215 results about "Diagnosis early" patented technology

The invention relates to the technical field of medical diagnosis, and discloses a DCM early noninvasive analysis method based on multi-radiomics and serum markers. Collecting image data through a multi-modal medical imaging device, and collecting serum marker data through a blood detection device; respectively generating a radiomics feature set and a serum marker time sequence feature set by using a multi-scale feature extraction algorithm and a time sequence analysis model; fusing the features by adopting a dynamic weighted fusion strategy to generate a joint feature matrix; inputting the model into a pre-trained multi-task deep learning model to predict a DCM risk probability; and finally, based on a genetic algorithm, optimizing the diagnosis decision tree and outputting an early DCM diagnosis result. The method is noninvasive and accurate, and can effectively improve the early diagnosis accuracy of DCM.

The present invention provides a method and system for intelligent follow-up of chronic atrophic gastritis based on multi-source electronic medical records, which relates to the technical field of intelligent disease follow-up, including preprocessing and feature extraction of structured, semi-structured and unstructured electronic medical record data, mining the inter-layer feature association relationship by a meta-path reasoning module, and generating comprehensive graph features of patients; identifying the causal relationship between features by a Bayesian causal network module, and building a disease risk prediction model in combination with a temporal causal graph module; and optimizing the follow-up plan by a Nash equilibrium algorithm based on risk prediction results and medical resource constraints; the present invention can effectively predict the disease risk of chronic atrophic gastritis, and optimize the follow-up plan in combination with medical resources, thereby improving the follow-up efficiency and facilitating the early diagnosis and treatment of chronic atrophic gastritis.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention provides an old people body health early diagnosis and risk assessment system based on artificial intelligence, and relates to the technical field of old people health monitoring and assessment. Old people health parameters are collected in real time according to a set time interval, and a statistical method is used to calculate a mean value and a standard deviation of cardiovascular parameters; then, based on acceleration data, heart rate change, blood pressure fluctuation and position change information, sleep, movement and resting states of the old people are judged, and the day and night blood pressure change rate, the sleep state heart rate standard deviation, the movement state pulse wave conduction speed change quantity and the resting state heart stroke output average value are calculated; four risk assessment formulas of blood pressure risk, heart rate risk, pulse wave conduction velocity risk and heart stroke output quantity are constructed according to the data, a comprehensive health risk value is calculated through weighted merging, and the health state of the old people is judged to be healthy, medium-low risk or high risk according to a set threshold value.

The invention discloses a septicopyemia death risk prediction method based on artificial intelligence, and the method comprises the following steps: data collection and preprocessing: collecting clinical data and inspection indexes of a septicopyemia patient, and carrying out the cleaning and standardization processing of the data; feature selection and engineering: selecting features related to septicopyemia death risks based on clinical knowledge and statistical analysis, and performing feature engineering; model training and verification: training the preprocessed data by using a machine learning algorithm, and verifying the model by using a training set, a verification set and a test set; model evaluation and optimization: evaluating model performance by using indexes such as AUC, accuracy rate, recall rate, F1 score and the like, and optimizing model hyper-parameters; and model explanation and application: explaining a model prediction result by using an explanatory tool, and applying the model in a clinical environment to assist death risk assessment. The invention provides a method for improving the early diagnosis and prognosis prediction of septicopyemia, thereby reducing the death rate.

The invention discloses application of an lncRNA biomarker in esophageal squamous cell carcinoma diagnosis, and relates to the technical field of esophageal squamous cell carcinoma diagnosis. The esophageal squamous cell carcinoma marker comprises LINC00482. The lncRNA marker screened by the invention has the technical advantages of high esophageal squamous cell carcinoma diagnosis sensitivity and strong specificity, has good diagnosis characteristics, and has important significance on early diagnosis, early intervention, deep understanding of the pathological mechanism of esophageal squamous cell carcinoma and the like. In addition, the method for auxiliary diagnosis or diagnosis of esophageal squamous cell carcinoma by using the biomarker has the technical advantages of simple operation, low price and short detection period, and can meet the detection requirements of various samples such as blood, tissues and the like.

