427 results about "Diseases/diagnoses" patented technology

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

The invention relates to the technical field of medical image processing, and discloses a method for intelligently describing liver space-occupying lesion ultrasonic image content by using LLM. A liver ultrasonic image sequence, a patient historical medical record text and a blood biochemical index vector are obtained through a multi-modal data acquisition module, and features are extracted through a cross-modal contrast learning network to generate embedded vectors and align the embedded vectors. And inputting the aligned image embedding vector into a dynamic context sensing decoder, and generating a description text semantic mark sequence by using a layered multi-head attention mechanism. The confidence coefficient is evaluated through an uncertainty calibration module, and the text is optimized through a post-processing reordering mechanism when the confidence coefficient is lower than a threshold value. A real-time interaction optimization mechanism is further arranged, and the model is updated according to feedback of doctors. According to the method, multi-modal data are fused, description accuracy and reliability are improved, text quality is optimized, clinical requirements are met, and liver disease diagnosis is assisted.

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

The invention discloses a multi-modal diagnosis and treatment scheme recommendation system based on reinforcement learning, and relates to the technical field of artificial intelligence, and the system comprises a data processing module which is used for obtaining multi-modal medical data of a patient and carrying out the preprocessing of the multi-modal medical data to obtain a multi-modal data set; the feature extraction and fusion module is used for performing feature extraction and fusion based on the multi-modal data set to obtain multi-modal features as the current state of the patient; the training set acquisition module is used for constructing a decision network and inputting the current state of the patient to obtain a current diagnosis and treatment scheme, taking the current state of the patient and the current diagnosis and treatment scheme as data groups, and circulating the process to obtain a plurality of data groups to form a training set; the network optimization module is used for optimizing the decision network based on the training set and the core objective function to obtain an optimized decision network; and the optimal scheme output module is used for obtaining the current state of the patient to be recommended and inputting the current state into the optimized decision network to obtain an optimal diagnosis and treatment scheme. And the disease diagnosis accuracy and comprehensiveness are improved.

The invention provides an integrated electrochemical biosensor capable of detecting double miRNA with high sensitivity, and the integrated electrochemical biosensor can simultaneously detect double miRNA markers with high sensitivity through self-assembly of a Y-shaped capture probe, preparation of an Au / TiO2-FTO array electrode, catalytic hairpin amplification (CHA) and construction of a micro-droplet chip electrochemical biosensor. The advantages of equalization of double-target miRNA capture probe sites, excellent electrode performance, reliable signal amplification, low sample consumption and the like are integrated, the linear range of miRNA21 and miRNA155 detection is 10 fM to 100 nM, and the detection limits are 5.00 fM and 5.17 fM respectively. By detecting the miRNA21 and miRNA155 double markers, the accuracy of breast cancer diagnosis is up to 98%, and the recognition sequences at the two ends of the Y stent probe are modified, so that the sensor can be used for diagnosing other miRNA marker related diseases. The integrated micro-droplet chip electrochemical biosensor constructed by the invention has a remarkable clinical diagnosis value and a prominent practical application prospect.

The invention discloses an AI-driven disease diagnosis and curative effect monitoring analysis system, which comprises a data processing module used for collecting multi-source diagnosis and treatment data and constructing a multi-modal sample set; the diagnosis prediction module is used for generating a diagnosis label, a diagnosis confidence value and a prediction curative effect trend through a multi-task learning model; the curative effect comparison module is used for collecting actual curative effect data, constructing a curative effect observation sequence and generating a curative effect deviation sequence based on the curative effect observation sequence and the predicted curative effect trend; the credibility evaluation module is used for executing credibility review based on the curative effect deviation sequence and the diagnosis confidence value; and the path evolution module is used for recording continuous multi-round diagnosis correction results, generating a jump type diagnosis path and updating a diagnosis and treatment data chain. The dynamic closed-loop verification mechanism between the multi-mode diagnosis and treatment data and the predicted curative effect trend improves the accuracy of disease diagnosis, the reliability of curative effect prediction and the adaptability of the diagnosis and treatment process.

