51 results about "Clinical case" patented technology

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The invention relates to the technical field of medical training equipment, in particular to a critical care ultrasonic image training system, which comprises a clinical data acquisition module for acquiring critical clinical case data, ultrasonic image materials and diagnosis and treatment process specifications; the training scene construction module is used for constructing a high-fidelity three-dimensional training scene according to severe clinical case data, ultrasonic image materials and diagnosis and treatment process specifications; the virtual ultrasonic practical operation module simulates the operation of an ultrasonic probe based on a high-fidelity three-dimensional training scene, and generates an ultrasonic image corresponding to an anatomical part in real time; the intelligent skill assessment center assesses operation normalization and diagnosis accuracy through practical operation data and real-time generation of an ultrasonic image corresponding to an anatomical site in combination with an improved ResNet-LSTM model, and an assessment result is obtained; and the personalized teaching unit generates a customized training scheme and knowledge strengthening content according to the evaluation result in combination with the learning track of the student. Therefore, the problems of insufficient dynamic scene simulation, poor authenticity and the like in the prior art are solved.

The invention discloses a traditional Chinese medicine clinical auxiliary decision making system and method based on a multi-mode large language model. According to the method, through a multi-modal information perception and fusion step, visual feature extraction is carried out on a tongue picture image, and the tongue picture image is fused with clinical text semantics; through a case matching step of retrieval enhancement, similar cases are retrieved from a clinical case knowledge base; a clinical auxiliary evaluation report including syndrome discrimination, diagnosis conclusion, traditional Chinese medicine prescription and reasoning process is generated through a context-aware diagnosis generation step; and the clinical rationality of an output result can be ensured by adopting field customization indexes through specialized evaluation steps. A corresponding system comprises a tongue diagnosis generator, a clinical case knowledge base, a diagnosis generator and other components, and end-to-end clinical decision support is achieved. According to the method, the problems of difficulty in integrating multi-modal information, lack of professional data sets and the like in traditional Chinese medicine diagnosis are effectively solved, and doctors are assisted to improve the accuracy and reliability of diagnosis results.

The invention relates to the technical field of intelligent examination systems, in particular to an intelligent test paper composition method, a medical examination system, equipment and a medium. The method comprises the following steps: firstly, receiving department examination demand parameters, retrieving a test question set meeting conditions from a clinical case test question bank to form a candidate test question pool, and then intelligently screening candidate test questions by utilizing a preset clinical diagnosis and treatment dynamic model to obtain a preliminary test question combination; analyzing high-frequency clinical examination point features in hospital historical examination data, and performing optimization adjustment in combination with knowledge point distribution in the preliminary test question combination; and by evaluating the clinical difficulty balance degree and the diagnosis and treatment knowledge point coverage rate in real time, the test paper composition parameters are dynamically adjusted until preset standards are met. By introducing the clinical diagnosis and treatment dynamic model and historical examination data analysis, intelligent construction of clinical relevance between test questions and dynamic optimization of knowledge point distribution are realized, so that the generated test paper more accurately evaluates the actual diagnosis and treatment ability of medical personnel.

The invention discloses a rehabilitation assistive device assessment method based on multi-agent and RAG technology. The method comprises the following steps: constructing a multi-agent system which comprises a rehabilitation physician agent, an auxiliary tool engineer agent, a nurse agent and a user interaction agent; building a dynamic knowledge base based on an RAG technology, wherein the dynamic knowledge base comprises an auxiliary tool product database, a rehabilitation medicine knowledge base, a clinical case base and an industrial standard base; interacting with the user, and dynamically adjusting the question mode, the language style and the question sequence according to the user type and the answer content; retrieving in a dynamic knowledge base according to the obtained information, and respectively outputting professional evaluation suggestions; and according to the collaborative decision model, integrating professional evaluation suggestions of the rehabilitation physician intelligent agent, the auxiliary tool engineer intelligent agent and the nursing personnel intelligent agent to generate a multi-dimensional auxiliary tool evaluation report and recommendation scheme. According to the method, the efficiency of the evaluation process of the assistive device is higher, and the evaluation result is more accurate and effective.

