39 results about "High risk group" patented technology

The invention discloses a sudden death risk real-time assessment system and method based on multi-modal physiological signal fusion, relates to the field of human physiological state monitoring and early warning, and solves the problems of low accuracy and poor real-time performance of sudden death risk assessment by single-modal signals. The system comprises a signal acquisition module, a preprocessing module, a high-dimensional feature extraction module, a multi-modal feature fusion module, a sudden death risk quantification module, a model updating engine module and an early warning feedback module, and each module integrates a self-adaptive filtering unit, a time sequence convolutional network unit, an improved multi-head self-attention mechanism unit and the like. According to the scheme, multi-mode signals such as electrocardio are synchronously collected through multiple channels, a dynamic risk index is calculated through self-adaptive noise reduction, parallel extraction of time-frequency domain nonlinear features and mutual information weighted fusion in combination with kernel density estimation, and online incremental updating and multi-stage early warning of a model are achieved; the sudden death risk can be accurately evaluated in real time, the anomaly detection sensitivity and the early warning timeliness are improved, and the method is suitable for daily health monitoring and high-risk group risk management and control.

The invention relates to the technical field of medical informatization, in particular to an MCI cognitive trajectory and bidirectional outcome dynamic prediction method based on a joint model. The method comprises the following specific steps: firstly, constructing a univariate joint model, then constructing a multivariate joint model, and then constructing a function type multivariate joint model; performing parameter estimation and statistical inference on the model; fitting evaluation is carried out on the function type multivariable joint model based on multi-longitudinal cognition and multi-state outcome; and finally, carrying out individualized dynamic prediction. According to the method, a baseline or longitudinal nerve image is processed by using a function type data analysis technology, and a change track is reconstructed; a plurality of cognitive assessments are used as longitudinal measurement indexes, covariants such as age and gender and functional nerve images are used as predictive factors, two-way outcome of MCI and occurrence time of the two-way outcome are set as survival outcomes, a joint model based on multi-longitudinal cognitive multi-state outcomes is constructed, early screening of high-risk groups is assisted, and the survival outcomes of the high-risk groups are determined. And powerful support is provided for optimal distribution of medical and medical and health resources.

The invention discloses a tumor radiotherapy rehabilitation nursing intervention system based on artificial intelligence, and relates to the technical field of tumor rehabilitation nursing. By integrating multi-dimensional data and combining clinical standards to output quantitative evaluation levels and key weak dimensions, rehabilitation weakness of a patient is accurately positioned, a targeted target is provided for the nursing intervention strategy generation module, a personalized scheme is generated, and pertinence and performability of intervention are ensured; policy execution data are captured in real time and a scheme is dynamically adjusted, so that the intervention process always adapts to the rehabilitation state change of a patient, and meanwhile, active management and control on high-risk groups are enhanced by periodically collecting core indexes and linking risk upgrading and priority intervention, so that the tumor radiotherapy rehabilitation nursing is changed from passive response to active pre-judgment, and the rehabilitation nursing efficiency is improved. And the overall quality of rehabilitation nursing after radiotherapy and the prognosis effect of a patient are optimized.

The invention discloses a system and equipment for predicting colorectal cancer and progression thereof. Based on the found expression level difference of AXIN2 and / or EPHB2 in colorectal cancer and the progression thereof, the invention provides a colorectal cancer and progression prediction method, system and equipment, a computer readable storage medium and a computer program product, high-risk groups can be efficiently and accurately screened out from common groups, and colonoscopy is further completed in time. While the economic burden caused by endoscopic screening is relieved, patients suffering from colorectal adenoma and early colorectal cancer are screened out, and the method has great significance in early diagnosis and treatment of colorectal tumor and further remarkable reduction of the morbidity and mortality of colorectal cancer.

This invention discloses an artificial intelligence-based intervention system for tumor radiotherapy rehabilitation nursing, belonging to the field of tumor rehabilitation nursing technology. By integrating multi-dimensional data and combining clinical standard output quantitative assessment levels and key weakness dimensions, this invention accurately identifies patients' rehabilitation shortcomings, providing targeted objectives for the nursing intervention strategy generation module and generating personalized plans to ensure the intervention's relevance and feasibility. It captures strategy execution data in real time and dynamically adjusts the plan, ensuring the intervention process always adapts to changes in the patient's rehabilitation status. Simultaneously, by regularly collecting core indicators and linking risk escalation and priority intervention, it strengthens proactive management of high-risk groups, realizing a shift from passive response to proactive prediction in tumor radiotherapy rehabilitation nursing, thus optimizing the overall quality of post-radiotherapy rehabilitation nursing and patient prognosis.

