98 results about "Disease course" patented technology

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention discloses an electronic medical record data synthesis method based on semantic processing, and relates to the technical field of medical informatization, and the method comprises the following steps: S1, constructing a probabilistic medical knowledge graph; s2, generating a semantic representation vector; s3, constructing a multi-dimensional dynamic space-time atlas; s4, generating a discrete personalized disease course event sequence with space-time coordinates; s5, taking the discrete personalized disease course event sequence and the corresponding medical entity semantic representation vector as condition input, guiding the improved TSDiff model to execute an iterative denoising process, and outputting a multi-dimensional random disease course trajectory; s6, forming multi-modal electronic medical record data; and S7, performing multi-dimensional quality evaluation on the multi-modal electronic medical record data. According to the method, the limitations of logic inconsistency, modal splitting and model capability solidification in a traditional synthesis method are overcome, and an efficient and accurate solution is provided.

The invention relates to the technical field of medical monitoring, in particular to an intelligent monitoring and health management system for liver, gall and pancreas postoperative drainage fluid, which comprises a data acquisition module for acquiring core specific indexes of liver, gall and pancreas special drainage fluid, visual images of the drainage fluid, dynamic flow and physiological data of a patient; the intelligent analysis module constructs a hepatobiliary pancreatic postoperative exclusive multi-modal model, deeply couples postoperative disease course time sequence characteristics to identify risks at different stages after the operation, and triggers risk assessment when the operation is abnormal; the early warning module is provided with a light, medium and heavy three-level mechanism, and carries out resource adaptation type grading accurate pushing to a responsible physician and a nurse station terminal in combination with a medical care real-time load and a spatial distance. The health management module fuses individual features of the patient and real-time monitoring data, and constructs a personalized rehabilitation scheme in combination with rehabilitation feedback of the patient; and the data synchronization module transmits model update parameters through an encryption channel, and perfects the complete-cycle health archive of the patient. Therefore, the problems that in the prior art, an early warning mechanism is rigid, and data security and full-period management are insufficient are solved.

The invention provides a medical record generation optimization method based on type differentiation, and belongs to the field of natural language processing, and the method comprises the steps: constructing a medical record type classification system, designing an exclusive generation model and a scene rule for different types of medical records such as a first disease course, a daily disease course and a stage knot, and combining with a clinical feedback dynamic optimization generation strategy, thereby achieving the purpose of optimizing the medical record generation. The medical record generation method is suitable for scenes such as clinical medical record writing, medical teaching medical record construction and medical AI auxiliary diagnosis and treatment systems of hospitals at all levels.

The invention provides a big data resource service-based patient hierarchical intervention method and system, which are applied to the technical field of medical information, and are used for acquiring full-cycle health data of a patient, generating and updating a patient disease course trajectory sequence, analyzing health index change and trend under short, medium and long time scales, and calculating health scores and steady-state coefficients, so as to realize hierarchical intervention of the patient. A layered intervention scheme is generated, detection and personalized intervention of the health state of the patient are achieved, the timeliness and pertinence of medical intervention can be improved, and the disease management effect is optimized.

The invention discloses a complex medical quality management and control index automatic calculation method based on an intelligent agent. According to the index semantic model construction method provided by the invention, the automatic conversion of the medical quality control indexes from a natural language to structured semantics is realized, so that the index definition has computability and mobility, the dependence of manual analysis and script configuration is eliminated, and the standardization, generalization and reuse efficiency of the index definition is remarkably improved. Through multi-source data semantic packaging and an MCP service abstraction mechanism, semantic unification and interface standardization of multi-source heterogeneous data such as electronic medical records, inspection information, disease course records and medical advice management are achieved, and a semantic data layer capable of achieving cross-system access is constructed; the problems of data dispersion, field isomerism and interface incompatibility in a traditional system are effectively solved.

The invention discloses a traditional Chinese medicine teaching and clinical simulation method and system based on a six-channel transmission theory, and particularly relates to the technical field of teaching simulation. The method comprises the following steps: acquiring disease course record data and symptom time sequence data, reconstructing a symptom evolution chain of each case and a corresponding six-channel identification label, and extracting six-channel disease course node time sequence characteristic data; the method comprises the following steps: constructing a single-path six-path transmission candidate network and a six-path transmission candidate topological network to obtain standard single-path six-path transmission network data and multi-branch six-path transmission topological structure data, and performing graph fusion and path weight re-calibration to generate six-path transmission topological network model data; and judging whether the current disease course node meets a trans-meridian bifurcation simulation triggering condition or not in combination with the symptom combination of the patient, and generating corresponding teaching feedback data and contrast learning data, so that a six-meridian transmission multi-branch nonlinear evolution process is presented in informatization teaching and clinical simulation scenes.