The invention discloses a skeletal muscle degeneration chronic disease prediction method and device, equipment and a medium. The method comprises the steps of data preparation and privacy protection; initializing and broadcasting a global model; updating a local model and calculating a weight; an AFA adaptive mechanism is applied; aggregating and updating a global model; performing testing and accuracy evaluation; carrying out iterative training and model optimization; disease prediction and early diagnosis. According to the invention, the accuracy and stability of disease prediction can be effectively improved; through the federated learning technology, the risk of privacy disclosure caused by concentrated data storage in a traditional method is avoided on the premise that the privacy of a patient is protected; besides, in combination with an adaptive aggregation strategy, the problem of data heterogeneity between different medical institutions can be solved, malicious or untrusted updates can be effectively identified and filtered, the precision and reliability of a global model are ensured, and the risk of performance reduction or model failure caused by data inconsistency or malicious clients is avoided.

The invention discloses a construction method of a risk prediction model for early diagnosis of liver cancer. The construction method of the risk prediction model comprises the following steps: 1, preparing clinical data; 2, processing, analyzing and learning clinical data, and constructing a project database of a hepatocellular carcinoma clinical diagnosis path based on big data; 3, determining main diagnosis key points in the disease timing sequence diagnosis scheme and related indexes influencing the operation, and formulating a diagnosis and treatment path extraction rule; 4, constructing a quality-efficiency evaluation index system for the diagnosis and treatment path set, and calculating the early diagnosis probability and postoperative recurrence probability of the liver cancer; and 5, establishing a Markov model for early screening and diagnosis of liver cancer and risk prediction of postoperative recurrence. According to the method, the ANN principle is utilized, an HCC early diagnosis risk prediction model and an HCC clinical diagnosis and treatment path evaluation system are established, HCC high-risk groups are subjected to early recognition, and the HCC early diagnosis rate is increased; meanwhile, an optimal clinical diagnosis and treatment decision is provided for HCC treatment.

The invention relates to the technical field of medical care information processing, and particularly discloses a pulmonary nodule risk prediction system and method for a patient with chronic obstructive pulmonary disease, and the system comprises a historical examination data acquisition module which collects multiple CT examination data, lung function data and lung quantitative data of the patient with chronic obstructive pulmonary disease; the growth inflection point recognition module recognizes a pulmonary nodule growth inflection point of the patient; the risk prediction model building module builds a pulmonary nodule risk prediction model; the current patient risk prediction module inputs the current CT examination data, the current lung function data and the current lung quantitative data of the to-be-detected patient into the pulmonary nodule risk prediction model to obtain a current pulmonary nodule risk prediction result of the to-be-detected patient; the review time recommendation module obtains review recommendation time of the patient to be detected based on the current pulmonary nodule risk prediction result of the patient to be detected and a preset review time recommendation model, effective prediction of the pulmonary nodule risk of the patient with chronic obstructive pulmonary disease is achieved, and an important basis is provided for early diagnosis and treatment.

The invention discloses an Alzheimer's disease computer-aided diagnosis system based on a unified multi-modal mechanism visual language model. The Alzheimer's disease computer-aided diagnosis system comprises a multi-modal data input module, a feature extraction and projection module, an abnormal mark token integration module, a unified multi-modal attention mechanism module, an anomaly detection module and a diagnosis result generation module. The method has the outstanding advantages of improving the early diagnosis precision of the Alzheimer's disease, enhancing the model robustness and improving the clinical practicability.

