10 results about "Patients symptoms" patented technology

The vehicle first-aid medical method for guiding first-aid patient classification, treatment and hospital transfer according to the present application includes the following steps: a patient information registration unit receives necessary information about a patient in advance before getting on a vehicle or in the vehicle after getting on the vehicle; a patient symptom input unit receives the current symptoms of the patient in multiple ways; a first-aid treatment guideline providing unit provides a first-aid treatment method suitable for the patient based on the collected information; and a remote medical support unit supports real-time communication with a remote medical staff in the vehicle, so that the patient or a first-aid team member can obtain direct advice and guidance from a first-aid medical specialist.

The invention discloses a traditional Chinese medicine disease diagnosis auxiliary system based on a clinical mapping knowledge domain, and relates to the technical field of traditional Chinese medicine diagnosis. Searching syndrome nodes associated with the symptoms and tongue picture features from a traditional Chinese medicine clinical knowledge map to form an initial syndrome set; calculating a matching degree between the pulse condition waveform data and typical pulse condition features of each syndrome node, and performing sorting and screening to form a focused syndrome set; extracting associated treatment methods and prescriptions to generate a preliminary diagnosis reasoning chain; and performing logic verification and optimization on the reasoning chain through a graph neural network syndrome evolution deduction model, outputting an optimized diagnosis path, and recording related data to a diagnosis and treatment file. The system can assist in traditional Chinese medicine diagnosis, improve diagnosis pertinence and reasoning preciseness, and perfect diagnosis and treatment records.

This invention discloses a treatment pathway recommendation method based on IBS multidimensional data. This invention relates to the field of intelligent diagnosis and treatment technology, and includes the following steps: S1, constructing an IBS treatment plan library. The treatment plan library contains standardized treatment plans corresponding to different diagnostic types and severity levels of IBS. The standardized treatment plan includes at least follow-up recommendations, medication recommendations, probiotic recommendations, and nursing guidance, and each treatment plan is associated with a corresponding IBS multidimensional data feature threshold. This treatment pathway recommendation method based on IBS multidimensional data overcomes the limitations of existing technologies with single-dimensional diagnostic data. It comprehensively integrates four core dimensions: patient symptom data, physical sign data, laboratory test data, and medical history data. Symptom data is obtained through a combination of patient self-reporting and doctor consultation to ensure no omissions; physical sign data relies on professional medical equipment to ensure objectivity; and laboratory test data is obtained synchronously from the hospital's testing system to reduce errors.

The invention relates to a multi-target parameter self-adaptive noninvasive tibial nerve stimulation method and system, and aims to solve the problem that an existing nerve regulation device cannot perform on-demand treatment according to dynamic change of symptoms of a patient. The induced foot sole neural signals are collected, and the F wave peak value amplitude and the incubation period meeting the stability condition are extracted in a preset time window to serve as real-time feedback signals. And establishing an individualized target interval based on the F-wave characteristic, calculating a dynamic deviation of a current F-wave peak value amplitude relative to the interval, and iteratively adjusting parameters such as stimulation intensity, frequency, pulse width and a double-target stimulation intensity proportion by taking a mean square error of the dynamic deviation as a loss function and adopting a gradient descent method until the F-wave characteristic is converged into the target interval. The dynamic change of the bladder function is self-adapted, and multi-target closed-loop self-adaptive stimulation of the tibial nerve is realized.

The invention discloses a trauma triage method, device and system, and relates to the technical field of medical assistance, and the method comprises the steps: S1, constructing an initial triage model which comprises a feature extraction network, a multipath local polynomial attention network, a global attention network and a fusion prediction network; s2, acquiring a training data set; s3, performing training optimization on the initial triage model by using the training data set to obtain an optimized triage model; s4, acquiring trauma data to be triage; s5, using the optimized triage model to predict and analyze the trauma data to be triage, and outputting a triage result; according to the method, the trauma data are aggregated according to the similarity, local polynomial attention feature extraction capable of being adaptively updated is carried out, and meanwhile, a patient-symptom-diagnosis heterogeneous graph is constructed to carry out polynomial attention learning on global features, so that the relevance between the trauma data can be learned more accurately while the calculation complexity is reduced, and the accuracy of the trauma data is improved. And a triage prediction result is given.

