A Physician’s decision-making skills are directly related to the patient’s positive outcomes. Therefore, a wealth of medical knowledge and clinical experience are key assets for a physician to have. The goal here is...A Physician’s decision-making skills are directly related to the patient’s positive outcomes. Therefore, a wealth of medical knowledge and clinical experience are key assets for a physician to have. The goal here is to use historical clinical data and relationships processed by Artificial Intelligence (AI) techniques to aid physicians in their decision making process. Presenting this information in a Clinical Decision Support System (CDSS) is an effective means to consolidate decision results. The CDSS provides a large number of medical support functions to help clinicians make the most reasonable diagnosis and choose the best treatment measures. Initial results have shown great promise in accurately predicting Fibrosis Stage in Hepatitis patients. Utilizing this tool could mitigate the need for some liver biopsies in the more than 170 million Hepatitis patients worldwide. The prototype is extendable to accommodate additional techniques (for example genetic algorithms and logistics regression) and additional medical domain solutions (for example HIV/AIDS).展开更多
Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content....Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R<sup>2</sup> of the model was 0.746;the validation R2 was 0.720;and its RMSE was 0.452 g·kgˉ<sup>1</sup>. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.展开更多
Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for ...Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.展开更多
文摘A Physician’s decision-making skills are directly related to the patient’s positive outcomes. Therefore, a wealth of medical knowledge and clinical experience are key assets for a physician to have. The goal here is to use historical clinical data and relationships processed by Artificial Intelligence (AI) techniques to aid physicians in their decision making process. Presenting this information in a Clinical Decision Support System (CDSS) is an effective means to consolidate decision results. The CDSS provides a large number of medical support functions to help clinicians make the most reasonable diagnosis and choose the best treatment measures. Initial results have shown great promise in accurately predicting Fibrosis Stage in Hepatitis patients. Utilizing this tool could mitigate the need for some liver biopsies in the more than 170 million Hepatitis patients worldwide. The prototype is extendable to accommodate additional techniques (for example genetic algorithms and logistics regression) and additional medical domain solutions (for example HIV/AIDS).
文摘Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R<sup>2</sup> of the model was 0.746;the validation R2 was 0.720;and its RMSE was 0.452 g·kgˉ<sup>1</sup>. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.
基金TheNationalHighTechnologyResearchandDevelopmentProgramofChina (No .86 3 5 11 930 0 0 9)
文摘Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.