The invention discloses a biomarker combination for predicting PCI postoperative LVR of an STEMI patient and application of the biomarker combination, and belongs to the technical field of biology. The research discovers that in a left ventricular remodeling (LVR) group and a non-left ventricular remodeling group after percutaneous coronary intervention (PCI) of a patient suffering from acute ST segment elevation type myocardial infarction (STEMI), a left ventricular endocardial lower myocardial overall longitudinal strain (GLS internal) value, a lung B-line integral, a serum sFRP5 level and Apelin-13 show significant differences. Through combined prediction analysis, it is found that the GLS internal value, the lung B-line integral, the serum sFRP5 and the Apelin-13 have excellent prediction performance on whether the LVR occurs after the PCI operation of the STEMI patient or not, high-risk groups, suffering from the LVR after the PCI operation, of the STEMI patient can be accurately and efficiently recognized, intervention is conducted as soon as possible, and the life quality of the patient is expected to be improved.

The application discloses a CT dose self-adaptive optimization system and method thereof, and belongs to the technical field of medical images, which comprises a CT scanner, an image data processing workstation, a high-risk population analysis device, a dose parameter self-adaptive optimization device, a dose parameter automatic adjustment device and an intelligent learning unit, and based on the theory of information geometry, the dose parameter optimization problem is promoted from the Euclidean space to the Riemannian manifold space, the dose parameter manifold and the image quality manifold are constructed, the geodesic line is calculated on the manifold through the Fisher information matrix as the Riemannian metric, and the optimal dose path is found. The system adopts a double-manifold collaborative optimization mechanism, establishes a mapping relationship between the two manifolds, and realizes accurate optimization of parameters in combination with a geometric-guided genetic algorithm, and through pre-scanning, high-risk population analysis, information geometry optimization and automatic parameter adjustment, individualized dose control is realized, the radiation dose is additionally reduced by 30-45% compared with a traditional method, the image quality is maintained or improved, and the safety of CT examination is remarkably improved.

This invention discloses a method for identifying intrahepatic cholangiocarcinoma (ICC) based on serum peptide characteristics and its application. The invention collects serum samples from three groups: individuals with ICC, those with benign liver disease, and healthy controls. Peptide characteristic peaks associated with ICC are screened, and a model is constructed using a logistic regression algorithm. This model demonstrates excellent discriminative ability on both the training and independent test sets, with AUCs reaching 0.986 and 0.963, respectively, significantly outperforming traditional tumor markers CA19-9 and CEA. The peptide characteristic peak combination and the discriminative model constructed based on it can be used for early detection of ICC, differentiation between benign and malignant cases, and screening of high-risk populations. It offers advantages such as rapid detection, standardized procedures, strong early ICC identification capability, and low false positive rate, providing a highly sensitive, specific, and widely applicable non-invasive screening tool for clinical use.

The invention relates to a biomarker for diagnosing rheumatoid arthritis and application of the biomarker, and belongs to the technical field of biological medicines. The invention finds that the concentration of lipopolysaccharide and diamine oxidase in the plasma of a patient with rheumatoid arthritis (RA) is obviously higher than that of a healthy control group; furthermore, the invention also discovers that three proteins from plasma exosomes, namely phospholipid flip enzyme 1 (PLSCR1), keratin 1 (KRT1) and complement C1Q C chain (C1QC), have a good diagnosis effect on rheumatoid arthritis, and the diagnosis accuracy of rheumatoid arthritis can be remarkably improved by performing combined diagnosis on lipopolysaccharide and diamine oxidase with the three proteins. Therefore, when the biomarker is applied to preparation of RA diagnosis products, a core basis can be provided for accurate diagnosis of RA and generation of a risk assessment system, and the biomarker has transformation application value in the aspects of early diagnosis of RA and screening and accurate prevention and treatment of high-risk groups.

