302 results about "Diseases types" patented technology

The invention provides a medical decision-oriented multi-modal data dynamic fusion and labeling method and system, and relates to the technical field of medical decision-making, and the method comprises the steps: collecting medical images, electronic medical records and physiological monitoring data of a patient, extracting multi-modal features such as a focus form, a disease type, a symptom, a treatment record and a physiological index, and carrying out the fusion and labeling of the multi-modal features; and constructing a dynamic fusion labeling model. According to the model, multi-modal features are coded, similarity is calculated, a feature association graph is constructed, feature aggregation is carried out, labeling is carried out in combination with a medical knowledge base, and a dynamic labeling vector is generated. And inputting the dynamic labeling vector into a medical decision support model, and based on similar case analysis and deep reinforcement learning, generating a diagnosis and treatment suggestion containing a treatment scheme and early warning information. By dynamically fusing multi-modal data and an online updating mechanism, the accuracy and efficiency of medical decision making can be improved, and personalized diagnosis and treatment are realized.

The invention relates to a corn disease intelligent grading evaluation and renovation recommendation method fusing multi-modal information, and relates to a corn diversity disease identification and multi-stage evaluation model based on position coding. The method comprises the following steps: introducing a position attention mechanism, and carrying out multi-layer stacking by taking an inverse movement residual module integrated with position coding as an element movement block; the capabilities of complex image feature extraction and spatial information analysis are enhanced, and a scab region is segmented, so that disease type distinguishing and severity evaluation are realized; constructing a model based on a corn disease entity relationship triple library: performing deep context coding on a corn disease text, realizing classification and prediction of complex relationships among disease entities through a multi-layer structure, and analyzing complex links and influence factors among diseases; and a corn disease treatment method recommendation model combined with an implicit relationship: according to a disease identification and grading result and a disease triple, mining an optimal implicit relationship among different disease entities through complex number embedding and a graph neural network, and giving an accurate disease treatment method recommendation.

The invention discloses a tunnel lining disease automatic identification method and system based on multi-source data fusion, and relates to the technical field of facility detection, and the method comprises the steps: collecting multi-modal time sequence data, carrying out the time-space alignment, and obtaining a time sequence multi-source data set; reconstructing a tunnel center line based on a vehicle pose and constructing a lining structure consistency coordinate framework, and performing structured projection and distortion correction on alignment data to obtain a multi-modal fusion data set; dividing a two-dimensional structure grid under the coordinate framework, extracting and fusing geometric, texture, depth and energy features, calculating a structure consistency damage index, and extracting a suspected disease area; and calculating a disease credibility index and judging a disease type in combination with multi-modal physical evidence, mapping a suspected disease region back to a three-dimensional space, completing disease boundary extraction and geometric quantization, and outputting structured disease information. According to the method, the structure expression and the structured alignment of the cross-modal data under the unified geometric reference are realized by constructing the consistent coordinate framework of the lining structure.

The invention discloses a traditional Chinese medicine famous prescription and operation multi-agent collaborative consultation method and system. The system comprises a medical case knowledge base module, an expert knowledge base module, a medicine knowledge base module, a pre-differentiation module, a multi-agent diagnosis module and the like. The method comprises the following steps: constructing structured experience medical case data, an expert experience knowledge graph and a traditional Chinese medicine knowledge graph knowledge base, carrying out mixed retrieval to carry out pre-differentiation, calling multi-agent consultation according to preliminary disease differentiation and syndrome types, and obtaining an expert diagnosis scheme for risk verification and prescription adjustment optimization. According to the invention, a multi-agent collaborative diagnosis technology is utilized, and a multi-agent architecture for simulating real traditional Chinese medicine consultation is constructed. A plurality of expert agents perform independent diagnosis and complement each other, so that the limitation of a single agent is avoided, and comprehensive and accurate diagnosis is provided. And in combination with expert experience knowledge graph and big language model reasoning, the system can flexibly differentiate symptoms according to the symptoms of the patient, accurately judge disease types and syndrome types, and improve the differentiation accuracy.

