288 results about "Risk stratification" patented technology

The invention discloses a cerebral hemorrhage prognosis analysis system fusing images and clinical data, and the system comprises the steps: carrying out the collection time point alignment of cerebral hemorrhage image data and clinical diagnosis and treatment data, and constructing a multi-modal time sequence association data set; hematoma region segmentation and surrounding structure compression analysis are carried out, and hematoma morphological features and placeholder effect features are extracted to form an image risk feature set; carrying out index fluctuation analysis and collaborative abnormal mode identification based on clinical data, and screening risk sensitive indexes with a linkage relationship to form a clinical risk factor set; consistency detection is carried out on the two types of risk features, deviation causes are traced, modal weight configuration is determined according to the consistency detection, differential fusion is executed, and a fusion prognosis feature vector is generated; two-dimensional risk layering of function prognosis and survival prognosis is carried out, the confidence coefficient is evaluated and predicted in combination with modal consistency and feature stability, a structured prognosis analysis report with a confidence level is output, and interpretable decision support is provided for prognosis evaluation of cerebral hemorrhage patients.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention discloses a supply chain quality collaboration method and system based on a block chain, and relates to the technical field of block chain and supply chain quality management, and the method comprises the steps: collecting quality events according to a batch quality collaboration baseline table, generating a credible collection proof package and a to-be-verified quality event queue, executing dynamic endorsement verification, and writing the dynamic endorsement verification into a quality event account book, outputting a batch state table and an under-chain evidence pointer; performing rapid detection and judgment based on the batch state table and the under-chain evidence pointer, generating a pre-release report and establishing a pre-release mapping table; and triggering a difference correction contract according to the pre-release mapping table, updating the batch state table, generating a collaborative work order, pushing a quality collaborative strategy instruction, and outputting a quality collaborative closed-loop record. According to the method, the risk feedback analysis result table is generated by auditing and evaluating the quality collaborative closed-loop record, the risk layering strategy table is revised, and the batch quality collaborative baseline table is updated, so that the key field constraint is continuously calibrated, and the traceability and consistency of collaborative processing are improved.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

The invention discloses a data-driven early-diabetic future diabetes-related disease progress risk prediction model and a construction method thereof, and the method comprises the steps: collecting clinical index data, including age, gender, BMI, WHR, HOMA-IR, HDL-C, TG, SBP, DBP, SCR and ALT, of early-diabetic patients in a training and verification queue; using an unsupervised soft clustering method combining dimension reduction based on UMAP, graph clustering and a Gaussian mixture model to identify the phenotypic heterogeneity of the prediabetes mellitus; obtaining the probability of the individual phenotype characteristics, evaluating the association between the probability and the development risk of the future diabetes related diseases in the early stage of diabetes, and constructing a development risk prediction model of the future diabetes related diseases in the early stage of urine diseases; and performing model optimization and robustness verification in the verification queue. According to the method, the heterogeneity of the prediabetes mellitus can be effectively identified, and accurate risk stratification and personalized prevention are realized.

The invention relates to application of tsRNA-3025a as a prognostic marker of acute myocardial infarction and a treatment target spot of myocardial ischemia reperfusion injury. A DNA (Deoxyribonucleic Acid) sequence corresponding to the tsRNA-3025a is shown as SEQ ID NO: 1: 5 '-ATCCTGCCGACTACGCCA-3'. The tsRNA-3025a can be used for treating acute myocardial infarction and myocardial ischemia reperfusion injury. In the aspect of prognosis, a detection kit is provided, and the risk of heart failure and short-term adverse events of a patient is evaluated by quantitatively detecting the expression level of the tsRNA. In the aspect of treatment, the invention provides the application of the anti-tagomir for inhibiting the function or expression of tsRNA-3025a in the preparation of the medicine for treating the myocardial ischemia reperfusion injury, and the anti-tagomir is subjected to specific chemical modification. A novel biomarker is provided for prognosis risk stratification of acute myocardial infarction, and an effective treatment strategy is provided for prevention and treatment of myocardial ischemia-reperfusion injury.