There is described herein a method of prognosing or classifying a subject with a Myeloproliferative Neoplasm (MPN) comprising: (a) determining the expression level of at least 10 genes in a test sample from the subject selected from the group consisting of SPP1, CEACAM6, GJA1, IGSF10, IGFBP2, COL4A5, LYVE1, MTIE, EMP1, XIST, DLK1, TPSAB1, TIMP3, CLC, MS4A1, ENKUR, ALOX12, KNDC1, HLA-DQB1, GAS2, CLEC2L, BEND2, CDH7, and NT5E; and (b) comparing expression of the at least 10 genes in the test sample with reference expression levels of the at least 10 genes from control samples from a reference cohort of patients; wherein a difference or similarity in the expression of the at least 10 genes in the test sample and the reference expression levels is used to prognose or classify the subject with MPN into a low risk group or a high risk group for worse survival.

This invention discloses a risk prediction device and storage medium for metabolic-associated fatty liver disease in children or adolescents. The two-stage cascaded screening strategy and model of this invention provide a highly accurate, accessible, and scalable solution for the early identification of metabolic-associated fatty liver disease in children or adolescents. This invention utilizes different anthropometric data to identify high-risk groups in routine school physical examinations, followed by further screening using a comprehensive serological index at community health centers, thereby achieving accurate diagnosis of metabolic-associated fatty liver disease and providing clinicians with a non-invasive auxiliary diagnostic tool that combines high precision, high accuracy, and broad accessibility.

The invention provides a Parkinson's disease rapid RT-QuIC diagnostic kit based on A30P mutant alpha-synuclein, and belongs to the technical field of detection kits. The specific A30P mutant protein is used as a reaction substrate, and rapid and high-sensitivity amplification detection can be carried out on trace misfolded alpha-synuclein in a skin sample through an optimized RT-QuIC system. The kit has the characteristics of short detection period and convenience in operation, provides a new tool for molecular diagnosis of Parkinson's disease, is suitable for definite diagnosis of clinical PD suspected cases, and can also be used for screening high-risk groups. The kit has the advantages of mature preparation process, easily available raw materials, easiness in industrial production and large-scale popularization, and important clinical application value.

The invention belongs to the technical field of obstetrics and gynecology disease prediction, and particularly relates to a premature fetal membrane premature rupture prediction marker based on maternal circulation exosome microRNA and an application kit. According to the invention, placentas, fetal membranes and plasma exosomes of pregnant women with full-term birth and premature birth premature rupture of fetal membranes are collected for sequencing, the fetal membrane-derived exosome hsa-miR-145-3p which can be detected in the plasma exosomes is screened out in combination with a verification set, and the cell source and the premature birth promoting effect of the fetal membrane-derived exosome hsa-miR-145-3p are respectively clarified at a cell level and an animal level; and finally, by detecting the level of the plasma exosome hsa-miR-145-3p of the pregnant 12 (+ 1)-17 (+ 6) perimaternal body, the efficiency (AUC = 0.833) of the exosome in predicting the occurrence of premature rupture of fetal membranes in premature delivery is illustrated. According to the method, the high-risk crowd of the pPROM can be predicted through a noninvasive method in the early and middle stages of pregnancy, and the method has guiding significance for subsequent prevention and intervention of the high-risk crowd.

The application discloses a molecular marker and MRI image fusion tumor early detection method and system, first collects serum molecular marker detection data and multi-sequence MRI image data of a digestive tract tumor screening object, constructs a molecular feature vector and extracts high-dimensional MRI image features respectively; a tumor prior probability is calculated through a molecular pre-screening deep neural network, and an adaptive spatial attention guide weight is generated; the weight is weighted and fused with the MRI image features to realize fine feature enhancement of a high-risk area and accurate lesion detection; based on a Bayesian evidence fusion reasoning framework, molecular detection evidence and image detection evidence are combined to update a posterior probability, and finally, a lesion positioning, detection confidence and visual detection report are output. The application realizes efficient complementation of molecular evidence and image evidence, greatly improves the accuracy, sensitivity and specificity of early detection of digestive tract tumors, and is suitable for non-invasive accurate screening of high-risk groups in clinical practice.

The invention provides a chronic disease health-care system for institutional old-age care, and relates to the field of intelligent medical treatment. The system comprises a periodic physical examination module for performing periodic physical examination on a crowd to obtain a physical examination result; the grading and bidding module is used for dividing the crowd into low-risk, medium-risk and high-risk groups according to the physical examination result; the low-risk group, the medium-risk group and the high-risk group are managed by units of different levels; the high-risk group management module at least comprises a chronic disease special case unit and is mainly used for creating a chronic disease file for a patient and performing chronic disease management service on the patient; the chronic disease type comprises any one or more of hypertension, diabetes, cerebral apoplexy, coronary heart disease, chronic obstructive pulmonary disease and fundus disease. According to the invention, a patient-centered'Internet + chronic disease management 'mode is adopted, chronic diseases are classified, graded and bid-divided, a county hospital-community / town health center-health station integrated system network is formed, early discovery of chronic diseases is truly realized, the morbidity of high-risk groups is reduced, and the disability and fatality rates are reduced.

