8 results about "Receiver operating characteristic" patented technology

The invention belongs to the technical field of bioinformatics and medical detection, and particularly relates to a marker combination for neuroblastoma (NB) noninvasive risk level grading, a prediction model, a prediction method and application thereof. The marker combination is used for determining the sex, determining whether the month age is greater than 18 months, determining whether plasma MYCN is amplified, determining whether tumors are metastatic, and determining the content of neuron-specific enolase and lactic dehydrogenase; a machine learning algorithm is used for constructing an NB noninvasive risk degree grading prediction model, the comprehensive performance of the random forest model is optimal, the area value under a subject working characteristic curve reaches 0.956, the sensitivity is 92.9%, the specificity is 82.1%, the accuracy rate is 87.5%, the Kappa value is 0.75, the F1 score is 0.881, and NB middle and low risk patients and NB high risk patients can be effectively distinguished; the NB non-invasive risk level grading prediction model constructed by the invention can quickly, accurately and non-invasively perform NB risk level grading, and has a relatively good clinical application value.

This invention provides an intelligent fault prediction and diagnosis method for chemical processes, comprising the following steps: S1. Data acquisition: Collecting monitorable data from chemical processes using chemical sensors; S2. Data preprocessing: Decomposing the collected data into spatial scales using variational mode decomposition to obtain the positional dependencies of different sensors, and iteratively optimizing the decomposition parameters using a beaver optimizer; S3. Constructing a deep learning model for time-frequency domain feature fusion: Based on a common convolutional block attention module, an improved feature-injected convolutional block attention module is proposed; based on a temporal convolutional neural network, an improved frequency-domain convolutional module is proposed to further extract features; S4. Deep learning model training: Using a cross-entropy loss function and an Adam optimizer, the preprocessed data is divided into training, validation, and test sets and input into the model for multiple training iterations; S5. Evaluation of diagnostic results: The diagnostic performance of the model is comprehensively evaluated using recall, F1 score, and area under the receiver operating characteristic curve, and the best-performing model is selected from multiple trained models for deployment. This method enables real-time extraction and fusion of time-frequency domain features from chemical sensors, achieving efficient fault prediction and diagnosis in chemical processes.

The present application relates to a kind of biomarkers, which is selected from one or more of the following: UBR2, NPY4R, KLRF2, HABP2, GLP1R, DR1, RUNX2, ARHGAP5, LINC02135, DDHD1, MIR4523, TC2N.The biomarker is especially used for diagnosing stroke and differential diagnosis of cerebral hemorrhage.The present application screens 12 characteristic biomarkers to construct model, adopts receiver operating characteristic (ROC) analysis to evaluate the performance of model, uses sklearn to calculate area under curve (AUC), the sensitivity and specificity of model reach 0.898, 0.818 respectively, AUC value is 0.913;While differential diagnosis of cerebral hemorrhage, sensitivity and specificity reach 0.864, 0-854 respectively, AUC value is 0.909.The present application combines 5hmC modification spectrum of plasma evDNA and machine learning algorithm for the first time, the characteristic biomarker and / or model screened has the advantages of strong specificity and high sensitivity, overcome the problem of low efficiency and poor accuracy in the process of identifying stroke and cerebral hemorrhage in prior art.

This invention proposes a clinical prediction model and design method for the probability of developing anal fistula in Crohn's disease patients, belonging to the field of clinical medical technology. The risk prediction model includes: an input module receiving patient parameters consisting of age at diagnosis, biological agent mode switching, and Montreal manifestation classification at initial diagnosis; a processing module connected to the input module, calculating the assessment and prediction probability of anal fistula in Crohn's disease patients based on the age at diagnosis, biological agent mode switching, and Montreal manifestation classification data at initial diagnosis, according to the following formula; and an output module connected to the processing module, outputting the risk probability of anal fistula in Crohn's disease patients obtained from the risk prediction model. The prediction model is validated on the training and validation sets using the area under the receiver operating characteristic (ROC) curve and a calibration curve to determine its discriminative power and calibration. It can accurately predict the risk of anal fistula in Crohn's disease patients before treatment.

The present application relates to the technical field of medical information processing and machine learning, and particularly relates to a cardiovascular disease prediction method based on cross combination strategy and dynamic weighted stacking. The method comprises the following steps: S1, collecting data and constructing a data set; S2, constructing a heterogeneous base model set according to the data set; S3, taking the area under the receiver operating characteristic curve as an evaluation index, and selecting 10 heterogeneous base models with the optimal performance; S4, assigning adaptive weights to the 10 heterogeneous base models selected in S3; S5, using the adaptive weights to weight and fuse the prediction probabilities of the 10 heterogeneous base models, and generating a meta-feature data set for training a meta-model; S6, selecting a meta-model, and training the meta-model using the meta-feature data set; S7, inputting data into the trained meta-model, and obtaining a prediction result according to the meta-model; and S8, analyzing the influence of each feature on the risk of disease. The present application solves the problem of insufficient feature correlation mining through full cross combination of multi-feature selection methods and multi-classifiers.

The application discloses a liver cancer risk data processing method and system based on machine learning, comprising the following steps: acquiring a sample data set containing multi-dimensional clinical test indexes; screening a cross-algorithm consistency feature set through multi-algorithm cross-validation; constructing multiple machine learning models in parallel; evaluating and verifying the models by using a receiver operating characteristic curve, a calibration curve and a decision curve, and screening an optimal model; and verifying the prediction performance of the model on different data subsets. The data processing method adopted by the application has good classification performance and generalization ability by deeply mining data features through a machine learning algorithm based on conventional clinical test indexes.

The present application belongs to the technical field of molecular medicine, and relates to a probe, a marker combination and a prediction method for non-invasive risk grading of neuroblastoma. The probe UUU-DZ-tFNA can detect UDG in vitro, cells, living bodies and plasma exosomes, and it is proved that UDG in plasma exosomes is closely related to the risk grading of NB. The plasma exosomes of NB patients are collected MYCN Whether to amplify, whether the tumor is metastatic, neuron-specific enolase content, lactate dehydrogenase content and plasma exosome UDG content are integrated, and a NB non-invasive risk grading prediction model is established through a machine learning algorithm. The optimal risk grading model is a neural network model. The area under the receiver operating characteristic curve of the combined model is improved by 5.2% compared with the optimal prediction result of the combined prediction model of the single clinical index, and the sensitivity and specificity are significantly improved. The present application has key clinical values for accurate grading of NB children, individualized treatment plan formulation and efficacy monitoring.

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.