The invention provides an AI-based medical record diagnosis and surgical operation code intelligent generation method and system, and relates to the technical field of medical information, and the method comprises the steps: obtaining multi-modal data from a hospital information system, including a text medical record, a medical image, a surgical video and a pathological report; entities and relationships are extracted through a multi-modal fusion model, and a hospital-level disease knowledge graph containing space-time dimensions is constructed; training a coding rule generation model and a verification model based on a reinforcement learning framework, and generating candidate codes and confidence scores thereof; and correcting the candidate codes according to manual feedback, and dynamically updating the knowledge graph and the rule base by using the graph neural network. Accurate disease diagnosis codes and surgical operation codes are generated in an intelligent mode, and the accuracy of the codes can be remarkably improved through automatic medical record code recommendation.

The invention relates to the technical field of disease diagnosis, in particular to a sacroiliac joint lesion intelligent identification method and system based on MRI, and the method comprises the steps: data preparation and enhancement, multi-expert collaborative labeling, mixed 3D-2D model processing, verification and optimization, and deployment and interaction. Compared with the prior art that a scheme for detecting the sacroiliac joint lesion by adopting a single 3D CNN or 2D CNN model has the problems of inaccurate positioning of an anatomical structure and loss of inter-layer feature association, resulting in insufficient sensitivity and specificity, the method provided by the invention has the advantages that through an improved hybrid 3D-2D model architecture, spatial features are extracted by utilizing the 3D CNN and an inter-layer dependency relationship is modeled in combination with Transform, so that the sensitivity and the specificity of the sacroiliac joint lesion detection are improved, and the detection accuracy of the sacroiliac joint lesion detection is improved. Meanwhile, spatial pyramid pooling and a channel attention module are adopted to enhance the multi-scale feature extraction capability, so that the method has the advantages that the lesion detection sensitivity and specificity are remarkably improved, the bone marrow edema region can be more accurately recognized, and the misdiagnosis rate is reduced.

The invention discloses a fruit and vegetable disease and insect pest spectrum database dynamic evolution updating and diagnosis optimization method based on model confidence feedback driving. Comprising the following steps: acquiring spectral data; based on the disease diagnosis model, outputting a corresponding model confidence coefficient; executing a calibration module, and outputting a calibrated model confidence coefficient; based on a preset confidence model, judging whether the model confidence is not less than a threshold value of high model confidence; based on the expert system module, judging whether the corresponding model confidence is not less than a threshold value of low model confidence; if so, executing an expert reexamination process; if not, inputting an abnormal sample library; and obtaining the number of newly added expert samples, a low confidence ratio and an interval to the last training time, executing an incremental training module, and outputting an instruction for updating and optimizing the disease and pest spectrum database and the abnormal sample library. And a data updating and model optimization mechanism for cooperative work of experts and models is established, so that misjudgment accumulation caused by excessive confidence of the models is effectively prevented, and the reliability of entering a database is ensured.

According to the invention, the multi-mode time-frequency domain conversion and the deep learning technology are combined, and a multi-component mixed Raman spectrum unmixing method is developed, so that clinical in-vivo and in-situ detection and disease diagnosis of novel Raman probes, instruments and the like are facilitated. The method comprises the following steps: (1) converting a mixed Raman spectrum from a time domain to a frequency domain by using fast Fourier transform (FFT), discrete cosine transform (DCT) and discrete sine transform (DST), and extracting frequency domain features; (2) extracting multi-scale local time-frequency domain characteristics of the mixed spectrum by using short-time Fourier transform (STFT) and discrete wavelet transform (DWT); (3) carrying out spectral unmixing calculation in each mode in combination with a one-dimensional attention mechanism U-shaped neural network model; and (4) fusing various modal unmixing results by using a meta-learning method, and analyzing the weight of each modal to obtain an accurate unmixing spectrum. Compared with a traditional Raman spectrum analysis method, the Raman spectrum multi-component signal unmixing method based on multi-modal time-frequency domain transformation and deep learning can accurately separate independent Raman signals of different tissue structures and biochemical components in a complex environment in a living body, so that the unmixing accuracy of the Raman spectrum multi-component signal is improved. Therefore, convenience is provided for subsequent disease mechanism analysis and diagnosis. The method provides an innovative and potential solution for in-vivo and in-situ detection analysis and disease diagnosis of medical clinical Raman spectroscopy.

