40 results about "Risk screening" patented technology

The invention belongs to the technical field of physical ability analysis and diagnosis, and particularly relates to a latent variable fusion modeling method and system for old people weakness trajectory prediction based on biomechanics. According to the method, the plantar pressure, the joint angle and the electromyographic signal in the gait process of the elderly are collected through wearable sensing equipment, multi-dimensional structured feature parameters are extracted, and a key index group reflecting the weakness degree is generated based on preset weight fusion. And in combination with time sequence fitting and variation trend analysis, dynamic evaluation and risk level judgment of the weakness state of the old people are realized. The system comprises a data acquisition module, a feature extraction module, an index fusion module, a trend analysis module and a result output module, has the characteristics of high accuracy, strong real-time performance, good interpretability and the like, and is suitable for weakness risk screening and rehabilitation intervention guidance.

The invention discloses an athletic injury risk screening system based on motion capture and biomechanical analysis, and relates to the field of athletic injury risk screening, and the system comprises a standardization module which is used for establishing a grouping reference value database and a to-be-detected person exclusive file; the data acquisition module is used for acquiring three-dimensional action data of a person to be detected and preprocessing the data; the feature vector module is used for calculating joint feature parameter values of the moving joints in three moving planes based on an inverse dynamics method and constructing biomechanical feature vectors; the model construction module is used for constructing an athletic injury risk assessment model and outputting a comprehensive risk level and a specific risk mode label and probability; and the model training and verification module is used for performing staged training on the model and verifying the feasibility of the model. The method has the advantages that multi-modal biomechanical and kinematic data are fused, and accurate assessment of the sport injury risk and comprehensive identification of a specific risk mode are realized.

This invention discloses a method for screening the risk of congenital scoliosis. The method includes: acquiring a back surface image of the subject, which reflects the surface morphological distribution of the back region; preprocessing the back surface image to obtain a standardized surface image to reduce the influence of differences in acquisition posture, imaging conditions, or subject body shape; based on the standardized surface image, constructing surface morphological feature information for congenital scoliosis risk screening, which includes at least the left-right symmetry of the back, the state of spinal midline deviation, or the trend of surface morphological changes; inputting the surface morphological feature information into a pre-trained congenital scoliosis risk screening model to generate congenital scoliosis risk screening results. This invention can achieve objective and consistent congenital scoliosis risk screening.

The invention provides a foot ulcer risk screening instrument based on artificial intelligence, and relates to the field of foot ulcer risk screening instruments. The foot ulcer risk screening instrument based on artificial intelligence comprises a foot screening instrument, three vertical grooves are formed in each of the two sides of the front end of the foot screening instrument, three threaded rotating rods are arranged at the front ends of the inner walls of the vertical grooves, sliding blocks are arranged on the inner walls of the vertical grooves in a sliding mode, and connecting blocks are arranged at the rear ends of the sliding blocks. Limiting plates are arranged on the two sides of the rear ends of the connecting blocks in the middles of the two sides, limiting side plates are arranged on the two sides of the rear ends of the two sides of the connecting blocks on the two sides, and a detection side plate is arranged between every two adjacent limiting side plates. According to the invention, reasonable layout of foot dorsum, foot side and sole detection components is realized, three types of screening cameras are respectively corresponding to different areas of the foot, a structural basis is provided for comprehensive screening of multiple parts, and the problem of insufficient coverage of non-sole areas in traditional screening is effectively avoided.

The invention discloses a post-AMI heart failure oxidized lipid screening method based on solid phase extraction, LC-MS / MS and AI. The method comprises the steps of blood sample pretreatment, solid phase extraction and purification, LC-MS / MS detection and AI model screening. According to the method, high-sensitivity qualitative and quantitative detection of oxidized lipid is realized through optimized LC-MS / MS parameters, and key oxidized lipid molecules and risk levels of heart failure after AMI emergency treatment blood supply reconstruction can be quickly and accurately screened in combination with a trained random forest AI model; the kit solves the problems that in the prior art, trace oxidized lipid detection sensitivity is low, specificity is poor, and heart failure risk screening lags, has the advantages of being high in detection efficiency, good in accuracy and high in practicability, can provide a scientific basis for early warning and intervention of clinical heart failure after AMI, and has high clinical application value.