The invention discloses a cardiovascular three-dimensional optical coherence imaging system, and belongs to the technical field of medical instruments. A cardiovascular three-dimensional optical coherence imaging system comprises a host integration unit and an auxiliary diagnosis unit. The system solves the problem that accurate diagnosis and treatment requirements are difficult to meet in the prior art, interference signals are generated through the host integration unit, high-speed data processing is carried out in combination with the GPU, high-resolution cardiovascular tissue two-dimensional and three-dimensional images can be generated, powerful support is provided for early diagnosis and treatment of diseases, and the system is suitable for popularization and application. The auxiliary diagnosis unit establishes a cardiovascular disease diagnosis knowledge base based on a large amount of clinical case data, automatically identifies plaque types in a three-dimensional image by using a machine learning algorithm, can quickly and accurately provide diagnosis reference for doctors, and calculates a calcification radian score based on the distribution and geometric morphology of calcified plaques, so that the accuracy of diagnosis is improved. And the severity of lesion can be evaluated more accurately by quantitatively analyzing the poor attachment degree of the stent and the tissue prolapse volume.

The invention provides a cancer early-stage dynamic risk prediction method and device, equipment and a storage medium, and relates to the technical field of data management and risk assessment. The method comprises the following steps: acquiring health medical data, key gene data and behavioral habit data of a target user; configuring a dynamic risk prediction model based on the key gene data and the behavior habit data to obtain a dynamic risk prediction model corresponding to the target user; and inputting the health medical data into a dynamic risk prediction model corresponding to the target user to obtain predicted health medical data of the target user, and performing cancer early-stage dynamic risk prediction on the target user based on the predicted health medical data. According to the method, the health condition of an individual can be comprehensively analyzed from multiple dimensions, the change of the physical condition of a patient can be better adapted, and a more reliable basis is provided for early diagnosis and treatment of cancers.

The invention belongs to the technical field of gene detection, and particularly relates to application of a CFTR gene mutation combination, an amplification reagent and a product in preparation of a CF risk assessment product. The CFTR gene mutation combination consists of an intron region mutation combination and an exon region mutation combination, based on the CFTR gene mutation combination, the invention further develops an amplification reagent and a CFTR gene mutation detection product, the amplification reagent and the CFTR gene mutation detection product comprise a primer combination for multiplex PCR amplification of the CFTR gene mutation combination, and the primer combination has the characteristics of high accuracy, high specificity and high sensitivity, can accurately detect related gene mutation, and provides a reliable basis for risk assessment and diagnosis of CF. When the amplification reagent and the CFTR gene mutation detection product are combined with sweat chlorine detection for use, the diagnosis rate of CF can be remarkably improved, and the amplification reagent has important application value in clinical diagnosis of CF and is expected to provide more powerful support for early diagnosis and treatment of CF patients.

The invention provides an autoimmune disease antibody detection system based on a biosensing technology, which is characterized by comprising a data acquisition module, a data processing module, a detection analysis module and an interaction management module, the data acquisition module is used for acquiring comprehensive detection information of the autoimmune disease of a patient, the data processing module is used for preprocessing the comprehensive detection information, and the detection analysis module is used for analyzing and evaluating the autoimmune disease risk of the patient in combination with the preprocessed comprehensive detection information; the interaction management module is used for managing information data in the system and completing information interaction between the system and the outside; by integrating a biosensing technology, intelligent data analysis and dynamic health management, high-sensitivity detection, accurate risk assessment and personalized health intervention of autoimmune diseases are realized, and the early diagnosis rate and the health management level are improved.

The invention belongs to the technical field of gene engineering, and relates to an esophageal squamous cell carcinoma diagnostic kit based on a peripheral blood mononuclear cell methylation marker and application. The kit comprises a primer pair which is used for detecting the methylation of a cg05914150 site, a cg06769875 site, a cg07475126 site, a cg08433285 site, a cg10038907 site, a cg17968322 site, a cg19572487 site, a cg20184330 site, a cg22741248 site and a cg22823192 site. According to the detection kit provided by the invention, the type of a detected sample is peripheral blood, the sample is relatively easy to obtain and non-invasive, and the patient compliance is relatively good. Abnormal methylation changes mostly exist in the early progression process of tumors, early diagnosis of ESCC can be achieved through the methylation detection method provided by the invention, and the five-year survival rate of ESCC patients is effectively increased.