The invention provides a temporal-mandibular joint disease intelligent diagnosis method and device based on multi-modal data, and the method comprises the following steps: obtaining multi-modal medical data, and extracting the vector representation of each piece of multi-modal medical data to form a knowledge vector library; extracting entities, entity relationships and events from the multi-modal medical data, and establishing a disease diagnosis knowledge graph based on the entities, the entity relationships and the events; and obtaining to-be-diagnosed data, and matching the to-be-diagnosed data with the disease diagnosis knowledge graph in the knowledge vector library to obtain an optimal diagnosis method. According to the scheme, the knowledge vector library and the disease diagnosis knowledge graph are constructed by acquiring the multi-modal medical data, feature extraction and analysis are performed on different modal data in multiple modes, the different modal data are integrated into the knowledge graph, and meanwhile, the optimal diagnosis result is acquired based on multi-mode matching weighted sorting of the to-be-diagnosed data, so that accurate and intelligent diagnosis is realized.

The invention relates to a big-small model collaborative diagnosis system based on federated learning and knowledge transfer. Comprising a data processing layer used for acquiring a to-be-diagnosed multi-modal medical examination data set and performing cross-modal feature processing to obtain cross-modal fusion features; the cross-modal fusion feature comprises a cross-modal alignment feature and text embedding; the large-small model collaborative architecture layer is used for performing disease preliminary screening according to the cross-modal fusion features, and calling a corresponding model for disease diagnosis according to a preset diagnosis strategy to obtain a diagnosis result; the encryption federal layer is used for encrypting bidirectional knowledge transmission between the edge end small model and the cloud end large model by using an encryption mechanism; and the bidirectional dynamic knowledge routing layer is used for bidirectionally optimizing the edge-end small model and the cloud-end large model based on a bidirectional dynamic routing mechanism in combination with the diagnosis result. By adopting the system, the diagnosis performance, the real-time response capability and the privacy protection level can be guaranteed, and meanwhile, the dynamic collaboration among model collaboration, knowledge evolution and data compliance is realized.

The invention discloses an interstitial lung disease diagnosis method and system based on artificial intelligence, and the method comprises the steps: carrying out the structural processing of clinical data of a patient, constructing a multi-dimensional hypergraph structure, inputting a variational graph auto-encoder model under a constraint condition, carrying out the coding, and introducing a pathological feature constraint to generate a hidden variable; extracting high-order features by using an artificial intelligence multi-dimensional hypergraph neural network, and inputting the high-order features into a diagnosis model based on a deep neural network for optimization training; and finally, processing to-be-detected data according to the same process, calculating a diagnosis probability value, and outputting a result in combination with a threshold value. The system comprises six units including a data structured processing unit, a coding feature extraction unit, a feature aggregation unit, a model training optimization unit, a diagnosis reasoning unit and a result generation unit which are mutually connected and cooperatively work. Through multi-dimensional hypergraph modeling and a pathological constraint mechanism, the problems of poor multi-modal data fusion and lack of pathological feature constraints of a traditional method are effectively solved, and efficient and accurate diagnosis of interstitial lung diseases is realized.