The invention relates to the technical field of artificial intelligence and medical decision systems, and discloses a reasoning agent construction method and system for intelligent medical treatment guidance, and the method comprises the steps: constructing a hierarchical modal completion network and a modal correlation knowledge graph; constructing a medical feature cross-modal mapping network based on comparative learning, and mapping different modal medical data to a shared feature space; according to the uncertainty of the complemented data, constructing an uncertainty quantitative model and automatically adjusting a diagnosis confidence threshold; aiming at different disease types and symptom combinations, constructing a disease modal incidence matrix and a modal reliability evaluation network; through weighted voting, evidence convergence and specialist authority evaluation, cross validation and collaborative decision-making of multiple specialist knowledge are realized, and a final diagnosis suggestion is formed; according to the invention, the problem of limited diagnosis capability caused by lack of medical data in a medical resource limited environment is solved.

The invention relates to a tunnel three-dimensional disease intelligent identification system and method based on a large model, the system comprises a point cloud data acquisition module and a processor, and the processor comprises a data processing module, a three-dimensional tile optimization module and a disease identification module. The data processing module carries out standardization, noise reduction and registration processing on the received point cloud data; the three-dimensional tile optimization module constructs a multi-level tile pyramid structure based on the registered point cloud data, establishes a mapping relation between a space coordinate and a tile index, compresses tile data based on a curvature point cloud simplification algorithm and adjusts texture quality to form a three-dimensional tile image; calculating a comprehensive score of the tile quality to verify the quality of the three-dimensional tile image; a disease identification module extracts multi-modal fusion features of the preprocessed three-dimensional tile image and geometric features corresponding to disease types; fusing the multi-modal fusion feature and the geometric feature to obtain a joint fusion feature; performing field fine tuning on the joint fusion features; and obtaining a disease identification result.

The invention relates to the technical field of peanut disease intelligent monitoring scheme design, in particular to a peanut disease intelligent monitoring method and system and electronic equipment. RGB and near-infrared images are synchronously acquired through a dual-channel acquisition device, after illumination compensation, defogging and geometric correction, multi-scale features are extracted by using a transfer learning optimized Eff cientNet-B4 network, and spectral information is fused by using a dual-path CBAM attention mechanism to generate a disease sensitive feature vector. The cascade classifier realizes disease type identification and severity grading based on ResNet-34 and a random forest model, and predicts a disease development trend in combination with an LSTM time sequence model. And integrating a U-Net segmentation network to generate a visual report, and associating an expert knowledge base to output a prevention and treatment scheme. According to the method, the problems of low detection precision, poor model generalization ability and insufficient decision support of a traditional method are solved, and an efficient and accurate disease management tool is provided for peanut planting.

The invention relates to the field of subway tunnel lining disease detection technologies, in particular to a subway tunnel lining disease detection method and system, a terminal and a medium, and the method comprises the steps: obtaining a detection instruction; obtaining tunnel information based on the detection instruction; identifying disease information based on the tunnel information; acquiring the disease type based on the disease information; obtaining the number of the disease types based on the disease types, and calculating a difference value between the number of each disease type and a preset value; sorting the plurality of difference values; obtaining maintenance priorities of the plurality of disease types based on the sorting of the plurality of difference values; and obtaining a maintenance scheme of each disease type in sequence based on the maintenance priorities of the plurality of disease types. The detection efficiency can be improved.

The invention relates to the technical field of acupuncture point recommendation, in particular to a clinical acupuncture assistance method and system based on a traditional Chinese medicine knowledge graph, and the method comprises the steps: obtaining a disease type corresponding to a to-be-detected patient; screening out each matching scheme of the target disease type and an acupoint connected with each target scheme from the target knowledge graph; the target knowledge graph acquisition method comprises the following steps: determining a curative effect matching degree and a practical matching degree between each historical disease type and each reference scheme; screening out a matching scheme of each historical disease type; and connecting each historical disease type with the matching scheme thereof, and connecting the acupoints in the matching acupoint combination corresponding to each historical disease type with the historical acupuncture scheme to which the acupoints belong, so as to form a target knowledge graph. According to the invention, the appropriate acupoint combination is adaptively recommended for the disease type corresponding to the to-be-detected patient, so that the traditional Chinese medicine can be assisted to carry out final acupoint selection, and the reasonability of acupuncture acupoint selection is improved.

