181 results about "Disease risk" patented technology

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention discloses a chronic disease risk prediction method and system fusing a knowledge graph and a large language model, and the method comprises the following steps: obtaining a natural language problem related to a chronic disease, and carrying out the semantic analysis; according to the analysis content, hypothetical questions and answers related to chronic diseases are generated through a large language model, and key entities are extracted; mapping the key entities to corresponding nodes in a medical knowledge graph, exploring a semantic path and a causal relationship between the key entities, and constructing an inference chain pointing to potential disease risks from acquired information; introducing a fragment granularity sensing mechanism, performing fine granularity analysis on each fragment in the reasoning chain, and rearranging and optimizing a link sequence; and based on the optimized inference chain, converting the question and answer result into a structured diagnosis result for visual display. According to the method, the whole process from question asking to answer generation of the patient is optimized, the efficiency and accuracy of chronic disease risk prediction are effectively improved, and meanwhile, personalized health management service is provided for the patient.

The invention relates to the technical field of newborn medical big data risk assessment, and discloses a newborn disease risk assessment method based on big data analysis. The method comprises the following steps: acquiring an initial multi-modal health record of a newborn from a multi-heterogeneous medical data source; carrying out fusion analysis on the data, and constructing a risk assessment map driven by an event; starting a continuous risk tracking process, performing hierarchical risk scanning by using the map, and identifying a stable risk cluster, an evolution risk cluster and an isolated risk signal; deducing a hierarchical risk management and control plan based on the spatial distribution and time evolution mode of the risk cluster; at the end of each period, integrating latest clinical treatment feedback and monitoring readings, and carrying out reverse calibration on entity node states and relation edge strength in the atlas; and dynamically recombining a measure execution sequence and resource allocation in the plan according to the calibrated atlas. According to the invention, dynamic and continuous evaluation and self-adaptive intervention planning of the neonatal health risk are realized.

The invention discloses an earth and rockfill dam illness feature mining method and system based on historical text data, and the method comprises the steps: collecting earth and rockfill dam historical illness data, constructing an earth and rockfill dam illness diagnosis text corpus set, carrying out the structured preprocessing of the corpus set, and obtaining an illness feature and danger removal measure text corpus set; generating a structured word sequence subset through a word segmentation tool; processing the structured word sequence subset by adopting an LDA topic model, determining an optimal topic number through a confusion degree curve, and outputting a final topic and a corresponding topic word; constructing a visual network graph based on the subject term co-occurrence frequency; and carrying out centrality analysis on nodes in the visual network diagram, identifying key nodes in the visual network diagram, quantitatively analyzing association rules of the danger characteristics and danger removing measures, and completing feature mining of the earth and rockfill dam danger. The problems that traditional manual diagnosis is high in subjectivity and low in utilization rate of historical engineering data are solved, and intelligent auxiliary decision making of the earth and rockfill dam danger is achieved.

The invention discloses a disease risk assessment method and screening device based on a multi-group student physical collaborative digital network, and relates to the field of intelligent medical detection. In order to solve the defect that multi-omics-level system collaborative analysis and robust risk assessment are difficult to realize in the prior art, the technical scheme provided by the invention is as follows: acquiring a plasma sample, acquiring a spectral signal by adopting an attenuated total reflection Fourier transform infrared spectrum, and establishing a plasma spectrum digital information space; the method comprises the following steps: constructing a biological collaborative digital network containing four nodes of protein, lipid, saccharides and nucleic acid based on pathophysiology priori knowledge, and defining node strength, edge weight and network collaborative efficiency; a health baseline configuration file is established by using a health sample, a standardized deviation score of a to-be-tested sample is calculated, a comprehensive risk score is obtained, a disease screening result is output in combination with a machine learning model, and digital evaluation of multi-omics collaborative characteristics is realized. The method is suitable for non-invasive rapid screening and risk assessment work of neurodegenerative diseases and mental diseases.