The invention discloses a recurrent abortion risk analysis knowledge graph construction method. A data acquisition unit, an evidence-based medicine evidence evaluation unit, a knowledge reasoning unit, a knowledge fusion unit, a classification attribute layer construction unit, a risk factor layer construction unit and an evaluation index layer construction unit are included. The method has the advantages that the description model of the factors causing the recurrent spontaneous abortion and the onset risk of the recurrent spontaneous abortion is constructed based on the knowledge graph, auxiliary guidance opinions are provided for clinical risk screening and diagnosis and treatment thoughts of the recurrent spontaneous abortion, risk factors of the recurrent spontaneous abortion can be comprehensively checked, and decision data basis is provided for doctors and patients.

The invention discloses a swallowing abnormity identification method and system for esophageal cancer risk screening, and relates to the technical field of swallowing audio identification, and the method comprises the steps: obtaining the current swallowing audio collected by a user, the collection time, and the fluidity of a used swallowing object; feature components, corresponding to the feature items, of the current swallowing audio are extracted based on the mobility; acquiring all feature components and acquisition time of the reference swallowing audio from historical extraction records of the user; combining the feature component of the current swallowing audio and the feature components of all the reference swallowing audios into a target time sequence feature; generating a progressive swallowing difficulty risk according to the target time sequence characteristics; an esophageal cancer risk prediction result is generated according to the physiological features of the user and the progressive dysphagia risk, and early-stage noninvasive esophageal cancer risk prompting and individualized screening which can be operated at home are achieved through the voice recognition technology.

The application discloses a glycolipid metabolism index prediction model for predicting cardiovascular events and a construction method thereof. The model aims to solve the problems that existing cardiovascular risk assessment tools are not accurate in describing glycolipid metabolism state and are single in prediction dimension. The construction method comprises the following steps: obtaining demographic information and clinical data of a subject; measuring the content of high-density lipoprotein subcomponent HDL2b; taking body mass index, lipoprotein a, triglyceride-glucose index and HDL2b as core evaluation parameters, taking whether a cardiovascular event occurs within five years as a target variable, and adopting a multivariate logistic regression to construct a prediction model. The model can realize rapid and accurate stratification of the risk of cardiovascular event occurrence of an individual by quantifying index scores and adding up. The application integrates multi-dimensional key indicators of glycolipid metabolism, has the advantages of high prediction performance, clear risk stratification and strong repeatability, and is suitable for early risk screening and evaluation of cardiovascular diseases.

The invention relates to the technical field of medical data processing, and discloses a periodontitis risk screening system based on family history and saliva inflammation marker detection, which comprises a data acquisition front end, a physical detection suite and a background risk calculation engine. The data acquisition front end acquires the medical history and smoking history of relatives, quantifies the medical history and smoking history into genetic and environmental factor scores, and captures a detection image containing test paper and a standard colorimetric card at the same time. The physical detection kit comprises saliva matrix metalloproteinase-8 test paper and a colorimetric card for providing a white balance reference. The background engine performs white balance correction and analysis on the image by using the colorimetric card data to obtain a biomarker score; and performing normalization and weighted fusion calculation on genetic, environmental and biomarker scores to generate a comprehensive risk total score and a risk grade. Through the image correction and data fusion technology, the ambient light interference is eliminated, and the comprehensive quantitative evaluation of the genetic background and the current pathological state is realized.

The invention discloses a method and a device for evaluating fetal distress risk based on saturated fatty acid intake, and a medium. The method comprises the following steps: acquiring daily average saturated fatty acid intake data of a pregnant woman to be evaluated; comparing the intake with a plurality of preset risk threshold intervals, and determining a corresponding fetal distress risk level; and finally outputting an evaluation result containing the risk level. According to the method, objective dietary data and a rigorous statistical method are combined, and the association between saturated fatty acid and fetal distress is converted into a clear quantitative risk assessment standard, so that the progress of risk assessment from qualitative to quantitative and from fuzzy to accurate is realized; the problem that in the prior art, an individualized and quantitative fetal distress risk assessment tool for saturated fatty acid is lacked is solved. The invention provides a rapid, convenient and non-invasive risk screening tool, and provides a scientific basis for pregnancy diet intervention and clinical monitoring.