The invention relates to the technical field of medical information, and particularly discloses a blood transfusion strategy optimization system based on clinical indexes and molecular markers, which comprises the following steps: firstly, collecting clinical indexes of a patient and molecular marker data derived from circulating tumor cells; standardized integration is carried out to construct a multi-dimensional feature set reflecting correlation between clinical dynamics and molecular features; key association rules are identified through data mining, quantitative abnormal expression profiles and risk hierarchical labels are generated for each patient according to the key association rules, the blood transfusion urgency state is evaluated in combination with real-time data, physiological responses and risk changes under different blood transfusion schemes are deduced, and the optimal individual blood transfusion strategy parameters are solved. Performing deduction verification on the strategy by constructing a patient-specific virtual simulation scene, comparing the strategy with historical data to calculate an expected efficiency deviation degree, and outputting a final execution scheme subjected to optimization verification; according to the invention, the transformation from static threshold transfusion to individualized dynamic prediction and optimized transfusion is realized.

The invention relates to the technical field of medical image processing and artificial intelligence diagnosis, in particular to an oral and maxillofacial surgery image recognition and diagnosis method and system based on deep learning, and the method comprises the following steps: multi-modal image collection and cooperative preprocessing: collecting an oral and maxillofacial surgery CBCT image, a cone beam CT curved surface tomography image, an oral endoscope image and an ultrasonic image, a standardized multi-modal image data set is obtained through inter-modal registration and an adaptive enhancement algorithm; according to the method, a traditional diagnosis framework of'single-mode image + manual film reading 'is broken through, and a three-order diagnosis logic of'multi-mode image cooperative enhancement-cross-scale feature dynamic fusion-focus typing and risk hierarchical linkage' is innovatively provided; and accurate identification, typing and malignant transformation risk prediction of common oral and maxillofacial surgery diseases (such as jaw cyst, wisdom tooth impediment, temporomandibular joint disorder and maxillofacial tumor) are realized.

The invention provides a method and a system for predicting early gastric cancer prognosis by a circulating marker, and relates to the technical field of auxiliary diagnosis. The method comprises the following steps: performing multi-omics detection on a blood sample based on a preset sampling time sequence to obtain a multi-dimensional time sequence characteristic data set containing three groups of heterogeneous data of circulating tumor DNA, exosomes and protein markers; calculating a change slope and a fluctuation variance of the heterogeneous data in adjacent time sequence intervals, constructing a dynamic variation feature matrix in combination with a standard attenuation weighting factor, and deeply mining spatial cross-correlation and sequence dependence features of the matrix to generate a multi-modal fusion feature fingerprint; and performing regression operation on the feature fingerprints by using an integrated learning stack model to obtain a dynamic prognosis risk score, and further retrieving a risk hierarchical mapping table to generate a prognosis evaluation result containing a survival curve. According to the method, multi-modal heterogeneous data can be effectively fused, the biological dynamic characteristics in the tumor postoperative recovery phase are captured, and the accuracy and timeliness of early gastric cancer prognosis prediction are remarkably improved.

The invention discloses an early gastric cancer intelligent screening and risk layering method and system based on multi-modal data fusion and deep learning. The method comprises the following steps: acquiring a gastroscope image, a pathological image, a medical image and clinical data of a patient, performing standardization processing and forming unified feature representation; training the multi-branch neural network model group, and generating a virtual queue tag based on each mode; fusing a multi-modal queue division result to generate a patient comprehensive queue label; adaptively selecting a decision path according to the label, and executing feature fusion and analysis of corresponding depth; generating a structured report containing gastric cancer risk levels and individualized monitoring suggestions; and deploying the system and dynamically tracking the change of a patient queue to realize continuous monitoring of disease progression. Cross-modal semantic alignment, uncertainty evaluation and a Markov prediction model are introduced into the system, the screening accuracy, credibility and dynamic risk evaluation capability are improved, and the system is suitable for early screening and personalized management of gastric cancer.

The invention discloses an early pancreatic cancer prediction and risk stratification system based on artificial intelligence, and belongs to the technical field of medical health data analysis and artificial intelligence. The system comprises a multi-omics data adaptive fusion module, a longitudinal health trajectory coding module, a biomarker combination discovery module, a risk prediction and dynamic layering module and a closed-loop feedback optimization module, and a data confidence index generated by the multi-omics fusion module directly affects the attention weight of longitudinal trajectory coding. Longitudinal track coding adopts a bidirectional long-short-term memory network to extract time sequence characteristics, a biomarker discovery module recognizes a synergistic marker combination through a Transform mechanism, a closed-loop feedback module dynamically adjusts parameters of each module according to a prediction result, and clinical verification shows that the prediction accuracy of the system reaches 85%, the I-stage diagnosis rate is improved by 60%, diseases are discovered 8-12 months in advance, and the diagnosis efficiency is improved. The method is obviously superior to the prior art.