The invention discloses a prediction model for enteral nutrition intolerance occurrence risk of an adult patient with severe burn and application thereof, and belongs to the field of disease risk prediction. According to the method, risk factors of enteral nutritional feeding intolerance of the serious burn patient are screened, and the EFI risk prediction model of the adult serious burn patient is established and verified based on a Logistic regression analysis method, so that a scientific basis is provided for early clinical recognition of high-risk patients and implementation of preventive intervention. Risk factors for occurrence of EFI (Extensible Firmware Index) of severe burn patients of adults are explored, the risk factors comprise the number of basic diseases of subjects, whether intestinal probiotics are used or not, the number of white blood cells within 24 hours of hospital admission, fasting blood-glucose within 24 hours of hospital admission and the total area of burn, and a risk prediction model is constructed based on the risk factors. And reference is provided for early clinical recognition of high-risk groups with intolerant enteral nutrition feeding of serious burn patients.

The invention discloses a Parkinson's disease dysarthria early recognition method based on voiceprint features, and belongs to the technical field of medical diagnosis. The method comprises the following steps: performing voice analysis and voice recognition on original voice data of a to-be-recognized object, extracting pronunciation deviation and an initial feature set related to voiceprint in a fragment, screening out target features with remarkable discrimination degree for recognition of dysarthria of Parkinson's disease, and obtaining a dysarthria recognition result of the to-be-recognized object through a model. Through accurate vowel and consonant fragment extraction and pronunciation deviation recognition, in combination with association of a character sequence and voice data, accurate interception of phoneme fragments is realized, quantitative judgment of pronunciation deviation is realized, key dimensions of dysarthria recognition are reserved through feature extraction, early signals of dysarthria of Parkinson's disease are captured, and the recognition accuracy of dysarthria of Parkinson's disease is improved. High-risk groups are marked, and an early warning result is calculated and output in combination with a joint risk value, so that the problem of unobvious symptoms in a prosperous period is solved, a review interval is dynamically adjusted, and whole-process management from identification to monitoring is realized.

This invention discloses an intelligent self-rescue system for marine jellyfish stings, relating to the field of first aid technology for biological injuries. This invention establishes a basic concept popular science system by integrating information on the species, habits, geographical distribution, and hazards of marine organisms to prevent marine organism injuries. It also integrates the clinical manifestations and treatment plans for injuries caused by venomous marine organisms, establishing a training system for treatment skills to aid in the preliminary identification of injury types and improve treatment skills. Furthermore, it integrates relevant data to compile a first aid guidebook and presents relevant medicines and equipment in a modular format for on-site emergency treatment. This effectively addresses the shortcomings and deficiencies in current marine organism injury treatment. Based on the epidemiological characteristics and treatment consensus of marine jellyfish stings, it improves the awareness, treatment skills, and on-site emergency treatment efficiency of potentially high-risk groups and medical personnel regarding marine jellyfish stings, reducing the risk of aggravated injury and mortality from jellyfish stings.

The application provides a construction method and system of a model for predicting ovarian low response and deploying individualized ovarian stimulation strategies, including two models: a POR diagnosis model and a POR ovarian stimulation strategy deployment model. First, candidate features are preliminarily screened and data is preprocessed; second, the model is trained based on various machine learning algorithms, the best algorithm is evaluated and screened; third, the key risk factors related to POR are determined through feature interpretation; and finally, the model construction, testing and verification are completed, and the prediction is interpretable at the individual level. The application can make interpretable prediction on the occurrence risk of POR of women of childbearing age, and can deploy individualized optimal ovarian stimulation strategies based on the POR risk, can be applied in a hospital information system scene, has important research significance for the clinical diagnosis and pathogenesis of POR, and has great use value for early screening of high-risk groups of POR, assisting doctors in making diagnosis and treatment decisions and reducing the economic burden of patients.

