61 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 provides a rare disease risk screening model training system and method, a screening system and a medium, and belongs to the technical field of medical information. According to the invention, an innovative three-stage architecture design is adopted, unified identification of all rare disease types is realized, and the blank in the prior art is filled. When only basic clinical information exists, a rare disease high-risk patient can be accurately identified, the diagnosis time is remarkably shortened, and a treatment precedent is won for the patient. The system adopts a small parameter quantity model integration strategy, reduces hardware requirements and maintenance cost, can be widely deployed in basic medical institutions, and promotes balanced distribution of medical resources. Through the technical measures of nested cross validation, algorithm comparison, threshold optimization and the like, the system has high accuracy and reliability, and the credibility of doctors to the system is enhanced.

The invention discloses a chronic obstructive pulmonary disease patient screening system for the respiratory medicine department, particularly relates to the field of computer-aided diagnosis, and is used for solving the technical problems that an existing screening method is low in screening efficiency, low in patient adaptability and difficult to popularize on a large scale in a basic level. According to the system, a standardized patient medical event sequence is constructed by obtaining a target population medical record containing medicine distribution and disease diagnosis codes; identifying a characteristic medication and diagnosis mode before definite diagnosis of the chronic obstructive pulmonary disease based on a sequence pattern mining technology; constructing a medical knowledge graph by using the modes, obtaining a feature embedding vector representing a chronic obstructive pulmonary disease medical trajectory through graph neural network learning, and establishing a high-risk digital portrait; inputting the medical sequence of the person to be screened into a feature extraction model based on same map training, and generating an individualized risk probability prediction value by calculating the matching degree of the medical sequence and the digital portrait; and a screening result report is automatically generated, so that efficient and noninvasive early risk screening is realized.

The invention discloses an abdominal aortic aneurysm risk screening method based on HbA1c multiple risk markers, and aims to solve the problems that AAA screening in the prior art depends on a single marker, is lack of nonlinear analysis and is insufficient in application tool, a comprehensive risk prediction model is constructed by integrating HbA1c, proteomics data and non-genetic factors, a nonlinear risk relationship is revealed, and the risk of the abdominal aortic aneurysm is predicted to be the risk of the abdominal aortic aneurysm of the abdominal aortic aneurysm of the abdominal aortic aneurysm. And an application tool is developed, visual display of the morbidity probability and the risk level of n years is provided, a personalized screening strategy is supported, and the accuracy and practicability of AAA early screening are improved.

The invention relates to the technical field of medical care informatics, in particular to a nephropathy medication interaction risk screening system which comprises a data acquisition unit, an SOC chip processing and analysis unit, a risk early warning unit and a man-machine interaction and communication unit. Through multi-source data access, dynamic weight scheduling and Kalman filtering fusion correction, accurate and on-demand acquisition of nephropathy core indexes is realized, and real-time intelligent discrimination of personalized medication risks is realized by relying on knowledge base support, adaptive path adjustment and a safe atomization updating mechanism, so that the risk of the nephropathy is accurately determined. Therefore, the problems that traditional system data acquisition lacks specialized pertinence, risk assessment personalization is insufficient, and model updating is lagged are effectively solved, and the accuracy and timeliness of nephropathy medication interaction risk screening are remarkably improved.

The invention provides a hyperuricemia high-risk screening system and method based on big data, and relates to the technical field of big data deep learning, and the method comprises the steps: collecting feature data related to hyperuricemia from a plurality of data sources, carrying out the data preprocessing, calculating a related feature index based on the preprocessed data, and building a convolutional neural network model, thereby achieving the high-risk screening of hyperuricemia. The method comprises the following steps: inputting a feature index, outputting a hyperuricemia risk level, then obtaining feature data of a person to be screened, inputting a trained model, generating a risk level correction coefficient in combination with personal information, and finally determining a final hyperuricemia risk level index by setting an upgrading and degrading mechanism and comparing the correction coefficient with a preset threshold value. According to the method, feature data related to hyperuricemia are collected through multiple data sources, and accurate assessment of the hyperuricemia risk is realized in combination with the convolutional neural network model.