The present invention relates to a system and a method for measuring and analyzing minimal residual disease (MRD) in pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL) using multiparameter flow cytometry (MPFC). The invention finds application in clinical diagnostics and hematology-oncology for quantifying MRD in B-ALL patients with high sensitivity and specificity, needed for risk stratification, monitoring treatment response, and informing therapeutic decisions. The system comprises interconnected subsystems including an acquisition subsystem with an MPFC instrument, a control and file generation subsystem, and an analytical subsystem. The analytical subsystem incorporates modules for sequential data reduction, automated data cleaning, automated unsupervised data clustering, and interactive cluster analysis. Key advantages include high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity, without reliance on reference samples or supervised machine learning models, making it applicable in laboratories with different measuring equipment and using different panels of antibodies for identification of leukemic cells.

The invention relates to the technical field of medical artificial intelligence diagnosis, and discloses a diabetic nephropathy auxiliary identification method based on multi-source data. The method comprises the following steps: starting a medical data traceability process, capturing original indexes from a multi-heterogeneous system, and arranging the original indexes into an initial data set; and performing hierarchical data fusion according to the pathological evolution knowledge graph to generate a fusion health record with a timestamp. And introducing a dynamic feature weaving process, and extracting a multi-level abnormal feature network from the physiological parameter trajectory according to the priority of the biomarker. And importing the abnormal feature network into a progressive mode learning process, training an identification engine for mapping feature combinations to different disease risk levels, and outputting an identification conclusion by the engine. According to the method, the structured abnormal mode network is constructed through priority-driven dynamic feature weaving, and the recognition engine is trained by using a progressive learning mechanism, so that the accuracy and clinical interpretability of risk stratification of diabetic nephropathy are improved.

The invention provides a full-period follow-up visit patient risk dynamic management method based on time series data analysis. The method comprises the steps of performing standardization processing on multi-source follow-up visit data of a patient; data acquisition is dynamically optimized through an adaptive strategy based on reinforcement learning, and multi-scale time sequence features are extracted; performing dynamic risk assessment by using the attention mechanism enhanced double-layer LSTM model, and outputting a risk probability; and calculating a comprehensive risk index by integrating the risk probability, the change trend and the volatility, and realizing dynamic risk layering and automatic intervention based on a rule engine. According to the method, a traditional fixed acquisition mode is converted into a dynamic optimization process capable of responding to patient risks, equipment states and system loads in real time, on the premise that high-risk patient monitoring is guaranteed, system resource consumption is remarkably reduced, and long-term optimal balance of data quality, system performance and resource overhead is achieved.

The invention relates to the technical field of biostatistics, and discloses a noise language frequency hearing impairment prediction system based on a GEE model and genetic characteristics, and the system comprises a data collection module which collects follow-up visit data of a worker; the data processing module is used for generating a standardized modeling data set; the feature screening module is used for screening features through a statistical learning method to obtain a key feature subset; the time-varying interaction module is used for constructing a generalized estimation equation model and outputting regression coefficient estimation; the risk prediction module outputs the risk probability and the risk layering result of the individual; the decision support module is used for generating hearing protection suggestions of the individuals; according to the method, the generalized estimation equation model is constructed to process follow-up data of occupational noise exposure workers, so that the accuracy and the stability of language frequency hearing loss risk prediction are improved, a basis is provided for formulating a personalized hearing protection scheme and recommending a proper hearing protection device, and the method is suitable for popularization and application. And the transformation of occupational hearing loss from passive treatment to active prevention is facilitated.

The invention relates to the technical field of medical biology, in particular to a chronic pancreatitis complication risk grading system based on exosome transcriptome data, and aims to solve the problems that existing chronic pancreatitis (CP) typing lacks molecular basis, complication risk prediction is weak and clinical transformation is poor. According to the system, plasma exosomes are extracted and sequenced, a functional characteristic gene set is constructed in combination with pancreas single cell data, CP is divided into three risk increasing subtypes by using a COCA algorithm, and finally 12 core miRNAs are screened to construct a BPNN diagnosis model. The invention proves that the plasma exosome can be used for staging classification of chronic pancreatitis for the first time, miRNA non-invasive accurate layering illness conditions can be detected through qPCR, the risk of fatty diarrhea and 3c type diabetes mellitus can be predicted, and the non-invasiveness, convenience, classification accuracy and result repeatability of the plasma exosome have clinical application and transformation advantages.

