117 results about "Medical science" patented technology

The application discloses a kind of Zhang Xizheng pure Chinese medicine theory prescription medicine digital inheritance and intelligent analysis system.The system includes disease and syndrome differentiation rule engine subsystem, prescription three-way search subsystem, drug knowledge enhancement subsystem, medical record intelligent search subsystem, Zhang Xizheng school review subsystem and large language model interaction subsystem.The method performs the following steps: LLM clinical information extraction and disease prediction (extract symptoms, tongue, pulse, and 8 big disease classification prediction), disease and syndrome differentiation KB rule matching (8 big disease classification x multiple syndrome type weighted scoring matching), multi-source search (prescription three-way search+drug knowledge double-way vector recall and prescription-drug dynamic association+medical record multidimensional mixed scoring search), LLM fusion generation (inject all search results Zhang Xizheng school special prompt word generation theory prescription medicine analysis report) and Zhang Xizheng school four-dimensional review (western medicine perspective+air theory+drug use principles+theory prescription medicine consistency).The present application first realizes the computer formalization expression of Zhang Xizheng "disease differentiation before syndrome differentiation" mode and "air monism" theory, based on "Medical Western Medicine Record" 172 prescriptions+213 drugs / medical theory / medical conversation+137 cases to build a complete knowledge search and intelligent analysis system, with the ability of western medicine perspective+air theory+drug use principles+theory prescription medicine consistency.

This disclosure relates to an evaluation method, system, and electronic device for a medical question-and-answer system. The method involves inputting patient information into the system to obtain a predicted diagnosis; inputting the actual diagnosis and the predicted diagnosis into a medical entity recognition model to obtain a first medical entity and a second medical entity; calculating the similarity of the feature vectors of these entities with standard entries in a standard medical terminology database to obtain a first standard entry most similar to the first medical entity and a second standard entry most similar to the second medical entity; matching the second standard entry with the first standard entry; and evaluating the performance of the medical question-and-answer system based on the matching results. Thus, when evaluating a medical question-and-answer system, even when the predicted diagnosis and the actual diagnosis are inconsistent or have logical inclusion relationships, the system can effectively resolve the complex logical connections between medical terms, significantly improving the accuracy of the evaluation.

The utility model provides a kind of adjustable spectrum shaking table belongs to medical diagnosis and detection technical field, the device includes tray, disposable test piece one, disposable test piece two, drive base and homogenizing plate, installation groove is opened in tray, several LED lamp beads are evenly distributed in installation groove, and clamping strip is symmetrically arranged and fixedly connected at installation groove slot mouth edge, drive base is set in tray lower end, and homogenizing plate is clamped on clamping strip, the device is according to the deficiency of existing disposable test piece, by setting LED lamp bead, homogenizing plate cooperate drive base, it can guarantee that the style of different viscosity has very good shaking effect, eliminate the intensity, angle difference of artificial shaking, improve result consistency, the device can be operated without professional training, can directly use marketable disposable test piece, support a variety of serological detection, equipment operation is simple, overall cost is lower, it is favorable to market promotion, with good market prospect before sale.

This application discloses a method, equipment, medium, and product for comprehensive evaluation of equalization of basic medical services, relating to the field of medical geography. The method includes: spatial processing of research data to construct a unified geospatial database; calculating multiple evaluation index values ​​representing the spatial allocation level of medical resources and the spatial convenience of residents' access to medical care through spatial analysis to form an original decision matrix; using the entropy weight method, calculating the objective weights of each evaluation index based on the degree of variation of the multiple evaluation index values ​​across different evaluation areas; processing the original decision matrix using the objective weights based on the TOPSIS model to calculate the comprehensive evaluation index of equalization of basic medical services for each evaluation area; and performing spatial hierarchical mapping based on the comprehensive evaluation index of equalization of basic medical services to generate a multidimensional diagnostic report, thereby avoiding evaluation distortion, accurately reflecting residents' comprehensive employment opportunities in accessing medical services, and enhancing public credibility.