The invention provides a multi-source data fused early risk screening method and system for growth retardation of children, and belongs to the technical field of medical information. The method comprises the following steps: acquiring a first predicted growth sequence and a second predicted growth sequence of a target child; performing compensation correction on the second predicted growth sequence based on the intergenerational compensation sequence to obtain a second corrected predicted growth sequence; performing fusion processing on the first predicted growth sequence and the second corrected predicted growth sequence, and establishing a target predicted growth sequence; and monitoring and acquiring a tracking and actually measured height sequence of the target child, performing comparative analysis on the tracking and actually measured height sequence and the target predicted growth sequence, and when the tracking and actually measured height sequence does not meet the target predicted growth sequence, generating a growth retardation early warning. The technical problem that early-stage growth retardation risks cannot be effectively identified due to insufficient early-warning accuracy caused by child growth prediction in the prior art is solved, and the technical effects of improving the child growth retardation risk prediction accuracy and realizing early-stage accurate early-warning are achieved.

This invention relates to a device and method for risk screening and appeal material generation of medical insurance settlement data, belonging to the field of medical informatization and medical insurance data processing technology. To address the problems of reliance on manual processes, inconsistent procedures, and easy omissions in evidence indexing in medical insurance settlement anomaly screening, appeal basis association, and appeal material generation, this invention reads and parses settlement details returned by the medical insurance bureau, calls medical insurance rules to screen high-risk cases, forms a question-and-answer path based on an appeal-guided decision tree, associates relevant medical insurance policy clauses, fragments of successful appeal cases, and relevant medical record evidence indexes, and generates an appeal explanation. This improves the completeness and standardization of appeal material generation and is applicable to fields such as hospital medical insurance settlement review, preparation of medical insurance refusal appeal materials, and review of suspected medical insurance violations.

The invention discloses a primary prevention method for screening the probability of cardiovascular diseases, and belongs to the technical field of mobile medical treatment. According to the method, physiological, behavior and environment dynamic data of a user are collected through a wearable device and the like, a genetic risk score is introduced, and a machine learning model is utilized to calculate a personalized future illness probability; the core of the method is that a dynamic probability result is automatically associated to a preset stepped intervention knowledge base through a matching rule engine, customized prevention schemes of different levels from life adjustment to medical consultation and the like are generated and pushed in real time, and user compliance data are tracked to form a feedback closed loop. The system correspondingly comprises a data acquisition and fusion module, a risk assessment module, a knowledge base module, an intelligent matching module, an interactive pushing module and the like. According to the method, automatic and personalized closed-loop management from risk screening to intervention starting is realized, and the problem of disjunction between evaluation and action in traditional prevention is effectively solved.

The invention provides a hemodialysis catheter related blood flow infection risk screening system and method, and belongs to the technical field of medical management, and the system comprises an information input module which is used for inputting patient case information; the data acquisition module is used for acquiring multi-source data such as catheter indwelling operation records, catheter indwelling time and daily maintenance records; the risk screening module is used for screening catheter-related blood flow infection risks and determining risk levels; the evaluation module is used for evaluating the patient state and the catheter state; and the diagnosis judgment module is used for judging whether a catheter-related blood flow infection diagnosis standard is met or not and generating treatment intervention notification information. The method has the beneficial effects that by screening and determining the risk of blood flow infection related to the catheter, further evaluating the patient and catheter states, and combining an evaluation result, clinical manifestation and a microbiological examination result, judging whether an infection diagnosis standard is met and generating a treatment intervention notice, the screening efficiency is improved, and the infection occurrence rate is reduced; the treatment effect and prognosis of a patient are improved.

The application provides a non-contact screening system for obstructive sleep apnea syndrome. The non-contact screening system for obstructive sleep apnea syndrome only needs to collect a user's face video through a data collection device, can extract multi-dimensional biological characteristics related to obstructive sleep apnea syndrome, including craniofacial morphological risk characteristics and real-time physiological characteristics, and can realize risk screening of obstructive sleep apnea syndrome without any body contact through fusion analysis of the characteristics by a deep learning model, thereby significantly improving the convenience and comfort of screening while maintaining high diagnostic accuracy.