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 belongs to the field of nursing science, and particularly relates to an acute exacerbation malnutrition risk prediction model for an old chronic obstructive pulmonary disease patient and a construction method of the acute exacerbation malnutrition risk prediction model, which are used for establishing a rapid and accurate prediction model for acute exacerbation malnutrition risk screening and evaluation of the old chronic obstructive pulmonary disease patient. The invention discloses a model for predicting the risk of acute exacerbation dystrophy of an old chronic obstructive pulmonary disease patient. The risk prediction model is a multivariate Logistic regression model: P =-12.845 + 1.022 * age + 0.339 * ethnic + 0.077 * educated degree + 0.683 * gastrointestinal tract symptom + 0.035 * height-0.126 * body mass index + 1.045 * partial pressure of carbon dioxide-0.077 * chloride ion-0.017 * serum albumin-0.004 * prealbumin + 0.005 * hypersensitive C reaction protein + 0.479 * lymphocyte number. According to the acute exacerbation malnutrition risk prediction model for the patient with the chronic obstructive pulmonary disease of the aged and the construction method thereof, the prediction model for rapidly and accurately screening and evaluating the acute exacerbation malnutrition risk of the patient with the chronic obstructive pulmonary disease of the aged is established.

The application provides a leukemia artificial intelligence preliminary screening model based on blood routine data and a system for establishing the same. The method for establishing the leukemia artificial intelligence preliminary screening model comprises the following steps: S1: collecting electronic medical record data of leukemia patients and non-leukemia patients and processing the same to obtain a plurality of training databases and a verification database; S2: training the plurality of training databases by using an analysis module to obtain a plurality of corresponding rule sets in sequence, and combining and summarizing the rule sets with a false negative rate of less than 5% to obtain a final rule set, i.e., a leukemia risk screening model; S3: verifying the verification database by using the leukemia risk screening model; and S4: performing multiple iterations on the leukemia risk screening model to obtain an optimal model of the leukemia risk screening model. The leukemia artificial intelligence preliminary screening model established by the application can quickly and accurately screen out leukemia.

The invention relates to the technical field of data processing, in particular to a postpartum depression risk AI dynamic screening method and system. The method comprises the following steps: firstly, analyzing intensity values of different muscles in a user image, and dividing behaviors of a user at different moments into autonomic behaviors and non-autonomic behaviors; the smiling duration and the energy ratio of the autonomic behavior to the non-autonomic behavior are analyzed, an emotion contradiction index is determined, and then a contradictory smiling window is divided; determining a physiological comprehensive value by combining the stress delay angle and the recovery delay angle; analyzing forward control and reverse feedback from emotion to physiology to obtain a bidirectional path coordination ratio; screening out an imbalance event from a contradictory smile window through a bidirectional path coordination ratio; and for the unbalance event, determining the total unbalance risk of the postpartum user in combination with the bidirectional path coordination ratio and the physiological comprehensive value. According to the invention, the accuracy of postpartum depression risk screening is improved.

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 belongs to the technical field of biological medicine, and particularly discloses application of an aromatic hydrocarbon receptor as a biomarker in early risk screening of traumatic sepsis. The aromatic hydrocarbon receptor can be used as a new biomarker for predicting the occurrence of the traumatic sepsis, and the expression level of the aromatic hydrocarbon receptor is positively correlated with the occurrence of the post-traumatic sepsis, so that the aromatic hydrocarbon receptor can be used as a screening index for the occurrence risk of the traumatic sepsis. The invention provides a new technical means for early screening of traumatic sepsis.

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 invention discloses a cognitive impairment risk prediction clinical model for Chinese rural old people and a design method, and belongs to the crossing field of senile pathology and medical artificial intelligence. The input module is used for receiving individual data including age, education level, drinking behavior, systolic pressure, grip strength and depression state; the processing module is used for screening variables based on LASSO regression and calculating the total score T, the prediction score LogitP and the probability P of the cognitive impairment of the individual through multi-factor Logistic regression; and the output module is used for outputting the calculation result of the processing module for clinical use. The model is based on a CHARLS database, integrates demographic characteristics, lifestyle, chronic disease history and body function indexes, AUC in a training set and AUC in a verification set are 0.849 and 0.852 respectively, and the model has good discrimination ability, calibration degree and clinical practicability and can provide a convenient and efficient cognitive impairment risk screening tool for primary medical institutions.

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.

The invention discloses a dementia risk screening method, system and device, a storage medium and a computer program product, and relates to the technical field of intelligent detection.The dementia risk screening method comprises the steps that multi-source data of a testee are obtained, and the multi-source data comprise eye movement data, demographic data, gene data, questionnaire data and medical history data; performing data preprocessing on the multi-source data, and performing feature extraction and fusion on the preprocessed multi-source data to generate a comprehensive feature vector; inputting a demographic data feature vector, an eye movement data feature vector and a medical history data feature vector in the comprehensive feature vector into a dementia state evaluation model, and outputting a current dementia state; and inputting a gene data feature vector, a questionnaire data feature vector and a demographic data feature vector in the comprehensive feature vector into a risk transformation prediction model, and predicting a dementia transformation probability in a future time period. According to the method, the universality and the accuracy of dementia screening 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.