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 relates to the technical field of medical image processing and artificial intelligence, in particular to a skin cancer risk assessment method and system combining a vision and language model, and the method comprises the steps: collecting lesion images, extracting color edge textures, numbering abnormal regions, reading medical records, extracting symptom and time information, recombining semantic contents, matching image-text regions, and fusing expression fragments. And extracting continuous features, generating a combined sequence, mapping image-text contacts, and outputting a skin cancer risk identification result. According to the method, color jump and edge closure are dynamically read through a sliding area, a lesion area is marked, the structure capture granularity is improved, medical records are rearranged, symptom and time information is extracted, expression coherence is enhanced, the structure jump and symptom fragment blocks are compared, image-text mapping is established, and evolution consistency is guaranteed through sequence arrangement; boundary and disease course docking is clear in risk affiliation, and identification precision is improved.

The invention discloses a medical record data association mining method and system based on reinforcement learning, and relates to the technical field of medical record data association mining, and the method comprises the following steps: obtaining medical record historical data, carrying out multi-modal feature extraction, and constructing a medical record data multi-modal state vector; constructing state input of a reinforcement learning agent based on the multi-modal state vector, constructing an action space and a reward evaluation mechanism, performing updating and iteration according to the reward evaluation mechanism, and outputting potential correlation information of the medical record; a world model for simulating a patient trajectory is introduced in the reinforcement learning process, patient state transition characteristics are learned according to medical record historical data, and a simulation sample for auxiliary training is generated for reinforcement training; performing interpretable output on the finally obtained medical record potential association information according to an enhanced training result, and constructing a medical record association network; the method effectively solves the problems that multi-modal heterogeneous data fusion is difficult, dynamic evolution of the disease course is difficult to model, and potential association interpretability is insufficient.

The invention relates to the technical field of image or video recognition or understanding, and discloses an areca yellows early warning method based on regular economic forest multi-dimensional symptom analysis, in the method, the unique morphology of areca and various morphological changes which are invisible under trees but visible in remote sensing during the period that areca suffers from yellows are fully utilized, and the areca yellows early warning effect is achieved. And the multiple symptoms are combined to form a multi-symptom matching degree, so that the misrecognition problem can be eliminated in an auxiliary manner when specific symptoms exist. When the specific symptoms do not exist, whether the disease of the current areca-nut forest belongs to the infectious disease or not is indirectly judged according to the distribution characteristics of the multi-symptom matching degree in time and space by utilizing the distribution characteristics and the disease course development rule when the areca-nut is cultivated as an economic arbor, and if yes, the disease of the current areca-nut forest belongs to the infectious disease; and considering that the areca yellows occupy a large proportion in the areca infectious diseases, the pathogen detection cannot be infeasible due to entrainment of a large number of irrelevant diseased plant samples when the pathogen detection is carried out at the moment. The above points are combined to realize early warning of areca yellows.

The invention belongs to the technical field of diabetic foot clinical path management, and discloses a diabetic foot patient path management method and system based on lower limb vasculopathy grading. Mapping the ulcer position to the corresponding perfusion area and the artery segment to generate ulcer space characteristics; calculating a blood supply risk score of the perfusion area and generating a space risk map by combining the blood vessel image detection data and the microcirculation detection data; then collecting time sequence data including an ulcer index, an infection index, a blood supply index and a space risk map, and predicting the progress speed and the risk level of the future ulcer course; and finally, according to the blood supply risk score and the disease course prediction result, generating an individualized path management scheme through a joint triggering rule. According to the invention, precise and dynamic management of the diabetic foot ulcer is realized, the healing rate is improved, and the recurrence rate and the medical cost are reduced.

