Background
Critical values are commonly used in clinical laboratory tests to define health-related conditions of varying degrees. Knowing the values, people can quickly become aware of health risks, and the health professionals can take immediate actions and save lives.
Methods
In this paper, we propose a method that extends the concept of critical value to one of the most commonly used physiological signals in the clinical environment—Electrocardiogram (ECG). We first construct a mapping from common ECG diagnostic conclusions to critical values. After that, we build a 61-layer deep convolutional neural network named CardioV, which is characterized by an ordinal classifier.
Results
We conduct experiments on a large public ECG dataset, and demonstrate that CardioV achieves a mean absolute error of 0.4984 and a ROC-AUC score of 0.8735. In addition, we find that the model performs better for extreme critical values and the younger age group, while gender does not affect the performance. The ablation study confirms that the ordinal classification mechanism suits for estimating the critical values which contain ranking information. Moreover, model interpretation techniques help us discover that CardioV focuses on the characteristic ECG locations during the critical value estimation process.
Conclusions
As an ordinal classifier, CardioV performs well in estimating ECG critical values that can help people quickly identify different heart conditions. We obtain ROC-AUC scores above 0.8 for all four critical value categories, and find that the extreme values (0 (no risk) and 3 (high risk)) have better model performance than the other two (1 (low risk) and 2 (medium risk)). Results also show that gender does not affect the performance, and the older age group has worse performance than the younger age group. In addition, visualization techniques reveal that the model pays more attention to characteristic ECG locations.
Critical values are commonly used in clinical laboratory tests to define health-related conditions of varying degrees. Knowing the values, people can quickly become aware of health risks, and the health professionals can take immediate actions and save lives. In this paper, we propose a method that extends the concept of critical value to one of the most commonly used physiological signals in the clinical environment - Electrocardiogram (ECG). We first construct a mapping from common ECG diagnostic conclusions to critical values. After that, we build a 61-layer deep convolutional neural network named CardioV, which is characterized by an ordinal classifier. We conduct experiments on a recently released large ECG dataset, and demonstrate that CardioV achieves a mean absolute error of 0.4984 and a ROC-AUC score of 0.8735. In addition, we find that the model performs better for extreme critical values and the younger age group, while gender does not affect the performance. The ablation study confirms that the ordinal classification mechanism suits for estimating the critical values which contain ranking information. Moreover, model interpretation techniques help us discover that CardioV focuses on the characteristic ECG locations during the critical value estimation process.
The analysis and identification of the chemical composition of ancient glass are of great significance for the evolution of history through time and space. The chemical content of ancient glass will change due to weathering, so a comprehensive evaluation model for the chemical content of the ancient glass is proposed in this paper. In this paper, the chi-square test is used to analyze the four classification variables, and it is found that there are significant differences between the ornamentation and colour, and the chemical composition content before weathering is predicted. Then the system clustering is used, and a subclass division model is proposed for the clustering results. Finally, the sensitivity analysis of the classification results is carried out, and different degrees of disturbance items are added respectively to obtain different model accuracy rates. The results show that the sensitivity of the model is better. The results show that the model can be used to analyze the composition of ancient glass relics and achieve good results.
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