The application provides a rule cultivation quality full life cycle monitoring method and system, which comprises the following steps: in a training period, collecting operation logs of rule cultivation personnel in multiple clinical information systems, and reconstructing the operation logs into behavior trajectory sequences reflecting clinical core capabilities; evaluating the quality deviation degree of the rule cultivation personnel in the clinical case processing process based on the behavior trajectory sequences; obtaining the skill mastering vector of the rule cultivation personnel in each clinical core capability based on the quality deviation degrees of multiple clinical operations in a historical period and a preset capability dimension mapping rule; generating the capability evolution characteristics of the rule cultivation personnel in the training period according to the change law of the skill mastering vector over time; and generating a multi-dimensional evaluation result including skill maturity, risk deviation degree, diagnosis and treatment logic consistency and clinical efficiency factors based on the capability evolution characteristics and the quality deviation degree. The application improves the timeliness and accuracy of rule cultivation quality evaluation.

The invention relates to the technical field of medical care, and discloses a female cervical cancer prevention and detoxification system which comprises a body fluid monitoring module used for monitoring the circulation condition of body fluid in a female body, the body fluid monitoring module comprises an implantable micro sensor network, and the sensor network is distributed at key parts of the human body. The sensor is used for collecting flow velocity, flow direction and component change data of body fluid in real time; the toxin analysis module is used for analyzing types and contents of toxins in the body fluid, and the toxin analysis module comprises a high-precision spectrum analyzer, a chromatographic analyzer and a biochemical sensor array and is used for carrying out qualitative and quantitative analysis on the toxins in the body fluid. Body fluid circulation data are collected in real time through the body fluid monitoring module, the toxin analysis module is combined to carry out accurate qualitative and quantitative analysis on the types and content of toxins in body fluid, and then a deep learning algorithm based on artificial intelligence in the scheme generation module is utilized; and screening the most suitable detoxification scheme from a medical knowledge base and a clinical case database.

This application discloses a method and apparatus for constructing a Traditional Chinese Medicine (TCM) diagnostic and treatment knowledge model. The method includes: transforming a general-purpose basic language model into a model specifically for the TCM field to obtain a basic TCM model; controlling the obtained basic TCM model to learn diagnostic and treatment thinking within the TCM field to obtain a basic TCM diagnostic and treatment model; aiming to enhance the flexibility of logical reasoning in the obtained basic TCM diagnostic and treatment model, controlling the obtained basic TCM diagnostic and treatment model to learn the diagnostic reasoning paths of clinical cases in the TCM field to obtain a TCM reasoning diagnostic and treatment model; controlling the obtained TCM reasoning diagnostic and treatment model to learn the unique TCM knowledge and unique diagnostic and treatment thinking of a target renowned physician to obtain a unique TCM diagnostic and treatment model; and controlling the obtained unique TCM diagnostic and treatment model to favor the unique diagnostic and treatment thinking of the target renowned physician to obtain a TCM diagnostic and treatment knowledge model. This improves the diagnostic accuracy and clinical decision-making reliability of the model.

This application provides a method and apparatus for generating traditional Chinese medicine (TCM) prescriptions based on multi-task joint optimization. The method includes: constructing a training set based on historical clinical case data, the training set comprising multiple samples and sample weight labels, each sample containing clinical text features, TCM prescription labels, and prescription set labels; constructing an enhanced training set based on the training set; training a multi-task joint optimization prescription generation model based on the enhanced training set; predicting TCM prescription sequences using an autoregressive approach, with the TCM prescription labels as the supervision signal; a set prediction branch used to predict the presence or absence of each TCM herb in the global TCM dictionary based on the semantic representation of clinical text features, with the prescription set labels as the supervision signal, and introducing sample weight labels into its loss function to enhance attention to low-frequency TCM herbs; training the model by weighted fusion of the loss functions of the above two branches; and generating TCM prescriptions using the trained model.

