13 results about "Receiver operating characteristic" patented technology

Systems and methods for predicting esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using machine learning are provided. An example system may obtain an electronic health record (EHR) dataset, identify missing values in the EHR dataset, and generate imputed values for the missing values using simple random sampling imputation. The system may train a model using an extreme gradient boosting algorithm and a training dataset including the EHR dataset to generate a trained model including multiple decision trees. Training the model includes tuning the model to achieve a greatest value of an area under a receiver operating characteristic curve associated with the model. The system may obtain a patient EHR dataset, generate a prediction associated with a risk of EAC and / or GCA by applying the trained model to the patient EHR dataset, and provide the prediction to a computing device to determine a patient treatment protocol.

The application provides an intercept rendezvous condition analysis method and system based on a binary decision tree, comprising: establishing a rendezvous condition mathematical model; setting intervals of a rendezvous angle, an initial relative distance and a target speed ratio of a spacecraft in the rendezvous condition mathematical model according to random distribution characteristics of measurement noise, target inclination and radial maneuver noise of a spacecraft detection device, and generating training data through a relative motion model; training a binary decision tree model by using the labeled training data, evaluating the obtained binary decision tree model according to a receiver operating characteristic (ROC) curve, and obtaining a trained binary decision tree model when the evaluation meets preset requirements; and extracting rendezvous condition rules from the binary decision tree according to constraint conditions and bifurcation endpoint data of each layer node of the trained binary decision tree model, and obtaining set-form rendezvous condition rules with interpretability through set intersection and union operations of intervals of each element in the rendezvous condition.

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.

The invention provides a multi-angle persistent chemical screening method based on a multi-task graph neural network, and belongs to the technical field of high-throughput screening for chemical risk management. The method aims at achieving synchronous, efficient and accurate screening of the related attributes of the durability of the chemicals. The method comprises the following steps: firstly, constructing a multi-angle chemical durability parameter data set; the method comprises the following steps: taking a molecular graph generated by a chemical structure as model input, constructing a model through a graph neural network, and finishing synchronous training by adopting a mean square error and a superposition loss function with positive sample weight cross entropy; evaluating the performance of the model through indexes such as a determination coefficient and an area under a subject working curve; and characterizing a model application domain by adopting structure-activity relationship morphology analysis. The model constructed by the research relates to more persistent parameters, is more comprehensive, larger in data volume and wider in coverage range, has good fitting ability, prediction ability, robustness and reliability, and can provide technical support for environmental risk assessment of persistent chemicals.

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.

A system for helping an operator to question a help-seeker is provided. The system includes: taking the positive probability of any second reference group included as a cut-off point, and performing a classification test on any first reference group to obtain a classification sensitivity and a classification specificity corresponding to any second reference group; based on the classification sensitivities and the classification specificities corresponding to a plurality of second reference groups amongst any first reference group, obtaining a receiver operating characteristic curve corresponding to any first reference group; according to the receiver operating characteristic curve, determining a target first reference group from the plurality of first reference groups; and according to the distance between a coordinate point corresponding to any second reference group amongst the plurality of second reference groups and a perfect classification coordinate point, determining a target question semantic meaning.

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.

The invention belongs to the field of electroencephalogram signal processing, nerve decoding, neuroinformatics and sensory stimulation recognition, and provides a dynamic time sequence decoding method for electroencephalogram signals under sensory stimulation, which comprises the following steps of: preprocessing the electroencephalogram signals acquired in different sensory stimulation processes; then slicing the preprocessed electroencephalogram data by time points, and constructing an instantaneous feature matrix; training a classifier on each time slice by adopting a support vector machine and calculating the area under the working characteristic curve of the subject; and recognizing a time window of which the classification performance is remarkably higher than a random level by combining replacement test based on clusters, so as to obtain characteristic nerve response modes induced by different sensory stimuli. According to the method, an electroencephalogram time sequence decoding method is introduced into sensory stimulation recognition for the first time, the nerve response process can be analyzed on the millisecond scale, a stimulation specific significant time window can be extracted, a universal stimulation recognition template is constructed, and automatic, objective and quantifiable sensory stimulation classification is achieved.