The invention belongs to the field of biological detection, and particularly relates to application of an ON detection reagent in auxiliary diagnosis of acute chest pain diseases. It is found that the ON protein is closely related to acute chest pain such as AAD, AMI and APE, the ON serves as a marker, a detection reagent of the ON is prepared into an auxiliary diagnosis product, and great clinical value and practical significance are achieved for early diagnosis and long-term prognosis of the three diseases (especially AAD) and improvement of life quality.

A system for early diagnosis and prognosis prediction of sepsis, according to an embodiment of the present invention, includes: a memory for storing general blood test data of a patient; and a processor, wherein the processor may be configured to pre-process the general blood test data of the patient stored in the memory as data to be input to an artificial intelligence model, input the pre-processed data to the artificial intelligence model trained for early diagnosis and prognosis prediction of sepsis, and obtain a sepsis prediction result from the artificial intelligence model.

The invention relates to the technical field of biological medicines, in particular to a method for using a serine / threonine protein kinase receptor R3 (serine / threonine-protein kinase receptor R3, SKR3) as a cerebrospinal fluid marker for early diagnosis, screening and typing detection of a neurosyphilis patient. According to the invention, the concentration of the SKR3 protein in cerebrospinal fluid or serum is used as an index for early diagnosis of neurosyphilis, typing detection of symptomatic neurosyphilis, asymptomatic neurosyphilis, neurosyphilis and recessive syphilis, nerve injury degree evaluation and the like; the index can be independently used or used as a main reference index to be combined with other indexes to complete diagnosis screening, typing and nerve injury evaluation of the neurosyphilis. The sensitivity of the SKR3 as the marker is high, early warning and screening of the asymptomatic neurosyphilis can be achieved, and medical staff can give a reasonable treatment scheme to patients suffering from the asymptomatic neurosyphilis as early as possible.

The invention relates to the technical field of biological medicine, in particular to application of cerebrospinal fluid CTSC protein as a marker in preparation of neurosyphilis or nerve injury diagnosis products. The CTSC protein is also called Dipetidyl petidease 1, and according to the research conclusion of the invention, the CTSC protein can be used as a cerebrospinal fluid marker for early diagnosis, screening and typing detection of neurosyphilis patients. By measuring the concentration of CTSC protein in cerebrospinal fluid, the concentration is used as an index for early diagnosis of neurosyphilis, typing detection of symptomatic neurosyphilis, asymptomatic neurosyphilis, neurosyphilis and recessive syphilis, and nerve injury degree evaluation. The index can be independently used or used as a main reference index to be combined with other indexes for completing diagnosis screening, typing and nerve injury evaluation of the neurosyphilis, and the index has the advantages of being good in sensitivity, high in specificity and the like.

A multi-modal AI model for assisting knee joint tuberculosis diagnosis belongs to the field of assisting knee joint tuberculosis diagnosis and comprises a 3D image modal input module, a text modal input module, a test result modal input module, a multi-modal feature fusion module and a large language module. Aiming at the problems that the early diagnosis time of knee joint tuberculosis is too long and subjective interpretation is different, the diagnosis accuracy can be improved while the diagnosis time is shortened; according to the invention, an M3D model framework is optimized, CT / MRI images, laboratory indexes and text data are fused, and a multi-modal feature interaction auxiliary diagnosis system is constructed; according to the invention, accurate identification of early lesions of knee joint tuberculosis is realized, the misdiagnosis rate is reduced, and efficient and objective quantitative decision support is provided for clinic; according to the invention, the problems of high misdiagnosis rate and long flow caused by insufficient specificity in early diagnosis and examination of knee joint tuberculosis are solved through technical innovation.