The invention discloses an otology medicine knowledge base construction method and device and a storage medium. The method comprises the following steps: acquiring an otology knowledge data set, an otology clinical case data set and a plurality of ear CT images, identifying a plurality of first medical entity words in the otology knowledge data set and a plurality of second medical entity words in the otology clinical case data set, and extracting quantitative image features corresponding to a plurality of image sub-regions in the plurality of CT images; according to the multiple first medical entity words, the multiple second medical entity words and the quantitative image features corresponding to the multiple image sub-regions, a heterogeneous graph network is constructed, and the otology medicine knowledge base is generated based on the heterogeneous graph network, so that image data-clinical parameters-otology professional knowledge data can be effectively integrated; the otology professional knowledge is deeply associated with the image data and the clinical parameters, and unified structured data is generated, so that the otology medical knowledge base capable of integrating the otology image data and the clinical parameters is constructed.

This application provides a method, apparatus, device, and storage medium for self-correction of a medical visual language model based on a dynamic experience base (DEKB), relating to the field of medical visual language processing technology. The method includes: when the initial diagnostic result of the medical visual language model for a current clinical case does not match the fact label, constructing the current case as a structured experience unit and storing it in a dynamic experience knowledge base; upon receiving a query, retrieving historical experience cases related to the new query from the dynamic experience knowledge base; using the historical experience cases as contextual prompts to guide the medical visual language model in chain-like thinking, generating a corrected reasoning result. By constructing an endogenous dynamic experience knowledge base, designing a deep attribution analysis mechanism, and employing a dual-threshold retrieval algorithm, this application enables DEKB to transform the model's historical errors into structured knowledge that can be used for future reference, significantly improving the robustness and generalization ability of medical model image diagnosis reasoning.

The invention discloses an NK cell exosome, a preparation method thereof and application of the NK cell exosome in preparation of drugs for targeted treatment of rectal cancer through meridian points, and belongs to the field of biological medicine and traditional Chinese medicine. The invention provides a method for preparing a high-purity NK cell and a high-concentration and high-potency NK cell exosome. Experimental results show that the NK cell exosome obtained by the method provided by the invention can efficiently kill tumor cells and has dose dependence; animal experiments show that the NK cell exosome prepared by the invention can significantly inhibit tumor growth; clinical cases show that accurate targeted therapy can be realized through meridian point injection of the NK exosome, indexes of a rectal cancer patient are recovered to be normal, and clinical relief is realized. According to the invention, a modern exosome technology and a traditional Chinese medicine meridian point treatment method are combined for the first time, a cell derivative medicine for treating the advanced rectal cancer is provided, and a theoretical and practical basis is provided for clinical treatment of the advanced rectal cancer.

This invention provides a medical training system, comprising: a standardized feature library construction module, which breaks down target medical operations into multiple standard steps and defines a standard value range for each step; a competency standard library construction module, which defines multiple competency levels and associates them with corresponding target medical operations; a case provision module, which provides simulated clinical cases according to competency levels; an error recording module, which determines that an operation error is triggered when the monitored operation feature data deviates from the standard value range, and generates a personal error file; and a targeted training module, which, in response to a selection command, retrieves the training scenario of the operation error node and generates a specific training task for the operation error. Therefore, this invention establishes an objective, quantitative, and standardized operation evaluation system, transforming subjective operation evaluation into objective measurement, enabling graded judgment of operation compliance, reducing the subjectivity and variability of human evaluation, and improving the effectiveness of nurse training.

