519 results about "Risk stratification" patented technology

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

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 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 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, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, 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 relates to a data processing method and system for multi-source complex biological information data. According to the method, expression profile data, gene variation data and clinical survival data are collected, and unified standardization processing is carried out on the collected data. Feature alignment is performed on different source data based on sample identifiers, a joint feature expression matrix is constructed, and a context dependency relationship across data types is maintained. On the basis, multi-stage feature screening is carried out through Lasso regression and information gain evaluation in sequence, and an optimal feature subset used for modeling is obtained. And further training a risk scoring model by adopting a Cox proportional risk regression method, and calculating a risk scoring value of the sample by utilizing the constructed continuous scoring function. And finally, dividing the score value into a plurality of risk levels, and generating a survival curve of each level in combination with a Kaplan-Meier estimation method so as to verify the risk layering effect and prediction significance of the model. According to the method, the accuracy of biological information modeling can be improved, and the method has good universality and practical value.

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 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 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 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 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 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 discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

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, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, 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 provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention relates to the technical field of biological detection, in particular to a monoclonal antibody combination for HPV18 type E6 protein detection and application. The provided combination is composed of 5G3 and 2C7, and the amino acid sequences of complementary determining regions of variable regions of a heavy chain and a light chain of the combination are clear and are respectively shown as SEQ ID NO.1-12. The antibody combination has high specificity and sensitivity, the lowest detection limit can reach 100 pg / ml, and cross reaction with other HPV subtypes is avoided. According to a double-antibody sandwich ELISA and biotin-avidin amplification detection system constructed based on the combination, the signal intensity and the detection accuracy are remarkably improved, and the combination is suitable for rapid detection of the HPV18 type E6 protein in a cervical exfoliated cell sample and has application value in early diagnosis of cervical cancer, risk stratification, vaccine research and development and curative effect evaluation.

The invention relates to a gynecological postoperative intelligent rehabilitation management method and system, and relates to the technical field of rehabilitation management. Determining a basic recovery score of the patient based on the preoperative information; determining a rehabilitation adjustment coefficient based on the intraoperative information; based on the basic recovery score and the rehabilitation management adjustment coefficient, physiological feature tags of the patient are determined, and the physiological feature tags comprise a low-risk tag, a medium-risk tag and a high-risk tag; based on the physiological feature tag and the operation type, determining a rehabilitation path according to historical rehabilitation data; and when the physiological feature tags are different risk tags, determining a rehabilitation management strategy according to the rehabilitation path and based on different patient information. According to the method, quantitative indexes are adopted to carry out risk layering on the patients, the situation of one-step operation in traditional rehabilitation management is avoided, and therefore the pertinence and adaptability of a rehabilitation scheme are improved. Meanwhile, the method can improve the effective utilization rate of data.

The invention relates to the technical field of biomedicine, and discloses a rectal cancer postoperative recurrence risk prediction method based on a prediction model, and the method comprises the steps: obtaining a patient tumor in the in-vivo image data of a rectal cancer patient; extracting imaging features of the tumor of the patient, and identifying recurrence risk features of the rectal cancer patient from the imaging features; analyzing clinical pathological characteristics of the rectal cancer patient according to the preoperative evaluation data and the postoperative pathological report; key recurrence influence factors of the rectal cancer patients are identified, recurrence events and follow-up visit time of the rectal cancer patients are identified, risk scoring is carried out on the rectal cancer patients, and a risk scoring result is obtained; based on the risk scoring result, the recurrence risk stratification of the rectal cancer patient is defined, postoperative recurrence risk analysis is conducted on the rectal cancer patient in combination with the recurrence risk stratification, the recurrence risk characteristics and the risk scoring result, and a risk analysis result is obtained. According to the invention, the comprehensiveness and accuracy of the rectal cancer recurrence risk analysis result can be improved.

The invention discloses a hypertensive heart disease early screening system fusing ultrasonic multiple parameters, which comprises the following steps: acquiring ultrasonic image data and physiological index monitoring parameters of a patient at different time points, performing difference comparison on heart structure parameters to generate time sequence change data, and establishing a multi-dimensional data fusion scheme based on the time sequence change data and physiological indexes; according to the fusion scheme, acute risk trigger factors are identified, blood flow abnormal parameters are generated, and emergency threshold comparison is carried out to generate acute risk early warning signals; extracting a myocardial strain degradation rate to determine a chronic degradation acceleration time point, carrying out disease progress stage association to generate a chronic risk layering curve, and establishing a clinical index comparison table; carrying out double-layer correlation on the acute risk early warning signal and the chronic risk layering curve, and carrying out calibration judgment to generate a comprehensive risk judgment sequence; the sequence is decomposed into an acute judgment layer and a chronic judgment layer, a risk threshold value is configured to generate an instant early warning signal, a layered screening report is formed, and technical support is provided for early discovery of the hypertensive heart disease.