This invention relates to the field of medical diagnostic model technology, specifically disclosing a medical intelligent diagnostic large language model algorithm that integrates multimodal data. The algorithm includes: training a large language model based on case-based intensive retrieval; symptom abstraction: extracting key initial symptoms S from the natural language description input by the patient; candidate disease recall: retrieving the most relevant candidate diseases from the extracted initial symptoms S using the intensive retrieval model; diagnostic chain decision-making: analyzing the degree of matching between each possible disease and the patient's symptoms, generating a confidence value for each candidate disease, and quantifying diagnostic uncertainty using entropy; outputting diagnostic results, enhancing interpretability by dividing the diagnostic process into five transparent steps, providing an intuitive diagnostic chain, and improving the transparency and credibility of diagnostic decisions; high diagnostic accuracy by optimizing multi-round symptom inquiry strategies through quantifying the entropy value of the diagnostic chain, thereby improving diagnostic efficiency; and data privacy protection by constructing training data through synthesized patient cases, avoiding privacy and ethical issues, and achieving low-cost data generation.

The application discloses a syndrome diagnosis model training method, a syndrome prediction method and device. The syndrome diagnosis model training method comprises the following steps: unsupervised training of an initial model through a basic traditional Chinese medicine corpus to obtain a first diagnosis model; supervised training of the first diagnosis model through a general famous old Chinese medicine case database to obtain a second diagnosis model, wherein the general famous old Chinese medicine case database contains syndrome element information corresponding to patient symptoms; determining syndrome element pseudo-labels corresponding to a specific famous old Chinese medicine case database and correcting the syndrome element pseudo-labels to obtain target pseudo-labels; and optimizing training of the second diagnosis model through the specific famous old Chinese medicine case database and the corresponding target pseudo-labels to obtain a syndrome diagnosis model. The application solves the technical problems of limited accuracy and insufficient interpretability of an intelligent syndrome diagnosis model caused by the lack of syndrome element annotation in famous old Chinese medicine case data and the difficulty of annotation.

1. The name of the design product: pure tone audiometer. 2. The use of the design product: the product is suitable for the test of human ear threshold in clinical units, which can assist doctors in diagnosing patient symptoms. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

This invention belongs to the field of rehabilitation medicine technology, specifically disclosing an intelligent triage assessment system and method for F-S-D symptom clusters in breast cancer chemotherapy patients. The system includes: a multi-dimensional data acquisition module configured to acquire real-time sensory intensity data of the patient for three dimensions: fatigue, sleep disturbance, and depression, and map them into standardized symptom intensity vectors; a dynamic threshold determination module configured to determine whether to enable a fixed threshold or a dynamic threshold, and calculate a personalized dynamic threshold when enabling a dynamic threshold; a core symptom determination module configured to determine the patient's current core symptoms using a recursive decision tree; and a specialized assessment scale loading module configured to retrieve specialized assessment scales for core symptoms from the cloud for the patient to complete, and generate a structured report with severity gradients based on the completion results. This invention eliminates the need for full-scale quantitative assessment, making the testing process more targeted and reducing the workload of the assessment.

This invention discloses a closed-loop knowledge enhancement method, system, and storage medium for TCM syndrome differentiation reasoning, belonging to the fields of artificial intelligence and TCM intelligent diagnosis and treatment technology. The method includes: extracting entities and labeling attributes from patient symptom descriptions; generating main semantic vectors and attribute embedding vectors using a tongue and pulse-dominated encoding mechanism; performing path search with syndrome differentiation direction constraints in a knowledge graph based on the vectors to obtain candidate syndromes and evidence triples; constructing composite query vectors for supplementary book slice retrieval when knowledge is insufficient, based on knowledge sufficiency evaluation information output by a large language model; extracting candidate triples from supplementary answers and hit slices, writing them into a temporary knowledge graph after credibility verification, and merging them into the knowledge graph when conditions are met. This method can improve the accuracy of TCM syndrome differentiation reasoning and achieve closed-loop processing of syndrome differentiation reasoning, supplementary retrieval, and knowledge write-back.