The invention provides a serum tumor marker detection array optimization method and system.The method is applied to a control module of an array device.The method comprises the steps that when a multi-needle detection platform conducts serum tumor marker detection on a preset sample, preliminary expression information sent by the multi-needle detection platform is obtained; acquiring patient medical history information, iconography information and pathology information corresponding to the preset sample; inputting the preliminary expression information, the patient medical history information, the iconography information and the pathology information into a pre-constructed Bayesian analysis model, and outputting a prediction weight, a prediction disease progress direction and a correlation marker corresponding to each tumor marker to generate target information; wherein the target spot information comprises a plurality of target markers; and controlling the multi-needle detection platform to perform serum tumor marker detection on the preset sample again according to the target spot information to complete array optimization of serum tumor marker detection. Therefore, an efficient and innovative solution is provided for early diagnosis and monitoring.

The invention relates to the technical field of medical information systems, and particularly discloses an early diagnosis data management system for a patient with schistosome hepatic fibrosis, which is used for acquiring blood biomarker data of the patient, and comparing and analyzing real-time blood biomarker data and historical blood biomarker data to obtain early diagnosis data of the patient with the schistosome hepatic fibrosis. Classifying the blood biomarker data into reliable data and unreliable data using a logistic regression model; calculating a variability deviation index and a logic deviation index of the blood biomarker data through a dynamic time warping and auto-encoder technology, and evaluating time stability and internal logic consistency of the blood biomarker data; performing normalization processing on the variability deviation index and the logic deviation index, and calculating an accuracy coefficient of the data; dividing the data into accurate data and inaccurate data according to the accuracy coefficient, and transmitting the accurate data to a patient management system.

The invention discloses an MDD early-stage dynamic diagnosis method based on a multi-modal attention network, and relates to the technical field of MDD diagnos.According to the method, early-stage diagnosis analysis is carried out on various types of mental diseases through multi-modal data dynamic fusion and a layered attention mechanism in combination with a multi-modal attention network diagnosis model; the fitting degree of the representation data of the target diagnosis user and various types of mental diseases is deeply analyzed, the diagnosis cycle for the target diagnosis user is further dynamically set, the situation of diagnosis errors caused by one-time diagnosis of multi-modal data is avoided, corresponding diagnosis cycles are customized for different target diagnosis users, and the diagnosis accuracy is improved. The accuracy, the timeliness and the clinical practicability of MDD early diagnosis are remarkably improved, the reliability of clinical decision and the resource allocation efficiency are further enhanced, and an innovative technical scheme is provided for precise prevention and control of mental diseases.

The invention provides a product for gene detection of congenital central pulmonary insufficiency syndrome and application of the product, namely, a method for screening patients with the congenital central pulmonary insufficiency syndrome is established on the basis of obtaining an SNP (Single Nucleotide Polymorphism) which can cause the congenital central pulmonary insufficiency syndrome through screening. Wherein the SNP site is located at the 194th site of a nucleotide fragment of which the nucleotide sequence is SEQ ID NO: 1, and is Tgt; c mutation. On the basis that the SNP which can cause the congenital central pulmonary insufficiency syndrome is obtained through screening, a method for screening the congenital central pulmonary insufficiency syndrome patient is established, so that an effective CCHS gene diagnosis approach is provided, and the CCHS can be quickly and accurately diagnosed. The method disclosed by the invention has the advantages of high sensitivity, strong specificity, simplicity and convenience in operation and the like, and is suitable for early diagnosis and genetic counseling of the CCHS.

The invention belongs to the field of intelligent medical treatment, and particularly relates to a method, equipment, medium and program product for predicting RMPP based on lung microvascular changes. The method comprises the following steps: S101, acquiring a CT image of a pediatric MPP patient; s102, the CT image is extracted to obtain a quantitative PBV parameter, the quantitative PBV parameter comprises BV10%, and BV10% refers to the percentage of the blood volume in a blood vessel with the blood vessel cross section area larger than 10 mm < 2 > in the total lung blood volume; and S103, if BV10% is higher than a first threshold value, judging that the pediatric MPP patient develops into a prediction result with high RMPP risk, and otherwise, outputting a prediction result with low RMPP risk. According to the application, the prediction effect on the occurrence and development of the RMPP is found by quantitatively measuring the microvascular change in the CT, and the clinical value of early diagnosis of the RMPP is realized, so that the clinical early treatment is driven.