The invention belongs to the technical field of medical image processing, and discloses a medical image diagnosis system based on image and text feature fusion, and the system comprises an image processing module which is responsible for carrying out the enhancement, segmentation and feature extraction of a medical image; the text analysis module is responsible for extracting key clinical information from the electronic medical record; the semantic alignment and feature fusion module is used for embedding image features and text features into a unified vector space to realize fusion of multi-modal data; the model training and optimizing module is responsible for training and optimizing a medical diagnosis system model; and the clinical application module is responsible for applying the trained medical diagnosis system model to an actual medical environment to assist doctors in disease diagnosis. According to the medical image diagnosis system based on image and text feature fusion, by combining image enhancement, morphological processing, NLP analysis and multi-modal feature fusion, the illness state of a patient can be understood more comprehensively, the diagnosis accuracy is improved, and a more reliable auxiliary decision making basis is provided for doctors.

The invention relates to the cross technical field of artificial intelligence and medical image processing, in particular to a medical image segmentation system and method based on a wavelet bridge diffusion model and an efficient conditional random field. A WBDM-ECRF framework is constructed and comprises a discrete wavelet transform module, a BDM-T module, a BDM-S module and an ECRF module; decomposing the image through discrete wavelet transform, extracting a low-frequency sub-band, and enhancing the contrast ratio of a focus and normal tissues; the BDM-T takes U-Net as a backbone, integrates a FlashAttention mechanism, and optimizes a variance formula to realize efficient training; the BDM-S adopts a leapfrog sampling strategy, so that the reasoning time is greatly shortened; the ECRF introduces a multivariate potential function of a structural similarity index and smooth operation through edge expansion, and accurately optimizes edge segmentation. According to the method, the dependence of marked data is reduced, the training and reasoning efficiency is improved, the problem of fuzzy edge segmentation is solved, the Dice coefficient and intersection-union ratio performance on the ISIC data set is excellent, and reliable quantitative support is provided for disease diagnosis and treatment.

The invention discloses a mental disease medical auxiliary diagnosis method based on a bimodal knowledge graph and a related device, and relates to the technical field of mental disease diagnos.The method comprises the steps that a static medical diagnosis knowledge graph is constructed; generating a scenarized problem library based on the static medical diagnosis knowledge graph; dynamically updating a personal dynamic knowledge graph based on interaction between the scenarized question library and a user; and finally, optimizing the static medical diagnosis knowledge graph based on the personal dynamic knowledge graph. According to the invention, through cooperation of the static and dynamic knowledge maps, objectivity, dynamics and accuracy of mental disease diagnosis are improved, and closed-loop management of medical auxiliary diagnosis is realized.

The present invention relates to the technical field of dental medical treatment, and specifically discloses a method for using a CBCT (Cone Beam Computer Tomography) for automatically positioning a tooth, which comprises the following steps: S01, archiving CT data; S02, calculating AI and generating AI results; S03, entering CT reading by a client; S04, downloading and loading the CT data; S05, opening a function of a tooth lens; S06, selecting a corresponding tooth position in a tooth position list; and S07, selecting a 3D tooth rendering mode. The function of the ‘tooth lens’ is added into traditional CT reading, which is suitable for clinical disease diagnoses such as ‘tooth extraction, root canal therapy, tooth repair’ and the like in oral treatment, and doctors can precisely and quickly position the single tooth through selection for the tooth position, which facilitates more comprehensive analysis.

The invention discloses a chronic obstructive pulmonary disease patient screening system for the respiratory medicine department, particularly relates to the field of computer-aided diagnosis, and is used for solving the technical problems that an existing screening method is low in screening efficiency, low in patient adaptability and difficult to popularize on a large scale in a basic level. According to the system, a standardized patient medical event sequence is constructed by obtaining a target population medical record containing medicine distribution and disease diagnosis codes; identifying a characteristic medication and diagnosis mode before definite diagnosis of the chronic obstructive pulmonary disease based on a sequence pattern mining technology; constructing a medical knowledge graph by using the modes, obtaining a feature embedding vector representing a chronic obstructive pulmonary disease medical trajectory through graph neural network learning, and establishing a high-risk digital portrait; inputting the medical sequence of the person to be screened into a feature extraction model based on same map training, and generating an individualized risk probability prediction value by calculating the matching degree of the medical sequence and the digital portrait; and a screening result report is automatically generated, so that efficient and noninvasive early risk screening is realized.