The invention provides a sugar fat disease death risk prediction method and system based on NHANES data, and the method comprises the steps: obtaining the fasting blood glucose FPG and glycosylated hemoglobin HbA1c clinical detection data of a sugar fat disease patient, and calculating an SHR value through a formula; based on the data of the NHANES database, SHR total cause bilateral critical values of total cause death rate and cardiovascular death rate, SHR cardiovascular bilateral critical values and SHR cancer unilateral critical values of cancer death rate are obtained; and comparing the SHR value with an SHR total cause double-side critical value, an SHR cardiovascular double-side critical value and an SHR cancer single-side critical value of the cancer death rate, and carrying out risk layering and evaluation. The biochemical aging acceleration index has a prediction effect on the diabetes death risk, and KDMAgeAccel significantly intermediates the death risk caused by SHR.

The invention discloses an application of a biomarker in evaluating the risk of progressing from autoimmune hepatitis to cirrhosis. The biomarker comprises immune globulin G, and / or an antinuclear antibody, and / or aspartate aminotransferase and alanine aminotransferase. Independent predictive factor indexes of the liver cirrhosis risk of an autoimmune hepatitis patient in the biomarker are as follows: an antinuclear antibody ANA index: log2ANA is greater than or equal to 1.62; the AAR index of the ratio of aspartate aminotransferase to alanine aminotransferase is as follows: AAR is greater than or equal to 1.62; the immune globulin G, namely the IgG index, is greater than or equal to 30.3 g / L. The problems that in the prior art, an AIH risk model does not take cirrhosis as the outcome, visual presentation is lacked, prediction efficiency is insufficient, and clinical early-stage accurate risk layering and diagnosis and treatment decision requirements are difficult to meet are solved.

The invention relates to the technical field of molecular biology, in particular to a methylation marker for diagnosis of diabetic nephropathy. Specifically, the methylation marker comprises a P site of a CAT gene promoter region and / or an E site of a CAT gene exon 1 region. Based on a detection system combining methylation sensitive restriction enzyme with quantitative PCR, the kit has the advantages of simplicity and convenience in operation, good repeatability, accuracy in quantification and easiness in standardization, and is suitable for low-invasive samples such as peripheral blood. By effectively distinguishing pure type 2 diabetes patients from diabetic nephropathy combined patients, a reliable technical means is provided for risk stratification, early intervention and dynamic monitoring of diabetic people, and the method has good clinical application prospects and popularization value.

The invention discloses a biomarker combination related to hypoxia-induced neuroinflammation and application of the biomarker combination, and belongs to the technical field of biomarker detection. The marker combination disclosed by the invention consists of serum S100 beta protein, neuron-specific enolase, interleukin-6, high-mobility group protein B1 and neurofilament light-chain protein. The marker combination can be used for stratification of nervous system injury risk of severe hypoxia patients and acquisition of prognosis evaluation data. According to the detection method disclosed by the invention, synchronous quantitative detection of five markers is realized by adopting a multiple immunofluorescent microsphere technology, and a marker combination scoring algorithm is established to comprehensively evaluate the severity of the hypoxia-related neuroinflammation.

This invention discloses a method for constructing a prognostic model for acute myeloid leukemia (AML) based on ferroptosis-related genes. The method involves acquiring gene expression data from AML patients and healthy samples to screen for differentially expressed ferroptosis-related genes (DEGs) associated with AML. Using univariate Cox proportional hazards regression analysis, LASSO regression analysis, and multivariate Cox proportional hazards regression analysis, key genes ACSF2, SLC7A11, DNAJB6, and SOCS1 are selected from these DEGs. A prognostic risk model is constructed based on the expression levels of these key genes and their corresponding multivariate Cox regression coefficients. The risk score formula is: Risk Score = 0.534 × ACSF2 expression value - 0.453 × DNAJB6 expression value + 0.194 × SLC7A11 expression value + 0.308 × SOCS1 expression value. The prognostic model constructed in this invention has high predictive accuracy and reliability, effectively stratifying the risk and assessing the prognosis of AML patients, providing an important reference for the clinical treatment of AML.