The application discloses a medical data processing method and product based on multi-stage transfer learning and multi-modal data collaborative fusion, relates to the technical field of medical data processing and artificial intelligence, and adopts MedicalNet medical special pre-training weights to initialize a classification model backbone network; a three-stage progressive fine-tuning framework is constructed on the basis, field adaptive coarse classification fine-tuning and target task fine classification are performed, and clinical structured data is introduced in the third stage; after high-dimensional image features are reduced in dimension and low-dimensional clinical features are increased in dimension through an adaptive multi-branch multi-level MLP architecture, mid-term deep fusion is performed, the application can effectively mine the complex relationships such as complementation, correlation and cooperation of multi-modal heterogeneous data, improve the lung disease classification precision and model generalization capability, and significantly inhibit the small sample overfitting phenomenon.

This invention relates to a vulnerary multifunctional composition, based on unripe coffee cherries (peel, mucilage, beans and silverskin), seed extract of Trigonella foenum-graecum, with one or more tissue-inducing molecules derived from the secondary metabolites of both plants. The unexpected and surprising ability of this new composition to solve the problems of chronic non-healing wounds addresses a long felt unmet need in the medical arts. Through a multifaceted approach, it combats microbial infections, reduces chronic inflammation, counters oxidative stress, enhances immune responses, promotes angiogenesis, regulates protease activity, stimulates collagen deposition, increases growth factor availability, facilitates cell migration, and prevents persistent infections. These novel integrated therapies represent a breakthrough in addressing macroscopic wound healing while targeting the underlying microscale mechanisms. This invention also relates to methods for preparing, using, and administering the multifunctional composition for timely healing in mammals with various skin lesions.

Techniques for de-noising label-differenced medical images includes operating a medical scanner to collect a control image of a region of interest in a subject. The medical scanner is also operated to collect a labeled image of the region of interest. Multiple patches within the control image are selected. Each patch has a similarity better than a threshold compared to a co-located patch in the labeled image. A control low-rank variability model is derived for the patches within the control image to produce a de-noised control image. A labeled low-rank variability model is derived for the patches within the labeled image. A de-noised labeled image in the region of interest is produced based on the labeled low-rank variability model. A difference image in the region of interest is output based on a difference between the de-noised labeled image and the de-noised control image in the region of interest.

Provided are devices, systems, and methods for treating or preventing heart failure or a symptom of heart failure through coordinated nerve activity interruption for one or more target nerves. Devices disclosed herein may comprise a vascular catheter comprising a telescopic needle assembly configured to puncture vascular tissue in contact with the catheter, wherein the needle assembly comprises one or more electrodes configured to deliver electrical energy to a tissue in contact with the one or more electrodes. Methods may include treating or preventing heart failure or a symptom of heart failure by inserting a catheter into a vascular tissue of the subject; guiding the catheter towards the greater splanchnic nerve; piercing the vascular tissue of the subject with a telescopic needle assembly extending outwards from the catheter towards the target nerve; and ablating the target nerve by delivering a stimulation energy to the target nerve with one or more electrodes.

This invention relates to the field of medical data-driven dose prediction technology, and discloses a method, system, device, and storage medium for individualized dose prediction of hypothyroidism based on medical data. The method includes: constructing a standardized feature vector based on the child's weight, age in days, corrected age in months, current L-T4 dose, TSH value, FT4 value, previous TSH value, TSH rate of change, feeding method, month of consultation, etiology of hypothyroidism, comorbidity status, previous dose adjustment magnitude, and age at which TSH first reached target levels; extracting TSH dynamic trajectory features from the child's TSH time-series data from previous follow-ups; obtaining a basic recommended dose using a gradient boosting decision tree model constructed with counterfactual filtering training data; and correcting the basic recommended dose to obtain an individualized recommended dose. This method improves the prediction accuracy of the gradient boosting decision tree model and allows the individualized recommended dose to simultaneously take into account multiple clinical confounding factors.

