The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients.Existing methods depend on the judgment of doctors,the results are affected by many factors...The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients.Existing methods depend on the judgment of doctors,the results are affected by many factors such as doctors’knowledge and experience.The accuracy is difficult to guarantee and has a serious lag.In this paper,a mixture prediction model is proposed for perioperative adverse events of heart failure,which combined with the advantages of the Deep Pyramid Convolutional Neural Networks(DPCNN)and Extreme Gradient Boosting(XGBOOST).The DPCNN was used to automatically extract features from patient’s diagnostic texts,and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients,then the XGBOOST algorithm was used to construct the prediction model of heart failure.An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018.The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3%and 31%compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.展开更多
Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Consi...Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Considering thissituation, this paper proposes a power grid fault diagnosismethod based on a deep pyramid convolutional neural networkfor the alarm information set. This approach uses the deepfeature extraction ability of the network to extract fault featureknowledge from alarm information texts and achieve end-to-endfault classification and fault device identification. First, a deeppyramid convolutional neural network model for extracting theoverall characteristics of fault events is constructed to identifyfault types. Second, a deep pyramidal convolutional neuralnetwork model for alarm information text is constructed, thetext description characteristics associated with alarm informationtexts are extracted, the key information corresponding to faultsin the alarm information set is identified, and suspicious faultydevices are selected. Then, a fault device identification strategythat integrates fault-type and time sequence priorities is proposedto identify faulty devices. Finally, the actual fault cases and thefault cases generated by the simulation are studied, and theresults verify the effectiveness and practicability of the methodpresented in this paper.展开更多
基金This study was approved by the Ethics Committee of the First Affiliated Hospital of Army Medical University,PLA,and the Approved No.of ethic committee is KY201936This work is supported by the National Key Research&Development Plan of China(2018YFC0116704)in data collectionIn addition,it is supported by Chongqing Technology Innovation and application research and development project(cstc2019jscx-msxmx0237)in the design of the study.
文摘The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients.Existing methods depend on the judgment of doctors,the results are affected by many factors such as doctors’knowledge and experience.The accuracy is difficult to guarantee and has a serious lag.In this paper,a mixture prediction model is proposed for perioperative adverse events of heart failure,which combined with the advantages of the Deep Pyramid Convolutional Neural Networks(DPCNN)and Extreme Gradient Boosting(XGBOOST).The DPCNN was used to automatically extract features from patient’s diagnostic texts,and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients,then the XGBOOST algorithm was used to construct the prediction model of heart failure.An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018.The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3%and 31%compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.
基金the National Natural Science Foundation of China(51877079).
文摘Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Considering thissituation, this paper proposes a power grid fault diagnosismethod based on a deep pyramid convolutional neural networkfor the alarm information set. This approach uses the deepfeature extraction ability of the network to extract fault featureknowledge from alarm information texts and achieve end-to-endfault classification and fault device identification. First, a deeppyramid convolutional neural network model for extracting theoverall characteristics of fault events is constructed to identifyfault types. Second, a deep pyramidal convolutional neuralnetwork model for alarm information text is constructed, thetext description characteristics associated with alarm informationtexts are extracted, the key information corresponding to faultsin the alarm information set is identified, and suspicious faultydevices are selected. Then, a fault device identification strategythat integrates fault-type and time sequence priorities is proposedto identify faulty devices. Finally, the actual fault cases and thefault cases generated by the simulation are studied, and theresults verify the effectiveness and practicability of the methodpresented in this paper.