The invention discloses a gynecological tumor chemotherapy patient carry-on remote follow-up visit method and system based on an AI large model, relates to the field of medical treatment, and solves the problem that an existing gynecological tumor chemotherapy patient carry-on remote follow-up visit method is poor in effect. The method comprises the following steps: S1, carrying out periodic heart rate index monitoring on a gynecological tumor chemotherapy patient in a carry-on index monitoring period, and obtaining a heart rate carry-on monitoring abnormal value according to a monitoring result, S2, carrying out periodic blood oxygen index monitoring on the gynecological tumor chemotherapy patient in the carry-on index monitoring period, and obtaining a blood oxygen carry-on monitoring abnormal value according to the monitoring result, s3, the gynecological tumor chemotherapy patients are divided into the first follow-up diagnosis type gynecological tumor chemotherapy patients and the second follow-up diagnosis type gynecological tumor chemotherapy patients, remote follow-up diagnosis early warning is carried out, the accuracy of follow-up visit results is effectively improved, and therefore guarantee is provided for the clinical treatment effect of the patients.

The invention discloses a biomarker for detecting venous thrombosis and application of the biomarker, and belongs to the technical field of molecular markers, the biomarker is ESM1, early diagnosis and positive prevention of venous thrombosis embolism can be achieved by monitoring the marker, and compared with a diagnosis method in the prior art, the biomarker has the advantages that the biomarker is simple and convenient to use. The marker provided by the invention is a plasma-derived biomarker, can avoid invasive diagnosis, is minimally invasive in sampling, is easy to detect, does not cause body injury, and does not need to use a large instrument. Meanwhile, the ESM1 and D-dimer combined detection improves the sensitivity and accuracy of the existing serological detection method (D-dimer detection) for judging thrombus, and can be used for early prevention and treatment effect evaluation of venous thrombosis.

The present invention relates to a method and system for early diagnosis of lesions configured to enable early diagnosis of lesions by including functions for preprocessing, lesion detection, statistical analysis, visualization, and personalization adjustment based on Whole Slide Image (WSI) data. More specifically, the invention relates to a method for early diagnosis of lesions based on Whole Slide Image (WSI) data using an early lesion diagnosis system, comprising: a step of receiving and preprocessing said WSI data; and a step of detecting lesions by applying an artificial intelligence model to the preprocessed high-resolution image.

The present invention relates to exosome-derived biomarkers for diagnosing arteriosclerosis. The biomarkers of the present invention exhibit specific changes in expression associated with arteriosclerosis, particularly during the early stages of the disease, and thus are highly effective for early diagnosis and prognosis of arteriosclerosis. Particularly, the 3D arteriosclerosis model of the present invention closely mimics the arterial condition of actual patients, allowing for the combined use of the biomarkers and the model to enable early diagnosis, personalized treatment development, and precise monitoring of disease prognosis.

This invention proposes a random forest-based diagnostic method for Pneumocystis jirovecii pneumonia (PCP), comprising the following steps: collecting case data to form a raw dataset; cleaning and normalizing the feature encoding of the raw dataset; calculating the importance score of each feature based on three indices: permutation importance, Gini importance, and mutual information, converting it into a feature sampling probability distribution, and selecting features with sampling probabilities higher than a preset threshold; constructing a random forest model and training and optimizing it; validating the model performance using a test set and outputting the trained PCP diagnostic model; inputting standardized test samples into the diagnostic model and outputting diagnostic results. This invention solves the industry problems of intolerance to traditional invasive diagnoses, high false positive / false negative rates in routine serological tests, and insufficient generalization ability of existing machine learning models, providing non-invasive, efficient, and universal technical support for early PCP diagnosis, and is suitable for clinical application in medical institutions at all levels.