The utility model relates to medical equipment technical field discloses a traditional chinese medical science bone injury fomentation device, including the carrier plate and fixed block one and fixed block two, the right side rotation of carrier plate is connected with one of elastic band, the inside of fixed block one is equipped with the slow groove, the inside slide of slow groove is connected with two sliding blocks one, the similar side fixed connection of two sliding blocks one has spring no.

The application provides a health question and answer method of an AI voice assistant and a smart watch, and the method comprises the following steps: S1, accepting health description input of a user; S2, mapping the health description of the user to a medical concept library to generate a standardized symptom description; S3, inputting the standardized symptom description into a probability association layer, performing dynamic probability flow propagation calculation based on a pre-constructed medical knowledge graph to generate an associated symptom result and a probability of a corresponding disease hypothesis; and S4, combining user portrait data to correct the probability in a portrait adaptation layer. The application changes fuzzy description into a standardized symptom, combines medical knowledge graph reasoning disease probability and optimizes the result according to the user portrait, solves the problem of low question and answer accuracy caused by semantic ambiguity, improves the accuracy of personalized health suggestions, and avoids the disadvantages of traditional systems, such as the influence of forced accurate description on experience and the lack of help of generalized output answers.

The application relates to the field of medical software and discloses a risk modeling method based on deep uncertainty quantification and neural bias correction. In view of the cognitive uncertainty caused by the writing habits or fuzzy description of doctors in clinical medical records and the problem that the risk is underestimated due to keyword matching in the prior art, deep semantic features are extracted by a large language model, semantic cognitive uncertainty is quantified by means of Monte Carlo sampling, and potential negative description bias is automatically identified. Then, a neural style embedding vector is introduced, a residual network is combined, nonlinear risk compensation is performed on the uncertain features, and calibrated risk factors are output. The method also uses a variational autoencoder to realize privacy desensitization and feature dimension reduction, thereby realizing automatic positive compensation of risk while getting rid of the restriction of an artificial vocabulary, and significantly improving the insurance pricing accuracy and fairness under unstructured medical data.

Embodiments of the present specification relate to a method and device for generating a medical report based on evidence-based medicine, the method comprising: obtaining patient information and an original clinical question; inputting the patient information, the original clinical question and a first prompt word into a target model to generate a structured clinical question; the target model being a large language model in the medical field; retrieving a medical literature database according to the clinical question to obtain a plurality of medical literatures; inputting the target medical literature and a second prompt word into the target model to enable the target model to perform bias risk assessment on the target literature to generate a structured initial assessment result of the target literature; inputting the clinical question, each medical literature and its initial assessment result and a third prompt word into the target model to enable the target model to perform step-by-step reasoning based on a GRADE evaluation process in the third prompt word to generate a medical report; the medical report including a medical recommendation for the clinical question, a recommended strength of the medical recommendation and an evidence quality level.

This invention provides a sepsis consultation method and system based on multi-turn dialogue, belonging to the field of intelligent consultation equipment technology. This invention achieves effective integration of standardized medical knowledge and personalized clinical experience through the construction of a two-dimensional knowledge graph and cross-graph interactive retrieval, improving the accuracy and reliability of sepsis diagnosis and treatment recommendations. Through structured sepsis diagnosis and treatment chain constraints and a confidence-driven dynamic strategy, it alleviates the illusion problem of large language models in complex clinical reasoning, ensuring that the generated content conforms to clinical diagnosis and treatment logic norms. Through semantic anchor guidance, hierarchical activation, and knowledge feedback mechanisms, it achieves multi-granular semantic association from macro-clinical scenarios to micro-medical concepts, balancing retrieval accuracy and knowledge coverage. The confidence evaluation system comprehensively considers knowledge sufficiency, redundancy, and consistency, adaptively balancing knowledge utilization and information exploration, improving the reasoning efficiency and diagnosis and treatment quality of multi-turn dialogue.