The application provides an rs10045697 related SNP marker combination for predicting the risk and susceptibility of high myopia based on equivalent spherical lenses and an application thereof, and relates to the field of biological medicine. Specifically, the application screens a key SNP marker rs10045697 and a plurality of SNP sites related to high myopia in a high myopia queue based on whole genome association study (GWAS), and a model constructed based on the SNP marker combination can realize risk stratification and susceptibility detection of high myopia, is suitable for ophthalmic screening, high-risk population identification and precise prevention and control intervention, and has important clinical transformation value.

The application discloses a Parkinson disease dysarthria early identification method based on voiceprint features and belongs to the technical field of medical diagnosis. The original speech data of a to-be-identified object is subjected to speech analysis and speech recognition, and an initial feature set related to voiceprints in a pronunciation deviation and a segment is extracted, target features with significant discriminability for Parkinson disease dysarthria identification are screened out, a dysarthria identification result of the to-be-identified object is obtained through a model, accurate vowel and consonant segment extraction and pronunciation deviation identification are realized, phoneme segment accurate cutting is realized by combining the association of a text sequence and speech data, the quantization determination of the pronunciation deviation is realized, the key dimension of the dysarthria identification is reserved through feature extraction, the early signals of Parkinson disease dysarthria are captured, the high-risk groups are marked, the early warning result is output in combination with the joint risk value calculation, the problem that the prodromal symptoms are not obvious is solved, the review interval is dynamically adjusted, and the whole-process management from identification to monitoring is realized.

The invention discloses a lumbar vertebra bone mineral density index prediction method and system based on a CNN-LSTM hybrid model, belongs to the technical field of medical data processing, and is used for accurately predicting lumbar vertebra bone mineral density to assist clinical decision making. The system is divided into four layers of architectures, wherein a data input layer integrates 42 clinical features and a lumbar vertebra bone mineral density true value detected by DXA; the data preprocessing layer purifies data through missing value and abnormal value processing and MIC algorithm feature screening; the model prediction layer extracts local features through CNN, models a sequential relationship through LSTM, and optimizes parameters in combination with an Adam optimizer and an early stop strategy; and the result output layer identifies the delivery result through prediction calculation, visualization and high-risk population. Through cooperation of multi-source data processing and the mixed model, the prediction precision is improved, and support is provided for osteoporosis screening and the like.

According to the chronic atrophic gastritis clinical prediction model based on the random forest algorithm, the SHAP method is used for explaining and illustrating feature importance and explaining the prediction result of the model so as to determine relevant risk factors of the CAG, and reliable clinical evidence is provided for screening high-risk groups before endoscopic examination of the CAG. Meanwhile, the invention further provides a visual webpage platform based on a Shiny framework, namely a chronic atrophic gastritis clinical prediction platform based on a random forest algorithm, a doctor can visually obtain an individual CAG risk prediction value, operation is easy and convenient, practicability is high, and clinical application and popularization are facilitated.

The present invention relates to amyloid beta pathology, which is the cause of Alzheimer's disease, and, more specifically, to an amyloid beta pathology risk stratification method using an artificial intelligence model. To this end, provided is the amyloid beta pathology risk stratification method using an artificial intelligence model, comprising steps in which: a dataset related to brain atrophy is extracted from multimodal data (T1, T2 or CT); an artificial intelligence model (140) learns a dataset (100) related to brain atrophy; (S100) the trained artificial intelligence model (140) outputs a predicted probability distribution of amyloid beta-PET positivity and a predicted probability distribution of amyloid beta-PET negativity; (S100) the artificial intelligence model (140) determines an upper threshold and a lower threshold on the basis of the predicted probability distribution of amyloid beta-PET positivity and the predicted probability distribution of amyloid beta-PET negativity; (S120) a control unit stratifies the risk of amyloid beta-PET positivity into a high risk group (151), an intermediate risk group (153) and a low risk group (155) on the basis of the upper threshold and the lower threshold; (S140) a blood biomarker test is performed on the intermediate risk group (153); and (S160) a patient with a positive blood biomarker test result is determined as a patient of amyloid beta-PET positivity, and a patient with a negative blood biomarker test result is determined as a patient of amyloid beta-PET negativity.

Methods and systems to selectively output access-protected information associated with a code based on biometric data of person from who the code is obtained. An embodiment includes outputting first information upon capture of the code and outputting second information if biometric data obtained from the person matches biometric data associated with the code. The first information may be extracted from the code and the second information may be retrieved from a remote storage device. The biometric data associated with the code may be extracted from the code or retrieved from the remote storage device. The first information may include personal identification information. The second information may include, without limitation, access-protected information, personal medical information, an indication that the person is a member of an at-risk group, or an indication that the person is suspected of involvement in illicit activity. Outputting the first information may be omitted.