The application discloses a model demonstration teaching aid with an adjusting function for orthodontic and orthognathic head bones, which comprises a skull, the front surface of the skull is hingedly connected with a maxilla, the top end of the maxilla is abutted with a first demonstration mechanism, the bottom end of the maxilla is slidably connected with an upper dental alveolar bone, the inside of the skull is fixedly installed with a connecting mechanism, and the connecting mechanism comprises two left and right symmetrical mandibular branches. The demonstration teaching aid can dynamically demonstrate the process of orthodontic and orthognathic treatment, intuitively present the three-dimensional space changes of the jaw bones and the dentition, help students establish a three-dimensional anatomical cognition, deepen the understanding of the treatment mechanism, support flexible combination of different deformity states through modular design, adapt to various clinical case teaching needs, meanwhile, the demonstration teaching aid can customize the treatment scheme according to the individual conditions of patients, convert the abstract operation process into a concrete dynamic display, significantly improve the efficiency and accuracy of the doctor-patient communication, reduce the understanding deviation of the patients, and enhance the treatment compliance.

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 application discloses a test paper and a kit for rapidly detecting echinocytes, and the test paper is composed of a support plate, an anticoagulant sample pad, a connecting pad, a reaction color developing area and an absorption pad; wherein the anticoagulant sample pad and the absorption pad are respectively arranged on the opposite sides of the support plate, the anticoagulant sample pad is connected to one end of the reaction color developing area through the connecting pad, and the other end of the reaction color developing area is connected with the absorption pad. The application is mainly based on a colloidal gold detection method for identifying echinocytes by detecting double-stranded DNA combined on the surface of red blood cells, and the detection method using the test paper and the kit of the application can be realized without specific sites and equipment, is simple, fast and high in accuracy. The test paper and the kit of the application can be used for all clinical cases diagnosed by detecting echinocytes.

The invention provides a processing method and device for clinical cases, and the method comprises the steps: generating a unique hash value through a first fingerprint feature vector corresponding to a fingerprint of an uploaded user and a first identification code configured for a target clinical case when the target clinical case is uploaded, and then obtaining a determined first random sequence, and determining characters needing to be replaced in sensitive fields in the target clinical case according to the first random number sequence, selecting a first target character from a character candidate set configured for the characters needing to be replaced according to the first random number sequence, and replacing the characters needing to be replaced by using the first target character, by means of the method, encryption can be completed, sensitive information can be hidden, the integrity and readability of data are not damaged, and the value of the clinical case during secondary utilization is improved.

The invention discloses a medical examination report analysis system and method based on big data, and relates to the technical field of medical data processing, the system comprises a data acquisition module, a preprocessing module, a storage module, an index correlation analysis module, an abnormity early warning module, a result generation module and a user interaction module, and the method comprises corresponding data processing and analysis steps. Multi-source data such as inspection reports, historical diagnosis and treatment, clinical cases and the like are collected through a standardized interface, are subjected to cleaning, standardization and structural conversion, and are accessed by means of a distributed storage architecture; an Apriori association rule algorithm is adopted to mine index association, a random forest and a neural network model are combined to realize risk assessment, and a three-level grading early warning mechanism is matched to push accurate suggestions. According to the method, traditional manual interpretation is replaced, the analysis efficiency and accuracy are improved, the problems of insufficient associated information mining, non-uniform diagnosis standards and the like are solved, medical institution scenes at all levels are adapted, interactive optimization is supported, clinical precise diagnosis is assisted, the burden of doctors is relieved, and the method has important popularization value for intelligent application of medical data.

The application provides a lymphoma image data generation method based on conditional constraint and feature matching of a generative adversarial network, and is used for data enhancement of lymphoma images. The method first combines a label constraint, builds a full connection layer and four deconvolution layers for a generative model of WGAN-GP to generate images, then builds three convolution layers and a full connection layer for a discriminant model of WGAN-GP to identify whether the images are from the real or generated, and finally combines a feature matching method to establish a loss function, generates images with specified labels through the adversarial learning of the generative model and the discriminant model, so as to achieve the purpose of data enhancement. A large number of clinical cases are needed for the acquisition of lymphoma images, however, the method can solve the problem that workers are difficult to acquire specified type lymphoma image data in the prior art, further improves the work efficiency of workers, thereby reduces the working time of workers and improves the authenticity of the generated images.