The invention discloses a similarity measurement-based few-sample electrocardiosignal classification method. The method comprises the following steps of: acquiring an electrocardiosignal sequence from a public library and dividing the electrocardiosignal sequence into a support and query set according to a few-sample format; performing normalization processing and zero filling operation on the obtained sequence; inputting the sequence data with the uniform length into a parameter-shared one-dimensional convolutional neural network to extract a time sequence embedded feature vector; after the vectors are spliced and multiplied, weighting the vectors into weighted features through an attention network; inputting the weighted features into a multi-layer perceptron to calculate a similarity score, and training and fixing a neural network by using positive and negative sample pairs; and calculating the maximum similarity between the query sample and the support set, and outputting a prediction category. According to the method, the electrocardiosignal labeling cost can be remarkably reduced, the accuracy and efficiency of abnormal heart rhythm detection can be improved, dependence on large-scale labeling data is reduced, the practicability and expandability of electrocardiosignal classification are improved, and the method is applied to the field of medical signal processing and has important significance in the aspects of abnormal heart rhythm detection, disease diagnosis, wearable equipment application and the like.

The invention discloses a method and system for predicting membranous nephropathy based on machine learning, and relates to the technical field of medical diagnosis and treatment, and the method comprises the steps: collecting multi-source heterogeneous data of a patient; preprocessing the multi-source heterogeneous data, constructing a mapping relation between the traditional Chinese medicine syndrome type and the molecular biomarker, and generating a traditional Chinese and western medicine fusion feature matrix; screening a feature subset related to membranous nephropathy prediction from the traditional Chinese and western medicine fusion feature matrix; constructing a membranous nephropathy multi-level prediction framework according to the feature subset, and training each prediction model in the framework; and applying the trained and optimized membranous nephropathy multi-level prediction framework to the feature subset data of the new patient to generate a membranous nephropathy multi-dimensional prediction result. By establishing the Chinese and western medicine feature mapping relation, deep fusion of traditional Chinese medicine diagnosis and modern medical indexes is realized, and comprehensiveness and accuracy of disease diagnosis are improved.

The invention discloses a method for testing and evaluating safety effectiveness of a digital therapy product for mental diseases, which relates to the technical field of mental diseases and comprises the following steps: S1, evaluating objective indexes of the digital therapy product; s2, testing a man-machine interaction feedback mode; s3, mapping measurement of product output content and patient feedback; and S4, performing real-time adjustment and optimization. According to the method for testing and evaluating the safety effectiveness of the mental disease digital therapy product, aiming at the mental disease digital therapy product, a special testing and evaluating system is constructed, objective indexes and output contents of the digital therapy product are tested, and in the interaction process of a patient and the product, the safety effectiveness of the product is tested. By detecting data such as micro-expression, electroencephalogram signals and eye movement signals of a user in real time, feedback of a patient is obtained in real time, corresponding relation analysis is conducted on the feedback and output content of a digital therapy product, therefore, the treatment effect of the product can be accurately evaluated, and the accuracy of mental disease diagnosis and treatment is improved.

The invention discloses a disease diagnosis method and system based on multi-modal space-frequency domain adaptive fusion, and relates to the field of artificial intelligence and biomedical engineering.The method comprises the steps that multi-modal data are standardized, the unified and standardized multi-modal data are coded, and multi-modal initial feature representation is obtained; after projection and gating alignment and cross-modal interactive attention alignment are carried out on the initial feature representation of each modal, enhanced representations of each modal are obtained, and then the enhanced representations of each modal are fused into a shared feature representation; performing deep feature extraction on the enhanced representation of each mode to obtain deep features of each mode, and performing adaptive multi-domain feature enhancement processing to obtain multi-domain enhanced features of each mode; performing semantic alignment on the multi-domain enhanced features of each mode, and then performing fusion through a hierarchical attention mechanism to obtain fusion features; and the fusion features are input into a diagnosis network for prediction, a disease diagnosis result is obtained, and the intelligent diagnosis precision and robustness of papillary thyroid carcinoma are improved.