The invention relates to the technical field of sepsis monitoring, and discloses a sepsis monitoring method and system based on multi-modal data, and the method comprises the steps: setting a plurality of sepsis patient types based on influence factors, and generating a plurality of disease course period sequences which comprise a plurality of disease course periods, each disease course period is mapped with a corresponding multi-modal data set; comparing and analyzing the multi-modal data sets of the plurality of disease course cycle sequences to obtain feature data of each disease course cycle, change features of each feature data and a weight coefficient, and constructing a sepsis monitoring model; the real-time characteristic data of the to-be-monitored patient is obtained and input into the sepsis monitoring model, the monitoring result is obtained, whether the early warning instruction is generated or not is judged, the complexity of sepsis and the individual difference of the patient are fully considered, the monitoring accuracy is improved, and powerful support is provided for timely diagnosis and treatment of the sepsis patient.

The invention discloses a herpes zoster skin lesion staging evaluation method and system based on AI image recognition, and relates to the technical field of herpes zoster skin lesion staging evaluation, and the method comprises the following steps: collecting herpes zoster skin lesion image data continuously recorded in multiple periods, synchronously embedding a time identifier in the collection process, and carrying out the time identification; recording the shooting moment, the illumination state and the shooting angle information of each frame of image in a unified manner to form an original image sequence; and based on the original image sequence, according to the progressive sequence of the time identifiers, performing adaptive sorting adjustment on the time intervals between the adjacent frames. According to the method, a stable skin lesion time evolution track is constructed through time identification and a continuous processing flow, so that staging reverse judgment caused by frame order disorder is avoided, and the continuity and reliability of herpes zoster skin lesion staging evaluation are improved; and meanwhile, color, area and texture changes are uniformly incorporated into natural time rhythm constraints, so that a staging result better conforms to a clinical disease course rule, and the safety of remote follow-up visit and intelligent evaluation is improved.

The invention relates to an intelligent medical record generation method and system. The method comprises the following steps: extracting each piece of data in accessed multi-type data and constructing a time-based event; constructing a directed graph arrangement event; traversing the directed graph, and verifying whether an event conforms to a rule base or not by using the rule base containing causal logical reasoning in the traversing process; and traversing the directed graph based on the medical record generation condition, and sorting the traversing result based on the generation type to obtain a medical record document. According to the method, multi-source data are fused in the generation process, and cross-modal and cross-cycle long text integrated generation is achieved; performing real-time calling and verification on related knowledge based on the ternary knowledge base to realize dynamic enhancement; a time-event chain reasoning mechanism and fine-grained reference are realized based on a directed graph, so that the traceability of the time sequence, causal logic and information sources of disease course records is ensured, posterior verification and feedback after quality inspection are supported, and a high-quality medical record generation basis is provided.

The invention relates to a tailorable anti-fungal multi-point penetrating press-fit sewing dressing and preparation and application thereof.The method comprises the following steps that a high polymer material solution and an anti-fungal medicine solution are mixed, liquid-phase matter is subjected to defoaming treatment, spinning is conducted, curing is conducted through a fixing solution, and the tailorable anti-fungal multi-point penetrating press-fit sewing dressing is obtained; performing rinsing, drying, opening, needling, freeze-drying and vacuum drying on the obtained condensate, and then compounding the condensate with a gelatin layer and a surface layer to obtain the tailorable antifungal multi-point penetrating press-fit sewing dressing. Compared with the prior art, the preparation method has the advantages that an antifungal drug is introduced and compounded with a high polymer material, the compound is prepared into the drug-containing fiber, and then the drug-containing fiber layer and other material layers are prepared into a final product through a modern textile technology. The product can realize long-acting sterilization and bacteriostasis, and solves the problems of difficult administration, poor curative effect, repeated disease course, patient compliance and the like of body surface fungal infection at present.

The application discloses a kind of probe set and its combination 3D printing microfluidic chip to the method for analyzing specific glycoprotein sialic acid on exosome surface, belong to biochemical analysis technical field.The present application is based on 3D printing microfluidic chip combined with three-probe co-localization signal amplification strategy, nucleic acid signal is converted into fluorescent signal, by directly reading fluorescent signal intensity, realize the analysis of sialic acid signal on the glycoprotein on the surface of exosome, which provides a new method for overcoming the defects and limitations of exosome clinical application in the prior art, reduces the loss of sample, improves the sensitivity of reaction, and provides a feasible method for exploring the possible relationship between different exosomes and their parent cell specific glycoprotein sialic acid and disease development, and using sialic acid for tumor screening and disease course determination.