The present application relates to the technical field of artificial intelligence and psychology, and particularly relates to a digital sand table multi-modal data analysis method based on an intelligent chat robot, which comprises the following steps: constructing a knowledge-enhanced large language model architecture; using synonym replacement, scene generalization and dialogue rewriting strategy data enhancement on three types of training data sets; executing four-stage model training; synchronously collecting sand tool operation, facial expression, behavior and voice response data based on the large language model, synchronously integrating the multi-modal data sets according to timestamps, training a psychological analysis model; based on the explainability analysis of the screening results of the psychological analysis model, extracting the model attention weight and feature importance score, and performing attribution analysis on the sand table image, audio and video data to generate a text analysis report. The method can deeply mine subjective psychological motivation through dynamic interaction, realize deep fusion analysis of multi-modal data, and provide an explainability analysis report, and is suitable for depression risk screening of a large number of people.

The invention discloses a mental health risk intelligent screening and evaluating system based on semantic interaction, and the system comprises a data preprocessing module which is used for reading a semantic interaction text and generating a training and verification sample set; the semantic feature extraction module is used for constructing a deep pyramid convolutional neural network and outputting a semantic feature tensor; the high-level strategy generation module is used for generating sub-target vectors based on the semantic feature tensor; the low-layer strategy decision module is used for generating action probability distribution according to the semantic feature tensor and the sub-target vector; the strategy training module is used for performing joint training on high-level and low-level strategies according to the DPPO and outputting gradient information; the parameter screening module is used for evaluating a training effect and screening an optimal weight parameter group; and the reasoning output module is used for loading target model parameters and outputting a mental health risk screening result. According to the method, collaborative optimization of semantic feature extraction and hierarchical strategy decision is realized, and screening accuracy and reasoning efficiency are improved.

This invention, through lipidomics and clinical sample validation, found that prospective follow-up of pre-pregnancy serum samples from women with a history of recurrent pregnancy loss (RPL) revealed that the pre-pregnancy serum levels of phosphatidylinositol PI (18:1 / 18:1) (i.e., PI 36:2), phosphatidylcholine (29:0-6:0) (i.e., PC 35:0), phosphatidylethanolamine (18:1p-18:2) (i.e., PE 36:3p), triglycerides (28:1-10:4-11:2) (i.e., TG 49:7), triglycerides (14:0-18:2-20:5) (i.e., TG 52:7), and wax esters (22:1) (i.e., WE 22:1) were significantly lower in these women than in those who subsequently achieved successful pregnancies. This confirms that the pre-pregnancy serum levels of these six lipids (PI 36:2, PC 35:0, PE 36:3p, TG 49:7, TG 52:7, WE 22:1) were significantly lower in these women than in those who subsequently achieved successful pregnancies. Decreased levels of 22:1 lipids were significantly associated with the risk of miscarriage in subsequent pregnancies. The area under the curve (AUC) of the predictive model constructed by combining six lipids reached 0.85, demonstrating good predictive efficacy. Based on these findings, this lipid combination can serve as a reliable predictor of miscarriage risk in subsequent pregnancies in RPL patients and can be used to develop diagnostic reagents or kits for preconception risk screening.

The invention relates to the technical field of medical cosmetology and skin health management, in particular to a thermal margari operation comfort method and system based on skin parameters, and the method comprises the steps: collecting various types of medication data of an operated person, constructing a historical behavior analysis scheme containing risk identification logic based on high-risk population data, and on this basis, establishing a risk identification logic analysis scheme based on the historical behavior analysis scheme; on the basis of the scheme, associated historical behavior data of low-risk crowds and other users are collected and analyzed, and multiple groups of multi-dimensional user historical behavior data are formed through integration. And finally, processing the data by using a preset comprehensive evaluation scheme, and calculating the skin side effect risk level of each operated person, so that personalized risk screening and feature checking are completed before an operation, and an objective basis is provided for clinically formulating a safer and more comfortable treatment scheme, and the treatment safety and the user experience are improved.