This disclosure relates to a method for detecting colorectal cancer. The method comprises determining whether an advanced neoplasm is present in a first screening time period and upon determining that an advanced neoplasm is present in the first screening time period, performing colonoscopy in a second screening time period, after the first screening time period, to detect colorectal cancer. There is further provided a method for stratification of a subject comprising upon detection of an advanced neoplasm in a first screening time period, stratifying the subject into a high risk stratification for developing colorectal cancer in a second screening time period. There is further provided a computer implemented method for calculating a risk score for developing colorectal cancer comprising determining patient data; applying a trained machine learning model to the patient data to determine the risk score; and outputting the risk score.

This invention provides a method, device, and equipment for preoperative risk stratification assessment of endometrial cancer. It involves segmenting and annotating preoperative ultrasound images to extract radiomics feature vectors, and simultaneously performing structured encoding and mapping of pathological biopsy data to generate pathological feature vectors. These two feature vectors are then input into a fusion network containing a cross-modal attention gating module. This gating module automatically generates dynamic weight vectors based on the consistency between the image and pathological features. The two feature vectors are then weighted and modulated separately before being concatenated to obtain a joint feature representation. This automatically reduces the weight of the lower-confidence modality and amplifies the weight of the higher-confidence modality when their assessment conclusions are inconsistent. Finally, the joint feature representation is input into a multi-task prediction network to output the probability of myometrial invasion depth and the probability of lymph node metastasis risk, thereby generating preoperative risk stratification results and surgical plan recommendations.

The invention relates to the technical field of biology, in particular to 5-hydroxymethylcytosine and application of a prognosis model of 5-hydroxymethylcytosine in nasopharynx cancer prognosis evaluation. According to the method, the prognosis scoring model capable of layering the outcome of the patient is constructed, and the model shows strong distinguishing performance in a training set and a verification set. When it is integrated into a prognostic model together with tumor staging and EBV status, the 5hmC score results in higher calibration accuracy and net clinical benefit in decision curve analysis. According to the findings, a 5hmC prognosis model is determined as a noninvasive marker rich in biological information, can be used for prognosis risk layering of NPC patients, and has potential significance for individualized management and improvement of outcome prediction.

The application discloses a prognosis risk prediction model construction system and method based on chronic disease real world data, comprising a multi-source real world data acquisition and access module, a data standardization and quality control module, a multi-dimensional feature engineering and screening module, a comorbidity feature fusion and data set construction module, a prognosis risk prediction model training and optimization module, a model multi-dimensional verification and evaluation module, a model deployment and risk stratification output module; a special multi-source real world data acquisition and quality control system for chronic liver disease, diabetes and hypertension is constructed, data standardization processing is realized in combination with a chronic disease clinical diagnosis and treatment guideline, through multi-dimensional missing value, abnormal value processing and data deduplication integration, the quality and availability of the real world data are effectively improved, the core problem of disorderly and low-quality real world data in the prior art is solved, and a high-quality data source basis is provided for model construction.

The invention discloses a biomarker, application of the biomarker in predicting the risk of diabetic nephropathy and a kit, which can realize non-invasive efficient evaluation on the severity of diabetic nephropathy and provide important supplement for early diagnosis and refined risk stratification of diabetic nephropathy. The ratio of the Sulfatase-1 to the creatinine Cr is uSULF1 / Cr, and the Sulfatase-1 and the creatinine Cr are both from urine.

The invention relates to the technical field of biomedical detection and bioinformatics, and discloses a detection method and system for social anxiety disorder risk assessment, and the method comprises the steps: obtaining transcriptome data of a peripheral blood sample of a to-be-detected object, and carrying out preprocessing and normalization to obtain a standardized gene expression matrix; extracting minimum gene set expression data containing 10 genes such as HSF5 and FADS2, and performing Z-score standardization processing by using the solidified model parameters; calling a preset weight coefficient and an intercept item to perform linear weighting and probability conversion calculation on the standardized data to obtain a disease prediction probability of the subject; and carrying out risk layering according to the optimal critical value and generating an auxiliary diagnosis report. According to the method, stable features are screened through a machine learning algorithm, the scoring model is constructed, subjectivity of traditional clinical diagnosis is overcome, and objective, quantitative and automatic evaluation of social anxiety disorder risks is achieved.