4 results about "True positive rate" patented technology

A double cross-entropy loss function is a modification of the standard cross-entropy loss function that is tunable to penalize specific error types, i.e., false positives and false positives for binary classification. A prompt classifier is trained using the double cross-entropy loss function to classify prompts as malicious or benign. The double cross-entropy loss function for the prompt classifier is tuned so that false positive classifications are heavily penalized. The resulting trained prompt classifier maintains a high true positive rate while having a classification threshold that keeps the false positive rate very small. The trained prompt classifier is deployed in a high-load environment for prompt classification.

The present invention relates to a method and system for evaluating the performance of a SNP detection algorithm based on a latent class model, which belongs to the technical field of gene sequencing and solves the problem of inaccurate performance evaluation of a SNP detection algorithm when there is no gold standard. The method comprises: after obtaining the detection results of multiple SNP detection algorithms, integrating the detection results into binary type results; according to the binary type results of all SNP detection algorithms, evaluating the sensitivity and specificity of each SNP detection algorithm based on the latent class model under different assumptions; calculating the correlation residuals of paired SNP detection algorithms under different assumptions, determining the optimal assumption based on the correlation residuals, and obtaining the sensitivity and specificity ranking results of each algorithm under the optimal assumption. Accurate performance evaluation of the SNP detection algorithm is achieved.

The data compression analyzer can quickly make a binary decision to compress or not compress an input data block, or it can use a relatively slow neural network to predict the compression ratio of the data block through a regression model. The concentration (CV) and the number of zero-value (NZ) symbols are calculated based on the sum of squared frequencies from an unsorted symbol frequency table. A compression decision is made quickly when their product, CV*NZ, exceeds a level threshold THH. During training, CV*NZ is plotted as a function of the compression ratio C% for multiple training data blocks. Different THH test values ​​are applied to this plot to determine the true positive rate and false positive rate, and are plotted as a receiver operating characteristic (ROC) curve. The point on the ROC curve with the highest Yoden index is selected as the optimal THH for future binary decisions.

The invention discloses a medical examination data anomaly detection system and method based on multi-algorithm fusion and dynamic early warning, and relates to the technical field of medical big data processing. The method comprises the following steps: acquiring patient inspection data through a data interface layer, and converting unstructured data into a standardized entity object by utilizing an ORM mapping technology; constructing a multi-algorithm service pool, and integrating a plurality of detection service classes including threshold statistics, time sequence prediction (ARIMA / Prophet) and machine learning (SVM / random forest); dynamically loading algorithm parameters through an early warning rule configuration service, and calling a selected algorithm service in parallel by using a strategy mode to obtain a preliminary anomaly judgment result; a weighted voting mechanism is adopted to fuse multi-source results, and an early warning threshold value is dynamically calculated in combination with historical data statistical characteristics based on a sliding window; and finally, the abnormal level and attribution analysis are displayed through a visual module. According to the method, the problem of a single model detection blind area is solved through a software engineering algorithm fusion architecture, and the sensitivity and the specificity of data anomaly detection are remarkably improved by utilizing dynamic threshold calculation realized by code logic.