The presently disclosed subject matter discloses a system and method for predicting at least one outcome in a subject suffering from an inflammatory bowel disease, the system comprising a processing circuitry configured to: obtain (i) a series of frames of the subject's gastrointestinal tract, and (ii) a machine learning model capable of receiving a series of frames of the gastrointestinal tract of a given subject suffering from inflammatory bowel disease and predicting one or more outcomes associated with the disease course in the given subject; and, predict, utilizing the obtained series of frames of the subject's gastrointestinal tract and the machine learning model, at least one outcome in the subject suffering from the inflammatory bowel disease.

The invention provides an epileptic information management method and system, and relates to the technical field of medical information management. The method comprises the following steps: firstly, performing standardization processing on multi-source heterogeneous data of a patient, and constructing a multi-dimensional patient portrait containing static, dynamic and time sequence features; secondly, performing multi-level clustering on historical patients based on treatment response and disease course evolution similarity; generating a management suggestion according to the association degree of the to-be-managed patient and the clustering cluster and the current management scene; and finally, dynamically updating the portrait and the clustering affiliation based on treatment feedback. According to the invention, by mining time sequence features and establishing a closed loop feedback mechanism, precise classification and dynamic personalized management of epileptics are realized, and the accuracy of clinical treatment recommendation is significantly improved.

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

The application discloses a prediction method and device for the influence of different stage PM2.5 components on diseases and a medium; the method comprises the following steps: collecting sample data of a prospective cohort and preprocessing the sample data to obtain a standardized feature dataset; constructing a multi-state trajectory model; using the multi-state trajectory model to calculate the risk ratio and dynamic transition probability of transition between diseases under different PM2.5 pollutant exposures; constructing an accelerated failure time model; using the accelerated failure time model to quantify the time ratio of transition of each disease state under PM2.5 pollutant exposure, identify the acceleration effect of the disease course, and output the predicted time of state transition; and outputting the risk ratio, dynamic transition probability and predicted time of state transition as the prediction result. The application can improve the accuracy of predicting the dynamic and acceleration effects of PM2.5 components on the whole disease course of metabolic cardiovascular diseases, and provide a scientific decision basis for early health intervention of metabolic cardiovascular diseases.

The invention relates to an adverse reaction attribution analysis method and system based on multi-modal constraint decoding, and the method comprises the steps: constructing a three-dimensional splicing vector comprising text, numerical value and time migration features, and fusing the disease course semantics and physiological spatial-temporal features; a logic bias parameter is used for applying constraint to an output layer of the large language model, uncontrolled text generation is converted into quantitative causal calculation based on probability expectation, and model illusion is avoided; and in combination with mixed simulation verification of group pharmacokinetics and historical case retrieval, it is ensured that an attribution result conforms to a pharmacological mechanism, and accurate quantification and safety early warning of adverse reaction risks are achieved.

The invention belongs to the technical field of pathological science, and discloses application of TLSs in preparation of a product for prognosis evaluation of complex vascular malformation. The product realizes prognosis evaluation based on parameters of maturity, distribution and quantity of TLSs in a focus of complex vascular malformation; when the number of the TLSs in the parameter is larger and the maturity is higher, the associated information of the complex vascular malformation that the disease course is longer and the recurrence possibility is high is correspondingly output. The invention provides a specific evaluation standard of the TLSs in complex vascular malformation, and a prediction application value for prompting disease prognosis recurrence of the TLSs. The TLSs can be used as a pathological index for detecting recurrence prognosis by FAVA and PHOST, and the risk of postoperative recurrence of vascular malformation can be predicted by detecting the distribution, the number and the maturity of the TLSs. For pathologists with rich experiences, the maturity of TLSs can be evaluated according to HE slices, and the method has good sensitivity and specificity.

This invention relates to the field of intelligent identification technology for swine diseases, and discloses a method for identifying and making decisions about swine diseases by integrating body temperature and image features. The method separates the independent basis components of each disease in a mixed infection by performing non-negative matrix decomposition on the body temperature time spectrum. It then uses the Hungarian algorithm to establish a cross-time point correspondence between the basis components, and uses an optimal transmission algorithm to associate lesion instances with the body temperature basis components to generate a feature sequence of disease components. Finally, it uses a time-series perceptual graph neural network to perform independent inference on the symptom evolution time-series graph, outputting the disease type identification confidence and disease stage inference confidence for each pathogen. Based on a multi-pathogen joint decision rule base, it generates a coordinated treatment plan.