The invention discloses a portable detection system for early diagnosis of orthopaedic joint diseases, and relates to the technical field of orthopaedic joint disease diagnosis, which comprises the following steps: acquiring acoustic and motion feature vectors of joint activities, calculating a quality score based on an acoustic signal-to-noise ratio and a motion goodness of fit, performing weighted splicing on features by taking the quality score as a weight after normalization, generating a fusion feature vector; thirdly, reconstructing features by using a reverse decoding model, comparing the same degree with the original features, weighting to obtain fusion same degree, and iteratively adjusting the weight to optimize the fusion effect; and performing clustering analysis on the candidate feature vectors, replacing the candidate feature vectors if the candidate feature vectors deviate from a clustering center, and finally inputting a health model to output a diagnosis report. Through dynamic weighting of quality scores, high-fidelity features are processed preferentially, the information density and discrimination capability are improved, and low-quality data interference is reduced; and self-checking and dynamic correction are realized through reconstruction and similarity comparison, so that the fidelity and consistency of the characteristics are ensured, and the robustness of the system is enhanced.

The invention relates to the field of medical image analysis and neuropsychiatric disease diagnosis, and particularly discloses a brain network typing diagnosis system and method for attention deficit hyperactivity disorder. Comprising a data acquisition and preprocessing module, a topological feature extraction module, a supervised manifold learning module, a network reconstruction and diagnosis module, a parameter optimization module and a result verification module, continuous homology analysis is performed on a brain region function connection matrix based on an algebraic topology theory, and a brain region topological feature matrix is generated; utilizing supervised manifold learning to map the brain region topological feature matrix to a Riemannian manifold, and calculating a brain region importance weight map based on ADHD phenotypic features as supervised signals; according to the method, the brain network is subjected to subtype specific reconstruction through the curvature flow theory of Riemannian geometry, the topological difference between different subtypes is calculated, accurate typing diagnosis of ADHD is achieved, and the diagnosis accuracy is improved by 15%-20%.

In some embodiments provided herein is a generative foundation model trained over millions of health system-scale electronic health records along with web-scale medical text corpora to acquire knowledge of both medical practices and theories, and use of the generative model for rare disease diagnosis (including rare ophthalmic, diseases and rare systemic diseases), emergency condition identification (including ophthalmic emergencies and systemic emergencies), complex disease solving ("diagnostic puzzles"), or generating multimodal medical imaging reports (including ophthalmic images and radiology images such as X-rays and CT scans). In some embodiments, the generative model involves the use of language data, for pre-training, language data for supervised finetuning using a instruction tuning approach (e.g., QA pairs), and a human-machine hybrid evaluation strategy. In some embodiments, both the pre-training and supervised finetuning phases involve the use of a particular method of scaling to extend the context window. In some embodiments, the human-machine hybrid evaluation strategy involves language data for automated evaluations, as well as evaluations by generalists and by different specialists (e.g., ophthalmologists and radiologists) of varying levels of experience. In some embodiments, the generative foundation model, MetaGP, is used for unmet clinical needs through integration of medical and multimodal imaging data.

The present application relates to the technical field of medical magnetic resonance imaging, and discloses an intelligent dynamic contrast-enhanced magnetic resonance parametric imaging method and apparatus. In the method, a pixel-level vision transformer is integrated at the connection position of an encoder and a decoder of a generator to enhance the learning capability for non-local patterns and time series information in medical images, thereby improving the parameter estimation precision in noisy and variable environments. Gradient penalty is introduced into a loss function to optimize the performance of a discriminator, so that the discriminator can more effectively distinguish between real and generated images. A CycleGAN-like model can fully extract and use spatial features while performing time series analysis, thereby providing a more comprehensive and detailed PK parameter map, and providing powerful technical support for improving the accuracy of diagnosis of diseases such as cervical cancer.