The invention provides a cerebral apoplexy high risk group health monitoring and early warning management system based on multi-modal perception, and belongs to the technical field of monitoring, and the cerebral apoplexy high risk group health monitoring and early warning management system comprises a multi-modal perception module, a data preprocessing module, a feature extraction module, a risk assessment module, an early warning push module, a terminal interaction module, a data storage module and a self-adaptive optimization module. According to the method, physiological, behavior and environment multi-dimensional data are collected through multi-modal perception, after preprocessing and multi-dimensional feature extraction, risk assessment is performed by adopting a convolutional neural network and long and short-term memory network fusion algorithm, and closed-loop management is realized by combining graded early warning, multi-channel pushing and differentiated terminal interaction. And meanwhile, the model suitability is dynamically improved through adaptive optimization. According to the system, the comprehensiveness, the evaluation accuracy and the early warning intervention effectiveness of cerebral apoplexy risk monitoring are improved, the health data safety is guaranteed, and the personalized management requirements of high-risk groups are met.

The invention discloses a CT dose self-adaptive optimization system and method, and belongs to the technical field of medical image.The CT dose self-adaptive optimization system comprises a CT scanner, an image data processing workstation, a high-risk population analysis device, a dose parameter self-adaptive optimization device, a dose parameter automatic adjusting device and an intelligent learning unit. A dose parameter optimization problem is improved from an Euclidean space to a Riemannian manifold space, a dose parameter manifold and an image quality manifold are constructed, a Fisher information matrix is used as a Riemannian metric, a geodesic line is calculated on the manifold, and an optimal dose path is searched. According to the system, a double-manifold collaborative optimization mechanism is adopted, a mapping relation between two manifolds is established, precise parameter optimization is achieved by combining a geometric guidance genetic algorithm, individualized dose control is achieved through pre-scanning, high-risk population analysis, information geometric optimization and automatic parameter adjustment, the radiation dose is additionally reduced by 30-45% compared with a traditional method, and the method is suitable for large-scale popularization and application. Meanwhile, the image quality is kept or improved, and the safety of CT examination is remarkably improved.

The application discloses a stroke high-risk population positioning and evaluation system based on multi-modal data, relates to the technical field of medical information science, and comprises a stroke prevention and control management platform, which is in communication connection with the following modules, wherein: a multi-modal data acquisition and integration module is used for collecting multi-modal data related to stroke from multiple channels and performing a preprocessing operation on the collected multi-modal data. Through the integration of clinical data, image data, omics data and end health data, the application can comprehensively capture multi-dimensional information related to stroke, especially by simulating hemodynamic characteristics by using a cerebral vascular digital twin and combining a graph neural network to fuse multiple data sources, the application can more accurately identify high-risk populations, significantly improve the accuracy and reliability of risk prediction, and overcome the missed detection problem caused by the dependence on a single data source in traditional methods, thereby providing stronger support for early intervention.

The invention discloses application of a reagent for detecting the content or expression level of FABP3 and WIF1 in a sample in preparation of a reagent for diagnosing and / or predicting osteoporosis, a kit and a system. In the application, a sample comprises a serum sample. The application exemplarily provides the osteoporosis early noninvasive diagnosis kit based on serum protein marker FABP3 and WIF1 joint detection, so that accurate screening of high-risk groups is realized, and the deficiency of DXA in early diagnosis is made up.

This invention relates to a biomarker for diagnosing rheumatoid arthritis (RA) and its application, belonging to the field of biomedical technology. This invention discovered that the concentrations of lipopolysaccharide (LPS) and diamine oxidase (DAO) in the plasma of RA patients are significantly higher than those in healthy controls. Furthermore, this invention also found that three proteins derived from plasma exosomes—phospholipid invertase 1 (PLSCR1), keratin 1 (KRT1), and complement C1QC chain (C1QC)—have good diagnostic effects on RA. Combining LPS and DAO with these three proteins can significantly improve the diagnostic accuracy of RA. Therefore, applying the biomarker of this invention to the preparation of RA diagnostic products can provide a core basis for the accurate diagnosis of RA and the generation of risk assessment systems, and has translational application value in the early diagnosis of RA and the screening and precise prevention and treatment of high-risk groups.