The invention relates to the technical field of artificial intelligence, in particular to a psychiatric patient emotional fluctuation intelligent monitoring system based on a machine learning algorithm, which takes an electronic medical record of a patient as an entrance, automatically extracts the shortest emotional staying duration corresponding to a disease type, and calls similar historical data to carry out differential fine tuning on a pre-trained network. Enabling each patient to obtain an exclusive monitoring model; and meanwhile, by taking an abnormal proportion as a unified scale, online credibility evaluation is performed on an identification result, and acquisition parameters, an updating period and a compression ratio are reversely adjusted, so that a personalized psychological patient emotion identification model is output by the same hardware platform. The intelligent monitoring system provided by the invention can adapt to different diseases, different disease courses and individual differences by combining medical record information and real-time data of a patient, so that the problem that an existing system cannot accurately recognize emotion fluctuation due to a fixed mode is effectively solved, and the accuracy and reliability of emotion monitoring are remarkably improved.

The invention discloses a longitudinal whole course virtual patient generation method and system, and the method circularly completes the whole course simulation through the steps of constructing a double-source knowledge base, vectorization processing and semantic matching retrieval, information fusion, interaction content generation and dynamic memory updating. According to the method, medical disease course knowledge and patient personality characteristics are decoupled, semantic retrieval and information fusion are completed based on dense vectors input by a user, current-round interaction content is generated, whole-disease-course interaction information is formed, recursive compression and state updating are achieved through a dynamic abstract memory algorithm, disease course time sequence consistency is effectively guaranteed, and user experience is improved. Medical standardization and case diversity are considered, long-range interaction computing power consumption is reduced, and virtual patient simulation authenticity is improved.

The invention discloses a visible light image three-channel identification model-based crop incubation period symptom discrimination method, and aims to provide a method capable of determining and identifying a color channel of crop incubation period symptom features and dividing an incubation period disease course. The method comprises the following steps: extracting color moments of each order of a scab image through continuously shot healthy sample and inoculated sample RGB images; by analyzing the change rate of color moments, taking a color channel which can best reflect the change condition of scab pixels as an optimal identification channel, dividing the course of disease, and combining color space three-channel information to construct a three-channel identification model for identifying the symptoms in the incubation period; and performing fitting comparison on the field crop color moment absolute change rate curve graph and the sample color moment absolute change rate curve graph, and judging the disease course of the field crops.

This application relates to a method, device, equipment, and medium for dynamic prediction of Alzheimer's disease course. The method includes: acquiring multidimensional detection data of a patient; determining the patient's disease subtype and disease stage based on the multidimensional detection data and clinical diagnostic criteria; performing time-series analysis on the multidimensional detection data according to the disease subtype and the disease stage to obtain the patient's disease progression pattern; predicting the patient's future disease trajectory using a preset disease trajectory prediction model based on the disease progression pattern to obtain the patient's Alzheimer's disease course prediction result; and generating a recommended intervention plan for the patient based on the Alzheimer's disease course prediction result and preset clinical intervention rules. This method enables dynamic prediction of the Alzheimer's disease course and provides personalized intervention suggestions for patients, improving clinical treatment outcomes.

The invention discloses application of a reagent for detecting intestinal flora in preparation of a diffuse large B-cell lymphoma diagnosis or staging kit, and the reagent for detecting the intestinal flora is used for detecting the species abundance of the intestinal flora. The intestinal flora comprises short-chain fatty acid producing bacteria, enterococcus strains, helicobacter strains, saccharomycetes strains and pathogenic fungi. It is found that intestinal flora of a DLBCL patient shows significant dynamic evolution from a healthy period to an advanced disease period, and the core feature of the intestinal flora is subversive reconstruction of an interaction network of short-chain fatty acid producing bacteria and fungi. Therefore, by detecting the species abundance of the intestinal flora and analyzing the interaction strength of each community in the intestinal flora, the disease course of the DLBCL of the patient can be quickly and effectively evaluated by stages; in addition, 13 key strain variables screened from the intestinal flora have excellent DLBCL diagnosis performance, and a rapid and effective new way is provided for DLBCL diagnosis or staging.