The application relates to the technical field of computer-aided medical decision-making, and particularly discloses a medical information intelligent decision-making assisting method based on multi-modal data fusion. A clinical decision-making intelligent agent is constructed and pre-trained, and at least two self-supervised learning tasks are used to pre-train an encoder so that the encoder learns general feature representation of data. Subsequently, a reinforcement learning framework is introduced to train a decision-making network to simulate a real sequential diagnosis and treatment decision-making process, and structured knowledge of a medical knowledge graph is embedded in training as a constraint condition to ensure medical rationality and safety of decision-making, and the trained intelligent agent is used to process current patient data to generate diagnosis and treatment action decision-making and corresponding decision-making supporting information. Through organic cooperation of self-supervised learning, reinforcement learning and a knowledge graph, the application effectively solves technical obstacles such as great data heterogeneity, poor model interpretability and difficulty in integrating with clinical logic in clinical application, and improves practicability and reliability of an auxiliary decision-making system.

This invention discloses an automatic medical coding normalization method, system, and storage medium for multimodal medical data, belonging to the field of medical information technology. The method includes: collecting original textual terms and subject structured information of adverse events and / or concomitant medications, and performing text denoising, format standardization, and data deduplication; generating an initial candidate coding set based on a pre-trained medical language model; performing context-aware semantic disambiguation and rearranging of candidate codes in conjunction with subject structured information; retrieving historically confirmed coding cases from other research centers during the coding confirmation stage, outputting consistency prompts based on the differences between the current context and historical contexts, and receiving reconfirmation, coding modification, or review submission operations; and storing the final confirmation results and retrieving them to an electronic data acquisition system. This invention can improve the accuracy, processing efficiency, traceability, and multi-center coding consistency of medical coding.

1. The name of the design product: the main body of an ultrasonic probe. 2. The use of the design product: the whole product is used for medical diagnosis and treatment, and the image and related information of the internal tissue and organs of the human body are obtained by emitting and receiving ultrasonic waves, and the local part of the product is used for the main body of the ultrasonic probe. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: design 1 perspective view 1. 5. Design 1 is designated as the basic design. 6. Other circumstances that need to be explained: the present design requests partial design protection, wherein the solid line part is the content requested for protection, and the dashed line part is not requested for protection.

The system according to this embodiment aims to facilitate communication between patients and medical professionals and enable the exchange of accurate medical information. [Solution] The system according to the embodiment comprises a translation unit, a specialized terminology translation unit, a real-time translation unit, a multilingual support unit, and a drug information analysis unit. The translation unit performs translations to facilitate communication between patients and medical professionals. The specialized terminology translation unit accurately translates medical terminology translated by the translation unit. The real-time translation unit provides the content translated by the specialized terminology translation unit in real time. The multilingual support unit provides the content provided by the real-time translation unit in multiple languages. The drug information analysis unit analyzes drug interactions based on the content provided by the multilingual support unit.

A knowledge-guided fine-tuning and optimization method for a large medical model, and a related apparatus. A method for enhancing the explainability of a large medical model on the basis of "multi-modal data plus expert knowledge" is constructed, with a focus on improving the diagnostic accuracy and explainability of models in the field of diseases. The method comprises: acquiring multi-modal data to be subjected to diagnosis; and inputting said multi-modal data into a knowledge-guided model to obtain a thought-guiding information part in a logic-chain prompt, so as to input the thought-guiding information part into a large medical model to obtain an output that has been normalized by means of chain-of-thought prompting.

The application discloses a medical question and answer retrieval method based on entity inverted index and semantic vector fusion, and comprises the following steps: step 1, medical knowledge base preprocessing is carried out to obtain a standardized entity set; step 2, symbol retrieval is carried out based on entity coincidence degree to obtain the ranking position in the symbol dimension; step 3, semantic retrieval is carried out based on vector embedding to obtain the ranking position in the semantic dimension; step 4, the results of the symbol retrieval and the semantic retrieval are integrated by multi-path ranking based on the RRF algorithm; and step 5, a large model enhanced medical question and answer is set. The application improves the accuracy of biomedical knowledge answering.