The application discloses a rehabilitation auxiliary tool evaluation method based on multi-agent and RAG technology. The method comprises the following steps: constructing a multi-agent system, including a rehabilitation doctor agent, an auxiliary tool engineer agent, a nursing staff agent and a user interaction agent; building a dynamic knowledge base based on RAG technology, including an auxiliary tool product database, a rehabilitation medical knowledge base, a clinical case database and an industry standard database; interacting with the user, and dynamically adjusting the questioning method, language style and question order according to the user type and answer content; retrieving in the dynamic knowledge base according to the obtained information, and respectively outputting professional evaluation opinions; and integrating the professional evaluation opinions of the rehabilitation doctor agent, the auxiliary tool engineer agent and the nursing staff agent according to a collaborative decision-making model to generate a multi-dimensional auxiliary tool evaluation report and a recommended scheme. The above method can make the evaluation process of the auxiliary tool more efficient, and the evaluation result more accurate and effective.

The invention discloses a sewage infectious disease monitoring and early warning method and system, electronic equipment and a medium, and the method comprises the steps: obtaining first external monitoring data and first clinical case time series data corresponding to to-be-predicted sewage, and obtaining a target prediction model comprising a first target prediction branch and a second target prediction branch; decomposing the first clinical case time series data into a first low-frequency component and a first high-frequency component; inputting the first low-frequency component and the first external monitoring data into a first target prediction branch in a target prediction model to obtain a predicted first number of infected people; inputting the first high-frequency component and the first external monitoring data into a second target prediction branch in the target prediction model to obtain a predicted second number of infected people; determining a prediction curve slope based on the first infected person and the second infected person; and performing sewage infectious disease monitoring and early warning according to the first infected person number, the second infected person number and the prediction curve slope. The sewage infectious disease monitoring accuracy can be improved.

This invention provides a management system for non-invasive prenatal testing (NIPT), comprising the following modules: a case classification module for recording and classifying NIPT results; a follow-up form module for creating follow-up forms based on the results from the case classification module for tracking and monitoring; a data statistics module for automatically statistically analyzing the results from the follow-up form module and calculating NIPT performance indicators; a data center module for managing all NIPT results and improving the follow-up system; and a case learning module for acquiring NIPT result data from the follow-up form module and recording classic clinical cases to support case discussions and form a closed-loop testing system. This management system for NIPT, by constructing a closed-loop management system for the entire NIPT process, integrates multi-source data from testing and follow-up data to form a closed-loop management system for prenatal testing.

The invention provides a traditional Chinese medicine clinical curative effect data acquisition device and analysis method based on multi-channel detection, and relates to the field of medical instruments. The traditional Chinese medicine clinical curative effect data acquisition device based on multi-channel detection comprises a multi-channel synchronous acquisition module, a traditional Chinese medicine component extraction module, a data preprocessing module, a core analysis module, a terminal display module and a data storage module. According to the invention, the functions of multi-channel extraction and quantitative detection of effective components of traditional Chinese medicines are effectively integrated, standardized detection of process parameters and component contents in a pilot test stage is realized, the effects of detection precision and standardization similar to those of western medicine pilot tests are achieved, and a standardized traditional Chinese medicine clinical case data acquisition system is constructed; synchronous acquisition of case basic information, curative effect indexes and component metabolism data is realized, and the problem of data fragmentation is solved.

The application discloses a glioblastoma peritumoral tumor infiltration segmentation model, which comprises clinical case collection, image information collection, pathological information collection, image data preprocessing, image feature learning based on CPC for self-learning of a Gb peritumoral edema region, migration learning by using a 3DUX-Net segmentation model, construction of a peritumoral infiltration segmentation model, and segmentation model verification. The application learns the artificial ROI segmentation features, establishes the peritumoral infiltration segmentation model, verifies the effectiveness of the model through double-blind testing, and accurately segments the peritumoral tumor infiltration through the peritumoral infiltration segmentation model, thereby providing an important reference for clinical multidisciplinary diagnosis and treatment.