The invention relates to the technical field of geological survey, in particular to a rapid stratum disease risk detection method based on a ground penetrating radar, and solves the technical problem of depth positioning misalignment caused by propagation parameter fluctuation due to heterogeneity of an underground medium in stratum disease detection of the ground penetrating radar in the prior art. The method comprises the following steps: acquiring a reflected wave signal at each acquisition moment through a ground penetrating radar; performing signal decomposition processing on the reflected wave signal, separating an intrinsic mode component representing dielectric noise, and performing feature extraction on the intrinsic mode component to determine a dielectric noise intensity index; constructing a dielectric noise intensity sequence according to the dielectric noise intensity index, and performing spatial-temporal characteristic analysis to determine the confidence coefficient of stratum diseases; and according to the dielectric noise intensity index and the stratum disease confidence coefficient, in combination with a pre-calibrated stratum average dielectric constant, constructing a depth correction weight, carrying out adaptive correction on the original stratum disease depth, and outputting the corrected stratum disease depth.

The invention relates to the technical field of disease monitoring, in particular to a multi-device collaborative disease risk prediction system. The system comprises a non-contact type signal receiving and transmitting device, a gait collecting device, an intelligent bracelet and a central processing unit, the non-contact type signal receiving and transmitting device captures macroscopic movement data of a user through a millimeter wave signal transmitting and receiving module, and the macroscopic movement data comprises periodic characteristics of gaits, movement tracks of four limbs and posture changes; the gait acquisition device acquires plantar pressure distribution data of a user through a pressure sensor array; the smart bracelet collects motion data of a user through an accelerometer and a gyroscope; and the central processing unit carries out space-time alignment on data of the non-contact signal transceiving device, the gait acquisition device and the intelligent bracelet through a timestamp synchronization algorithm, so that the disease risk is predicted according to the analysis data. The system supports all-weather and multi-scene monitoring, is suitable for disease risk assessment, and can early warn cardiovascular events and other health problems.

The invention provides a chronic respiratory system disease risk early warning method and device based on artificial intelligence and a medium. The method comprises the following steps: firstly, acquiring physiological monitoring information of wearable equipment of a patient; inputting the physiological monitoring information into the risk prediction model to obtain a risk early warning result; wherein the risk prediction model is established according to patient medical record information, follow-up visit information and historical physiological monitoring information; the risk prediction model is a Transform-LSTM (Long Short Term Memory) double-branch integrated model. According to the method, physiological monitoring information collected by wearable equipment in real time is input into a Transform-LSTM double-branch integrated model constructed on the basis of patient medical record, follow-up visit and historical physiological monitoring multi-source information, so that a long-distance dependency relationship of multi-modal data is captured by means of a Transform branch to identify a potential risk trend in a stable period; and the time sequence dynamic characteristics of physiological monitoring information are captured through an LSTM branch to perceive short-term signal mutation in an acute exacerbation period, so that accurate early warning of the whole course risk of the chronic respiratory system disease is realized.

The invention discloses an intelligent glasses system and method for health risk monitoring of old people, and belongs to the technical field of intelligent health monitoring. The system is integrated on a glasses body and comprises a multi-mode sensing module, an edge calculation module and an interaction module. The method comprises the following steps: synchronously acquiring physiological parameters and behavior posture data of a user, extracting head posture change characteristics for representing body instability on a local edge side, performing time sequence fusion on the head posture change characteristics and the physiological parameter data, and inputting the fusion result into a pre-trained risk identification model to identify a falling risk or a sudden disease risk. And early warning is triggered when the risk is identified. According to the invention, non-inductive, continuous and accurate monitoring and early warning of high-risk scenes of old people are realized, the defects of single data, high false alarm rate and large response delay of existing equipment are overcome, and the real-time performance and reliability of health monitoring are remarkably improved.