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 relates to the field of clinical diagnostics, and discloses an elderly NSC emergency risk layering method and device and a medium, and the method comprises the steps: S1, obtaining multi-dimensional evaluation data of an elderly NSC patient; s2, adopting a preset risk mapping rule to convert the risk values into single risk values of a unified scale; s3, dividing the single risk value into a plurality of risk sub-models, and calculating a dimension risk score of each risk sub-model; s4, based on a preset fusion strategy, integrating the risk sub-model and the single risk value interaction effect, and calculating to obtain a total risk score of the patient; and S5, determining the risk level of the patient according to the total risk score, and outputting a clinical diagnosis and treatment suggestion. According to the method, multiple biomarkers and key clinical parameters are integrated, a multi-dimensional combined risk assessment model is constructed, and the potential pathological state of a patient can be reflected more comprehensively.

The invention discloses a differentiated thyroid cancer lung metastasis risk prediction model construction method and system. 4D Label-free quantitative proteomics analysis and PRM and IHC dual verification are carried out on primary focus paraffin embedded tissue samples of lung metastatic DTC patients and non-lung metastatic DTC patients, and it is found that multiple key differential expression proteins DEPs may become potential biomarkers for predicting the DTC lung metastasis risk. In addition, the sample size is enlarged, three single and combined DTC lung metastasis risk prediction models are respectively constructed based on clinical features and key DEPs expression, then internal verification is performed on the efficiency of the three fitted models, and the optimal model is taken to construct Nomogram. According to the method, help is provided for risk stratification of lung metastasis of DTC patients, and an important basis is provided for formulating clinical individualized treatment schemes and follow-up visit strategies for high-risk patients.

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 relates to a hierarchical diagnosis and treatment resource allocation system based on an AI priority pre-auditing mechanism, and belongs to the technical field of medical resource allocation, and the system comprises a user input module which is used for receiving initial information of a patient based on an AI applet, and the initial information at least comprises a symptom description, a medical history record, examination data and a diagnosis and treatment stage; the AI intelligent pre-inquiry module is used for performing multiple rounds of questions and answers on the patient according to the initial information to extract an illness state index, inputting the illness state index into a preset risk layering model, and determining an illness state severity / urgency index of the patient; the resource distribution module comprises a special number source pool reserved for experts, and the resource distribution module is used for opening expert numbers for the patients whose illness condition severity / emergency index is greater than an illness condition threshold value; and the referral guidance module is used for recommending a replacement doctor seeing scheme for the patient who does not accord with the open expert number. According to the invention, the technical problem of insufficient fairness and efficiency of an illness state emergency degree registration distribution mechanism caused by structural mismatching of expert resources is solved.

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 belongs to the technical field of medical health monitoring, and provides a human health prediction method and system based on facial video physiological signal detection, and the method comprises the steps: collecting a facial video stream, and extracting time sequence physiological signals such as heart rate, HRV, respiratory rate and the like through an rPPG algorithm; constructing a graph database individual health portrait in combination with multi-scale time sequence alignment; adopting a dynamic threshold algorithm to detect instantaneous anomaly, and fusing nonlinear dynamics and waveform morphological characteristics to quantify anomaly; constructing a hybrid model, extracting space-time and high-order features, and modeling multi-parameter interaction; a prediction result is dynamically corrected based on a Bayesian algorithm, and health risk layering is realized through clustering; and outputting the visual health report. Through non-contact monitoring, multi-modal fusion and edge-cloud collaborative architecture, the problems that traditional equipment is low in compliance, non-contact technology is insufficient in precision and prediction is shallow are solved, dynamic health prediction and closed-loop management are achieved, and the system is suitable for scenes such as remote monitoring and chronic disease screening.

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.

The invention discloses a postoperative portal vein pressure prediction system, which relates to the technical field of portal vein pressure prediction, and is characterized in that three-dimensional structure regions of interest of the liver and the spleen are segmented based on preoperative and postoperative abdomen enhanced CT vein phase image data of a patient; image omics features including first-order statistical features, texture features and shape features are extracted from the segmented regions of interest of the three-dimensional structures of the liver and the spleen, and core feature screening is carried out on the extracted features; integrating the screened core features with clinical hemodynamic parameters and surgical parameters to construct a multi-modal prediction model of the portal vein pressure gradient; and predicting and outputting a portal vein pressure gradient predicted value by using the multi-modal prediction model of the portal vein pressure gradient, and determining a postoperative portal vein pressure gradient risk grade of the patient. The portal vein pressure gradient prediction method solves the problems of insufficient dynamic evaluation capability, multi-modal information integration and risk layering application in the prior art, and realizes non-invasive and accurate prediction of the portal vein pressure gradient.

The invention relates to the field of medical detection, and particularly provides a diagnostic model for predicting prognosis of gastric cancer CAR-T cellular immunotherapy and application of the diagnostic model. According to the present invention, the content and the spatial distribution map of the CLDN18.2 positive gastric cancer tissue sample microenvironment component are systematically analyzed by integrating the multiple fluorescence immunohistochemical detection and the full-slice digital imaging quantitative analysis method for the first time; through multi-dimensional evaluation of a tumor microenvironment under the treatment background of a targeted CLDN18.2 chimeric antigen receptor T cell immunotherapy CT041, a diagnosis model for predicting the prognosis of the gastric cancer CAR-T cell immunotherapy is established for the first time, accurate screening of benefited people and risk stratification before treatment are realized, treatment selection is guided, and the treatment effect is improved.

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).