The invention discloses a fusion method for balancing medical text positive and negative sample training, belongs to the field of medical text classification crossing natural language processing and medical informatics, and solves the problems of long text information loss, labeling noise interference and positive and negative sample imbalance in the prior art. The method comprises the steps of preprocessing a medical text, defining five types of medical entities such as disease diagnosis and the like, and labeling and establishing entity association; segmenting a long text by using a sliding window, extracting text features based on BioBERT, adding a gradient inversion layer, and setting a classifier and a discriminator; in positive and negative training, a weighted cross entropy loss function is used for learning real label mapping of a text in positive training, and a noise suppression loss function is used for reducing the influence of noise on model learning in negative training. According to the method, the delirium-symptom-containing medical record can be accurately identified, and the accuracy and robustness of medical text classification are improved.

The invention provides an auxiliary doctor seeing decision method and system based on multiple agents and a storage medium, and the method comprises the steps: carrying out the structural information collection of a target patient according to a pre-diagnosis agent, and obtaining the structural data of the patient; inputting the structured data of the patient into a diagnostic reasoning agent for diagnostic reasoning to obtain a diagnostic predicted disease; inputting the diagnosed and predicted disease and the current medical resource load information of the doctor-seeing hospital into a resource scheduling agent for diagnosis and treatment analysis to obtain a diagnosis and treatment recommendation scheme; inputting the diagnosis and treatment recommendation scheme into a medical insurance strategy agent for medical insurance analysis to obtain a medical insurance recommendation scheme; and combining the patient structured data, the diagnosis and prediction disease, the diagnosis and treatment recommendation scheme and the medical insurance recommendation scheme to obtain auxiliary treatment information. According to the embodiment of the invention, based on the auxiliary doctor-seeing information, the auxiliary doctor-seeing decision corresponding to the disease diagnosis, the diagnosis and treatment scheme and the medical insurance scheme can be effectively output, and the diversity of the auxiliary doctor-seeing decision function is guaranteed.

The invention discloses an automatic coding method and system for disease diagnosis, and belongs to the technical field of medical event identification, and the method comprises the steps: obtaining and carrying out the structured preprocessing of a disease diagnosis document, and displaying the directory and content of the document; utilizing the diagnosis coding model to extract diagnosis names, evidences and surgical operations from documents, and displaying the diagnosis names, the evidences and the surgical operations in multiple windows in a highlight mode; auditing, commenting, editing, merging and traceability positioning of diagnosis and evidence information are supported; automatically generating diagnosis coding results, sorting the diagnosis coding results, modifying main diagnosis and adjusting the sequence of associated evidences; the system supports DRG / DIP simulation in-group display and assists in coding sequence decision making; according to the method, the workload of a coder can be greatly reduced, the medical insurance settlement efficiency and the data quality are improved, and the method has good clinical application value and popularization prospects.

The invention provides an electrocardiosignal anomaly detection method fused with electrocardiosignal propagation simulation. The method comprises the steps that a space diagram structure is established based on ECG data; performing spatial position mapping on each electrode and establishing a heart simulation physiological model; feature extraction is carried out on each electrode node, and a dynamic space diagram structure is constructed; based on a dynamic graph structure, through multi-layer graph convolution updating and time aggregation, global space-time feature vectors are output, electrocardiosignal detection categories are output through a classifier, a space dynamic neural network model is constructed, a total loss function is established for model optimization, the electrocardiosignal detection categories are predicted, and quantitative recognition of abnormal propagation paths is achieved. And iteratively optimizing the dynamic graph structure. According to the method, unified modeling is carried out through time dynamics and spatial relevance of physiological signals, the defect that a static graph cannot capture abnormal changes of a propagation path in a pathological state is overcome, and the accuracy of cardiovascular disease diagnosis and the effectiveness of clinical decision support are remarkably improved.