The invention relates to an aquaculture disease risk prediction method and device, equipment and a storage medium. The method comprises the steps of collecting an original data set, performing multi-scale decomposition on water quality parameter time series data, generating an intrinsic mode component set and a residual term, and screening components associated with historical disease tags from the intrinsic mode component set to obtain a sensitive mode subset; generating an environmental state prediction value based on the subset, generating an environmental baseline value based on the residual term, and calculating a dynamic weight based on the environmental state prediction value and the environmental baseline value; and performing feature extraction on the pathogen activity data, generating pathogen spatio-temporal features, generating joint features according to fusion of the dynamic weight and the environmental state predicted value, and generating a dynamic risk probability according to the joint features. According to the method, the accuracy and timeliness of aquaculture disease risk prediction are improved by fusing the sensitive mode subset after multi-scale decomposition and the spatio-temporal characteristics of the pathogens and dynamically adjusting the weight according to the environmental state prediction value and the baseline value.

The invention discloses an evolutionary intelligent agent system and method for automatically constructing a disease risk prediction model, and the system comprises a data processing module which is used for obtaining original medical data, calling a large language model to carry out standardization processing on the obtained original medical data; the feature extraction module is used for calling a large language model to extract features related to the target disease in the standardized medical data, and associating the extracted features with corresponding target disease tags to obtain a structured data set; the model construction and evaluation module is used for inputting the data set into a multi-objective Cartesian genetic programming MOCGP algorithm to construct a plurality of disease risk prediction models, and optimizing the plurality of disease risk prediction models through two optimization objectives of model interpretability and prediction performance, and at least one candidate disease risk prediction model with optimal prediction performance and high interpretability is obtained.

The application provides a thymus disease risk prediction method and system based on cross-modal feature interaction, which comprises the following steps: respectively preprocessing CT image data, MRI image data and image reports; sequentially performing word segmentation, vector conversion, feature extraction and pooling on the third data, inputting the labeled semantic feature vector into an image generation network to generate a PET-CT image; inputting the first data, the second data and the PET-CT image into the same medical mamba feature extraction network respectively to obtain the first modal feature corresponding to the first data, the second modal feature corresponding to the second data and the third modal feature corresponding to the PET-CT image; performing feature fusion interaction on the first modal feature, the second modal feature and the third modal feature to obtain a fusion feature, and obtaining the thymus disease risk according to the fusion feature. The application can greatly improve the thymus disease prediction accuracy.

The invention discloses a disease health management method and system and a storage medium, and relates to the field of medical diagnosis and treatment, and the method comprises the steps: extracting target information representing a user intention and / or first data in first dialogue data, and determining a target function matched with the target information based on the target information; based on the target information, the target function and a preset big language model, determining second dialogue data containing reply information of disease risk assessment or reply information of health education corresponding to the target information, and if the target function is a risk assessment function, realizing the risk assessment function through a risk prediction model, the corresponding second dialogue data can help the prediabetic patient to understand risk assessment; if the target function is a health education function, the corresponding second dialogue data can help the prediabetic patient to better perform self management; according to the guiding mode based on the user intention, the patient can clearly know the health condition of the patient, and the user is guided to maintain a healthy lifestyle.

The invention provides a data processing device for pulmonary tuberculosis disease risk assessment and application thereof. The data processing device provided by the invention comprises a memory, a processor and a computer program stored on the memory, and the processor executes the computer program to realize the following steps: receiving gene expression quantity data in a blood sample of a subject, performing pulmonary tuberculosis disease risk grouping on the subjects according to the gene expression quantity data; the gene at least comprises an aspartate beta-hydroxylase structural domain 2 gene (ASPHD2). The data processing device provided by the invention can be used for evaluating or assisting in evaluating the pulmonary tuberculosis disease risk of the subject, improves the accuracy of risk evaluation, and has a relatively high clinical application value.