This invention relates to the field of medical simulation technology, specifically to a closed-loop suctioning simulation teaching system and method for newborns. The system includes: a data analysis module, a risk screening module, a physiological calculation module, a decision-making and early warning module, and a result evaluation module. In this invention, multi-dimensional temporal features such as airway interaction pressure and suctioning duration are comprehensively collected and time-aligned. Outliers such as excessive radial pressure and insertion depth are simultaneously screened to form a risk feature vector, objectively characterizing potentially dangerous actions that could induce airway mucosal damage. Combined with risk data, physiological performance sequences such as blood oxygen saturation and heart rate are calculated in depth. A decision tree model is used to comprehensively predict the linkage between physiological indicators and operational risks to generate graded early warning instructions. This effectively overcomes the subjective assessment blind spots caused by traditional single physical feedback, accurately identifies potential harm hazards caused by improper operation, and achieves automated and standardized evaluation.

The invention provides a metabolic marker composition for distinguishing healthy kidney cancer from early kidney cancer. According to an analysis method based on urine metabonomics, a diagnosis model capable of accurately distinguishing healthy people from early-stage kidney cancer patients is constructed by detecting a specific metabolic marker combination and combining a machine learning algorithm. The model can noninvasively discriminate the early-stage kidney cancer in a high-sensitivity and high-specificity manner, and breaks through the limitation of an existing clinical detection method. The method disclosed by the invention has the advantages of noninvasive property, simple and convenient sampling and the like, is suitable for large-scale population screening, is particularly beneficial to popularization of early-stage kidney cancer risk screening in remote areas deficient in medical resources, and provides support for early-stage noninvasive diagnosis and effective prevention and control of kidney cancer.

The invention relates to the technical field of data analysis and screening, and particularly discloses a colorectal cancer risk screening system based on tongue picture analysis, which comprises the following steps: obtaining a tongue extension-free tongue image and an expiration sample, carrying out denoising, color recovery and segmentation on the tongue image to obtain independent tongue picture features, extracting hue saturation, texture and morphological parameters, and normalizing to generate tongue picture feature vectors; markers such as methane are detected from an expiration sample through tunable semiconductor laser absorption spectrum and photoacoustic spectrum, concentration vectors are generated after calibration, the two types of vectors are subjected to principal component analysis dimension reduction and cross-modal attention weighted fusion to obtain joint features, then the joint features are input into a double-branch deep learning model, a risk probability value is obtained after fusion output, and the risk probability value is obtained. Dividing low, medium and high risks and outputting suggestions. According to the invention, a standard and objective data analysis screening technical field path can be constructed, the precision defect of a traditional screening system is made up, the accessibility disorder of examination is overcome, and a technical solution is provided for early discovery, early diagnosis and early treatment of colorectal cancer.

The application discloses a gouty nephropathy tubular injury risk prediction system based on kidney RI, urine alpha 1-MG and eGFR slope, and relates to the technical field of medical data processing. The system comprises a data acquisition module, a risk score calculation module, a risk stratification module and a result output module. The three indexes of kidney ultrasound resistance index RI, urine alpha 1-microglobulin concentration alpha 1-MG and eGFR annual decline rate are scored, non-invasive early risk stratification of gouty nephropathy tubular injury is realized, and low / medium / high risk stratification results and monitoring suggestions are output. The application solves the problems of lagging identification of early tubular injury and lack of standardized non-invasive assessment tools in the prior art, can be used for risk screening and monitoring of gouty nephropathy patients, and has the characteristics of non-invasiveness, convenience and quantification.

The application relates to the technical field of medical image intelligent analysis, and discloses a prostate cancer early screening model and a construction method thereof. The method receives original image data from multiple sources in parallel, and obtains a candidate image set after quality evaluation and screening. The candidate image is subjected to layer-by-layer structure decomposition, and a background layer, a tissue layer and a blood vessel structure layer are separated. Structure features of the tissue layer and the blood vessel structure layer are extracted, and are compared with corresponding standard tissue feature libraries and standard blood vessel feature libraries, and a primary risk feature group is formed according to the differences. The feature group is subjected to time sequence simulation evolution, a simulation feature state at a future time point is generated, and is coupled with a current state to construct a dynamic risk feature map. The dynamic risk feature map is used to search for a path with the highest matching degree in a preset risk screening path, and a final risk decision mark is generated. The application realizes decoupled analysis of image components and dynamic risk evaluation.

