments.Results Our feature selection process identified key variables that significantly impact patient outcomes,including the duration of ICU stay,surgery,and anesthesia;APACHE-II score;intraoperative average heart rate and blood loss;cumulative opioid use during surgery;patient age;VAS-Move-Median score on the 1st to 3rd day;Charlson comorbidity score;volumes of intraoperative plasma,crystalloid,and colloid fluids;cumulative red blood cell transfusion during surgery;and endotracheal intubation duration.Notably,our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872.Conclusion We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
基金Supported in part by Natural Science Foundation of China(Grant Nos.51835009,51705398)Shaanxi Province 2020 Natural Science Basic Research Plan(Grant No.2020JQ-042)Aeronautical Science Foundation(Grant No.2019ZB070001).
文摘As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.
基金Capital’s Funds for Health Improvement and Research(No.2018-4-1116)
文摘Airway management is closely related to the safety and quality of anesthesia.In 2015,the Difcult Airway Society suggested that endotracheal intubation should beberoptic bronchoscope(FOB)-guided and blind intubation should be avoided.The supraglottic airway device(SAD)BlockBuster^(TM)with matched tracheal tube(Block-Buster^(TM)tracheal tube,BTT;Tuoren Medical Instrument Co.,Ltd.,Changyuan,Henan,China)is an improved second-generation SAD with intubation function.[1,2]Theberscope-monitored intubation via SAD BlockBuster^(TM)can make the whole process visible.So,we investigated the feasibility,success rate,and complications of the new intubation technique in a well-positioned SAD BlockBuster^(TM)by sequential case observation.