The invention discloses a disease detection, prevention and control method and system based on dynamic monitoring data. The method comprises the steps that traffic dust, heat-electricity, cable ozone, micrometeorology and individual respiration exposure data are collected through roadside multiple sensors, a photovoltaic inverter power monitoring module, a cable connector arc sensor, a distributed micro-meteorological station and wearable respiration monitoring equipment, so that initial data are obtained; performing preprocessing and quality control on the initial data, extracting and fusing respiratory tract stress features by using a self-attention mechanism, a genetic algorithm and a health degree mapping function, and generating a health risk score; and triggering graded early warning according to the health risk score, and predicting a doctor seeing peak. By implementing the method, the risk of the respiratory disease can be accurately early warned and closed-loop intervention can be implemented by fusing dynamic monitoring data of infrastructures, the method has the advantages of being accurate in monitoring, timely in early warning, cooperative in prevention and control and the like, and the defects of traditional disease monitoring, prevention and control are effectively overcome.

The invention relates to the technical field of machine learning, in particular to a hybrid expert intelligent health analysis system based on a large model, which can give consideration to the high efficiency of a traditional machine learning model and the knowledge reasoning advantage of a large language model, so that the disease risk prediction accuracy is improved, and the disease risk prediction efficiency is improved. And the intelligent and personalized level of health care analysis is enhanced. Preprocessing and cleaning the collected multi-source health data, performing feature analysis and classification on the input structured data by adopting multiple machine learning models for different application scenes, and outputting probability results of individuals under different disease risk dimensions; and inputting a risk prediction result into a large language model which is subjected to LoRA fine tuning based on a professional disease knowledge base, and generating a personalized intelligent health analysis and intervention scheme.

The application provides a highland thrombosis disease risk early warning system, comprising: a sensing acquisition module, which is used for acquiring patient characteristic data, the patient characteristic data including physical sign data and individual baseline data; an index analysis module, which is used for preprocessing the patient characteristic data and generating risk indexes; a risk assessment module, which is used for assessing a risk coefficient according to the risk indexes; and a risk early warning module, which is used for obtaining a risk degree corresponding to the risk coefficient according to a pre-constructed risk classification standard, and implementing corresponding risk early warning according to the risk degree. The application solves the problem in the prior art that a portable health monitoring device cannot accurately measure core hemodynamic parameters closely related to thrombosis, and also cannot accurately perform risk early warning on highland thrombosis diseases according to the acquired parameters.

The invention discloses a cardiopulmonary disease risk prediction method and system based on self-supervised diffusion enhancement. The method comprises the following steps: firstly, carrying out data acquisition and preprocessing; compressing the original audio data into a potential space through a DCVAE, and adding noise at a specified time step to obtain a noise-added potential representation; then, carrying out self-supervised feature learning by utilizing a DiT model of a diffusion converter, and collecting middle layer features through DiT model forward propagation; and finally, carrying out pyramid pooling on the intermediate features for risk prediction. The advanced signal processing technology and the deep learning model are combined, so that efficient and accurate disease risk prediction can be realized, and important help is provided for early diagnosis and personalized treatment of diseases; the method not only has important significance in the medical field, but also lays a foundation for the development of intelligent health monitoring equipment.

We provide the system. [Solution] A means for receiving and analyzing a saliva or blood sample, A method for calculating disease risk using analyzed DNA data, A means of saving the calculation results to the user's profile, A means of providing analysis results and preventive measures in response to user requests, A method for identifying ancestral roots based on the user's DNA data, A system that includes means of identifying other users with similar roots and providing them with local information.

The present application relates to the technical field of biological detection and disease risk prediction, in particular to a periodontal disease risk prediction system based on cardiovascular and oral flora data fusion, comprising a multi-modal data acquisition module, a heterogeneous data preprocessing module, a cross-modal feature fusion module, a double-branch risk prediction model module and a risk grading output module. The multi-modal data acquisition module non-invasively acquires cardiovascular physiological data and oral mucosa swab flora sequencing data of the subject; the preprocessing module denoises and normalizes the time series physiological data, and completes species annotation and abundance correction of the flora data; the cross-modal feature fusion module realizes adaptive alignment and deep fusion of the two types of heterogeneous data through a cross-modal attention mechanism; the double-branch prediction model outputs a risk probability after being trained by a comorbidity correlation data set; and the risk grading module divides the individual risk level in combination with a dynamic threshold. The whole-body cardiovascular and oral flora data are deeply fused to improve the prediction accuracy.