The present application relates to the technical field of depression risk screening, and provides a depression screening method based on adaptive graph topology and dynamic weighting of time sequence characteristics, which comprises the following steps: obtaining EEG data to be screened and performing pretreatment to extract an initial node characteristic sequence; initializing an independent learnable parameter matrix and generating an adaptive learnable adjacency matrix through nonlinear mapping; constructing a parallel double-branch network of a learnable graph topology module and a time sequence Transform module, inputting the initial node characteristic sequence and the learnable adjacency matrix into the network, and respectively extracting spatial topology dependent features and time features; splicing the spatial topology dependent features and the time features to obtain spliced features, performing weight distribution on the spliced features, and outputting final fusion features; and flattening the final fusion features and inputting them into a multilayer perception classifier to output a depression positive probability. The present application improves the accuracy of early screening.

The invention discloses a special exercise load testing method and system for jogging risk screening, and particularly relates to the field of load tests.The method comprises the steps that a personalized progressive increase type exercise load testing scheme is formulated according to basic physiology and jogging historical data of a user, and in the testing process, the user experience is improved; physiological response and kinematics data of a user are collected in real time through multiple sensors, then a risk assessment model containing a physiological risk layer and a kinematics risk layer is constructed and applied, a dynamic risk index is calculated, and real-time quantitative assessment is conducted on cardiovascular loads, metabolic states and motion postures; when any risk indicator exceeds a safety threshold, the system automatically triggers a test termination instruction to ensure user safety, and finally, based on complete test data, a risk screening report including risk levels, factor analysis and personalized exercise suggestions is generated.

The invention relates to the technical field of atrial fibrillation recognition, in particular to an artificial intelligence atrial fibrillation recognition system based on an MRI image, computer equipment and a storage medium, and the method comprises the steps: inputting an MRI-DWI image sequence collected at the early stage of admission of a stroke patient; performing standardized preprocessing on the image; entering a double-branch architecture of an AI model, and extracting semantic features of a stroke focus area; meanwhile, multi-dimensional image omics features are extracted from the focus area; the semantic features and the multi-dimensional image omics features are fused, and an AF classification result is output through a full connection layer Softmax; in the training stage, Dice loss and a cross entropy loss function are used for joint optimization, and in the testing stage, an AF prediction probability is output. The method can be expanded to other high-risk screening fields of cardiac stroke, atrial myopathy and the like through transfer learning. The system can also be integrated in a PACS system, automatic uploading, analysis and result pushing of images are achieved, and a clinical auxiliary basis is provided for a post-stroke anticoagulation strategy.

The invention relates to the technical field of medical image intelligent analysis, and discloses a prostate cancer early screening model and a construction method thereof. The method comprises the following steps: receiving original image data from multiple sources in parallel, and obtaining a candidate image set after quality evaluation and screening; and carrying out layer-by-layer structure decomposition on the candidate image, and separating a background layer, a tissue layer and a vascular structure layer. Structural features of the tissue layer and the blood vessel structural layer are extracted respectively and are aligned and compared with the corresponding standard tissue feature library and the standard blood vessel feature library, and a primary risk feature group is formed according to differences; and performing time sequence simulation evolution on the feature group to generate a simulation feature state of a future time point, and coupling the simulation feature state with a current state to construct a dynamic risk feature map. And searching a path with the highest matching degree in preset risk screening paths based on the dynamic risk feature map, and generating a final risk decision mark. According to the invention, decoupling analysis and dynamic risk assessment of image components are realized.

This invention provides a combination of metabolic biomarkers for distinguishing between healthy individuals and gynecological tumors. Based on metabolomics research, this invention analyzes and compares metabolites in the plasma of gynecological tumor patients and healthy individuals to screen for a set of metabolic biomarker combinations with significant differences. Based on the characteristics of plasma metabolites, this invention constructs a diagnostic model with high sensitivity and specificity. This method can be used for risk screening of three types of gynecological tumors: ovarian cancer, cervical cancer, and endometrial cancer. It is non-invasive, easy to obtain samples, and has high subject compliance, making it suitable for large-scale population screening, especially in remote areas with limited medical resources. It also avoids over-testing and reduces testing costs.