This invention discloses a method, device, electronic device, and storage medium for generating medical science popularization content. The method includes: parsing medical research content to be popularized to obtain structured content data; identifying medical terms in the structured content data to obtain target category labels for multiple medical terms; acquiring hot topic public opinion data and style content based on the target category labels; and inputting the structured content data, hot topic public opinion data, and style content into a pre-trained science popularization content generation model to obtain medical science popularization content. This invention can guide the science popularization content generation model based on hot topic public opinion and the desired style, prompting it to generate medical science popularization content with a hot topic public opinion orientation and a specified style based on the structured content data. The generated medical science popularization content is more accessible, approachable, and humanized, improving the reading experience and better catering to readers' interests, thus enhancing the efficiency and quality of medical science popularization.

This invention discloses a method and system for re-optimizing radiotherapy plans based on target inheritance and photon flow reset, relating to the fields of medical physics and radiotherapy technology. The method includes obtaining the initial radiotherapy plan for a patient from a treatment planning system; extracting a patient-specific target set from the initial radiotherapy plan to form a configuration file independent of the initial radiotherapy plan; and prohibiting modification of the configuration file in subsequent optimizations to achieve target freezing, resulting in a frozen target set. This invention, through a strategy combining target inheritance, freezing, and photon flow reset, enables the optimizer to break free from the constraints of the initial plan's historical parameters and restart the global search from a blank state, under the same clinical intent. This fundamentally breaks the local optimum locking caused by path dependence in traditional inverse optimization, systematically and significantly reducing the dose to organs at risk while maintaining target dose compliance, thus achieving a deterministic improvement in plan quality.

This application relates to the field of educational informatization technology and discloses an artificial intelligence-based virtual simulation teaching system for medical physiology, comprising: a system for collecting, processing, and encrypting and storing multi-source teaching data, including job requirement data, student operation data, voice interaction data, and clinical case data; a virtual simulation teaching module for constructing virtual diagnosis and treatment scenarios and conducting simulation teaching; a behavior analysis and demand adaptation module for correlating and analyzing job requirement data and learning behavior data, and outputting a behavior analysis report; a moral education evaluation module for quantifying medical ethics elements into constraint indicators and generating a moral education evaluation report in conjunction with the behavior analysis report; and an industry-education collaboration module for establishing a data transmission channel, accessing external data to update the simulation scenario, and enabling two-way interaction of teaching and evaluation-related data. This application improves the adaptability and evaluation accuracy of medical virtual simulation teaching.

The invention provides an artificial intelligence-based system for generating personalized medical information through the integration of multi-modal data across pre-hospitalization, hospitalization, and post-hospitalization phases. The system employs encoder modules to process diverse input modalities, including audio recordings, video streams, biomedical images, text-based clinical records, and physiological signals. These encoded representations are integrated into a unified latent space using a large language model (LLM) trained on medical datasets comprising historical patient cases, anatomical knowledge, and treatment guidelines. The LLM enables cross-modal analysis to generate personalized outputs via decoder modules, which transform the latent space representation into actionable formats like text-based summaries, visualizations, audio explanations, and treatment suggestions. A key innovation is real-time intraoperative feedback via encoder-decoder modules detecting anatomical structures and deviations from standard protocols. The system also includes a federated learning module to aggregate model updates across medical centers while preserving patient privacy through deidentification protocols.

This invention relates to a method, system, and storage medium for clinical diagnostic medical visual question answering based on multi-margin collaborative debiasing. The method includes: Step 1: theoretically analyzing the formation mechanism of medical language bias in clinical diagnosis from the perspectives of modal gradient imbalance, feature fusion bias, and classifier weight direction bias, and providing experimental evidence; Step 2: theoretically proving that the margin mechanism can alleviate medical language bias in clinical diagnosis; Step 3: using UpDn as the baseline model, adding the MAM mechanism and DCL module; Step 4: training the model; adding MAM loss and DCL loss as the overall loss function to the cross-entropy loss to obtain the MMCD model for clinical diagnostic medical visual question answering; Step 5: testing the effect of the trained model on the validation set. The beneficial effects of this invention are: providing theoretical support for the margin mechanism to alleviate medical language bias in clinical diagnosis, enhancing the persuasiveness and credibility of the technology.