The present application relates to the technical field of intelligent wearable medical equipment, in particular to an intelligent bracelet for monitoring neurological diseases, which comprises a wristband body and an electronic monitoring system, the electronic monitoring system comprising a multi-modal physiological signal acquisition module, a microprocessor control unit, a disease characteristic analysis module, an early warning feedback module, a wireless communication module and a power management module. By collecting multi-dimensional physiological signals, Parkinson's disease targeting features, epilepsy targeting features and double disease auxiliary features are extracted, then through the basic differentiated contribution weight of the convolution long short-term memory network model and the dynamic output distribution weight adjustment strategy triggered by the pathological threshold, the spatio-temporal fusion analysis is realized and the risk probability of the two diseases and the normal state probability are output, the early warning feedback module outputs differentiated prompts according to the risk probability, the wireless communication module synchronizes data or sends emergency alerts through dual-mode communication, and the power management module realizes power supply and low power reminder.

The invention relates to the technical field of integrated learning, in particular to an intelligent livestock disease prevention system which comprises a local geometric representation module, a performance model preliminary screening module, a decision logic verification module and an individual risk early warning module. According to the method, a plurality of base learners are evaluated and screened by mining a local geometrical relationship among physiological data and constructing a weight vector, a candidate model set adapted to current data features is formed, secondary preferential selection is performed according to the feature contribution degree concentration degree of each model during prediction, and a model with the most decision logic certainty is selected to execute a prediction task. The dynamic construction and preferential mechanism enhances the adaptability of the model to data fluctuation, avoids the limitation of generalization ability of a single model, and significantly improves the accuracy of disease risk identification and the stability of system operation.

The invention belongs to the technical field of diagnosis waiting management, and particularly relates to a diagnosis waiting sorting intelligent management system based on multi-source data analysis, which comprises a diagnosis waiting data fusion preprocessing module, an illness state emergency quantitative analysis module, a multi-dimensional diagnosis waiting priority decision module and an intelligent sorting result visualization module. According to the method, the illness state emergency degree is quantitatively calculated from three dimensions of symptom emergency degree, sign abnormity degree and basic disease risk through an illness state emergency degree quantitative analysis module, and it is ensured that medical resources are reasonably inclined to high-emergency-degree patients; a multi-dimensional waiting priority decision-making module calculates waiting priority coefficients through multi-dimensional analysis and sorts the waiting priority coefficients, an intelligent sorting result visualization module displays waiting queues, medical safety, waiting fairness and diagnosis and treatment efficiency are effectively balanced, and the diagnosis and treatment efficiency is improved. And accurate and reasonable sorting of the waiting patients is realized.

The invention discloses a cerebrovascular disease risk prediction method, and particularly relates to the field of disease risk prediction, and the method comprises the following steps: S1, collecting basic clinical data, dynamic physiological parameter data and image feature data of a target object, and constructing a data set; s2, performing feature extraction on the basic clinical data, the dynamic physiological parameter data and the image feature data, and fusing the extracted features to obtain a fused feature set; s3, constructing a fusion prediction model through the fusion feature set; and S4, inputting the data set into the fusion prediction model, and outputting a risk prediction result. According to the method, the information short board of a single data type is made up, different types of data are converted into a unified computable form through standardized quantitative processing, core information with risk indication significance is effectively screened out in combination with abnormal data proportion analysis and dynamic fluctuation intensity evaluation, and the recognition capability of early potential risks of diseases is remarkably improved.

The application discloses a disease risk stratification method and system based on reinforcement learning, relates to the technical field of reinforcement learning, and comprises the following steps: acquiring multi-modal time series data of a target patient in a monitoring process; dynamically fusing the multi-modal time series data to extract a patient state representation vector; inputting the patient state representation vector into a pre-trained risk stratification model to output a risk stratification action according to a current strategy; generating and pushing a clinical monitoring prompt according to the risk stratification action; after a preset time window, acquiring response data of the patient to clinical intervention, calculating a reward signal, and updating the risk stratification model by using the reward signal. The technical problems that the existing disease risk stratification model is static and fixed, cannot dynamically optimize a decision strategy according to the effect of clinical intervention, and results in insufficient accuracy of risk stratification results are solved.