The application provides a three-dimensional reconstruction optical navigation method and device for an electronic choledochoscope, and the navigation method specifically comprises the following steps: S1, extracting MRCP images of a patient meeting a condition from a clinical case library for depth processing and data labeling; S2, importing the labeled data into a liver and gallbladder three-dimensional reconstruction system to reconstruct a liver, a bile duct tree and a stone; S3, taking a target stone bile duct as a terminal point to calculate a shortest and safest three-dimensional channel from a laparoscope bile duct incision or a needle insertion point on the skin to the bile duct; S4, inserting a marked choledochoscope into the bile duct; S5, entering a double image fusion navigation mode; S6, performing puncture until the tip of the choledochoscope accurately reaches a target bile duct stone position; and the like. The application can lock and mark an intrahepatic target bile duct and a stone position before surgery, and reconstruct a three-dimensional map, so that the choledochoscope can be guided by the three-dimensional map to quickly enter the target bile duct after entering the intrahepatic bile duct, and the surgery and stone removal efficiency are improved.

The invention discloses a cascaded side face key point positioning and mandibular malformation evaluation method and system. The method comprises the following steps: detecting nose, lip and jaw regions in a side face image by using a deep learning method; then, side face contour features are accurately extracted by adopting an adaptive skin color segmentation technology, and needed medical key points are positioned; and finally, constructing a multi-index combined threshold value discrimination model to carry out mandibular malformation evaluation. Compared with a pure deep learning method, the positioning precision of the medical key points of the side face is higher, and the evaluation requirement of mandibular deformity can be met; the cascade architecture is low in computing power requirement and supports immediate evaluation of outpatient service; according to the mandibular malformation assessment method based on the double-triangle geometric model, data support is provided for hundreds of clinical cases, and the judgment threshold value is more robust; the method can be flexibly applied to the fields of oral cavity and mandibular surface malformation analysis, disease diagnosis, surgical planning and the like.

The application discloses a construction method of a mouse model with increased transcription factor FOXG1 copy number and application thereof, belongs to the field of animal models, and is characterized in that FOXG1 syndrome is caused by mutation of a risk gene FOXG1, and the increased copy number makes FOXG1 expression up-regulated as one of the mutation forms. FOXG1 up-regulation is also found in some autism patients. The construction method of the mouse model with increased FOXG1 copy number comprises plasmid construction and microinjection of CAG-loxp-stop-loxp-Foxg1-IRES-EGFP, F0 generation Founder mouse identification, and F1 generation mouse stable genetic genotype identification. The application over-expresses a mouse Foxg1 gene, simulates the increase of the FOXG1 syndrome clinical case copy number and the up-regulation of FOXG1 in autism patients. The application can provide a model for autism research and be used for developing precise diagnosis and intervention strategies for the FOXG1 syndrome.

The invention discloses a traditional Chinese medicine auxiliary diagnosis and treatment model correction method based on dynamic confidence coefficient calibration, and the method comprises the following steps: constructing a traditional Chinese medicine auxiliary diagnosis and treatment model, outputting a corresponding syndrome type according to the symptom of a patient, carrying out the frequency statistics of the syndrome types of a famous medical experience knowledge base, taking the syndrome type appearing at a high frequency as a common syndrome, and carrying out the correction of the traditional Chinese medicine auxiliary diagnosis and treatment model. The syndrome type of low frequency is rare; confidence coefficient smoothing is applied to the diagnosis result of the model predicated as the common certificate, and excessive confidence of the common certificate is inhibited; the calibration intensity of the dialectical confidence coefficient calibration module is dynamically adjusted according to an iteration process of model training; a calibrated model is utilized to generate a pseudo tag on a real-time clinical case library without a tag, the pseudo tag is added into a training set, the traditional Chinese medicine auxiliary diagnosis and treatment model is iteratively optimized, and the diagnosis accuracy of the model in a complex clinical situation is improved by inhibiting excessive confidence of common syndromes and dynamically adjusting the correction strength according to the model state. A large amount of unlabeled clinical data can be effectively utilized, and dependence on expensive labeled data is reduced.