According to the endocrine-related disease examination guidance app system and method provided by the invention, based on the analysis of the endocrine disease knowledge graph, the recommended examination items are more targeted, the examination required by the endocrine disease possibly suffered by the patient can be accurately covered, the accuracy of disease diagnosis is improved, the unnecessary examination items are reduced, and the examination efficiency is improved. The medical cost of the patient is reduced. The positioning information of the patient is obtained by calling the positioning function of the mobile phone, and the positioning information and the selected examination item are packaged together and sent to the medical institution adaptation module. The adaptive endocrine examination mechanism is recommended according to the positioning information of the patient, so that the patient can conveniently select a professional adaptive mechanism close to the patient, the trouble that the patient finds a proper examination mechanism is solved, and the medical experience of the patient is improved. And risk assessment is performed on the examination result and a report is generated, so that the patient can know own health condition and potential disease risk in the first time, and further measures can be taken in time by the patient.

An object of a system according to an embodiment is to provide an optimal meal recipe or exercise menu based on body data of a user and a goal and to evaluate a disease risk.SOLUTION: A system according to an embodiment includes a data input unit, a recommendation unit, a schedule generation unit, and a risk evaluation unit. The data input unit inputs body data of a user and a target. The recommendation unit recommends an optimal meal recipe based on the data input by the data input unit. The schedule generation unit generates a schedule based on the meal recipe and the exercise menu recommended by the recommendation unit. The risk evaluation unit evaluates a disease risk on the basis of the data input by the data input unit.SELECTED DRAWING: Figure 1

Embodiments of the present disclosure pertain to methods and systems for assessing a subject's vulnerability to developing at least one cardiometabolic-related condition. Such methods and systems generally include the following steps or instructions: (1) receiving a plurality of health-related data of the subject; (2) calculating a risk score from the plurality of health-related data; (3) correlating the risk score to the subject's vulnerability to the cardiometabolic-related condition; and (4) making a treatment decision based on the subject's vulnerability to the cardiometabolicrelated condition.

This invention discloses a method for assessing the risk of ovarian diseases, comprising: a data collection step, including the collection of basic health data of the patient; a preliminary assessment step, including providing a profile of the patient's individual characteristics through a preliminary assessment model and formulating a multimodal medical testing data collection plan, and obtaining a preliminary assessment result; a multimodal feature processing step, used to obtain multimodal data based on the preliminary assessment result and perform multimodal medical data feature processing; an assessment step, obtaining the correlation score between modalities; a correction step, used to correct the assessment process based on the correlation score; and a result generation step, used to generate the assessment result based on the correlation score. According to the above technical solution, the accuracy of ovarian disease risk assessment can be improved, and the diagnostic capability for minor or early-stage lesions can be enhanced.

The present application relates to the technical field of medical information processing, in particular to an abnormal event automatic classification and recording method in a kidney disease follow-up system, comprising collecting kidney function parameters and symptom codes, identifying abnormal sequences, extracting time anchor points and characteristic identifiers, calculating flip frequency and amplitude, constructing a feature vector to evaluate risk and generating event classification records. In the present application, by constructing an abnormal trigger ordered sequence and extracting anchor points to determine composite features, combining symptom flip frequency to construct a persistence intensity coefficient, quantifying disease complexity from the dimensions of time evolution and etiology coupling, using a multi-dimensional feature vector to fuse physiological amplitude and statistical characteristics, obtaining disease risk probability values through weighted calculation, realizing the transition from qualitative classification to quantitative risk grading, solving the false alarm and missed alarm problems caused by single index determination, providing dynamic diagnosis basis containing risk level for clinical, significantly improving the recognition accuracy and intervention timeliness of the hidden deterioration trend in kidney disease follow-up.