The invention relates to a bridge technical condition evaluation method and system. The method comprises the steps that bridge basic information input by a user is acquired; generating bridge structure information according to the bridge basic information; determining a disease type and a disease index of each part according to a bridge disease library; according to the disease type and the disease index of each part, calculating the score of each part deducted according to the disease; calculating the score of each part type in each structure according to the score of each part deducted according to the disease; redistributing the weight of each part type in each structure, and obtaining the technical condition score of each structure based on the score of each part type in each structure; performing weighted calculation on the technical condition score of each structure to obtain an overall technical condition score of the bridge; and determining a technical condition grade according to the overall technical condition score of the bridge. According to the invention, automatic evaluation of the bridge state is realized, and the efficiency and accuracy of bridge maintenance and management are greatly improved.

The invention discloses a chronic disease evaluation system and method based on a weak AI medical large model platform, and particularly relates to the technical field of medical artificial intelligence. The system constructs a multi-modal input matrix and generates fusion features by collecting multi-modal data such as electronic health records, wearable device time sequence signals and medical images; calculating a risk index by using cooperative work of a cloud large model and an edge end lightweight model and combining a current state and a historical trend; an early warning threshold value is dynamically adjusted according to the disease type and historical risk data, and risk early warning is achieved; key features are extracted, similar cases are matched, and an interpretability report is generated; and updating edge end model parameters through federal learning to protect data privacy. The problems of data splitting, static assessment, resource dependence and the like in a traditional chronic disease assessment system are solved, comprehensive, dynamic and accurate assessment of chronic diseases is achieved, and the method has high clinical application value.

The invention relates to the technical field of bridge disease diagnosis and maintenance, in particular to a bridge disease diagnosis and maintenance measure recommendation method which comprises the following steps: constructing a bridge disease database which comprises feature data and cause data of various types of bridge diseases and maintenance measure data matched with the various types of diseases; receiving field disease information of a target bridge input by a user, wherein the field disease information comprises a disease type and a disease characteristic parameter; performing matching analysis on the field disease information and data in the bridge disease database, and diagnosing a disease cause of the target bridge based on a matching result; according to the diagnosed disease causes, one or more maintenance measures corresponding to the disease causes are called from the bridge disease database and output as recommended schemes, and a complete data link from disease detection to maintenance decision is established by constructing the standardized bridge disease database and a matching mechanism.

The invention provides an artificial intelligence corpus construction method, device and equipment based on multi-modal data, and relates to the technical field of data processing. The specific implementation scheme is as follows: acquiring health data corresponding to a disease type; according to the health data, multi-modal features corresponding to the patient codes are generated; extracting medical entities and association relationships according to the multi-modal features, and generating knowledge entries; and constructing an artificial intelligence corpus according to the knowledge entries. According to the scheme, information of different data modes can be integrated, and limitation of single-mode data is avoided.

The invention provides a dynamic medication dosage optimization method fused with reinforcement learning. The method comprises the following steps: acquiring a first physiological index parameter of a target patient; obtaining a first diagnosis report of the target patient, wherein the first diagnosis report comprises basic information of the target patient and first disease information of the target patient; the first disease information comprises a first disease type and first disease description information; determining a first reinforcement learning algorithm corresponding to the first disease type; determining a first control parameter of the first reinforcement learning algorithm according to the first physiological index parameter and the basic information; and performing operation on the first disease description information through the first reinforcement learning algorithm and the first control parameter to obtain a first medication dosage parameter. Based on the application, the drug effect can be consistent with the physical condition of a patient, and poor drug effect and excessive side effects caused by too strong drug effect are avoided.

The invention discloses a case resource integration data system based on big data analysis, and belongs to the technical field of medical data. The method comprises the following steps: acquiring hospital case data and corresponding disease type data to construct a resource integration range, acquiring a personal case information set provided by medical consultation of a patient, performing sensitive data extraction on the personal case information set to obtain a dynamic case parameter set, and sending the dynamic case parameter set to a data risk analysis module; the multi-source data acquisition module processes the personal case information set as follows; according to the method, a data integration-risk analysis-clinical intervention closed-loop system is constructed, preorder data standardization integration guarantees analysis reliability, accurate risk analysis provides a direction for intervention, multi-level alarm and pre-plan matching is achieved through linkage of the preorder data standardization integration and the accurate risk analysis, prediction diagnosis reports and intervention suggestions are automatically generated, invalid operations are reduced, the clinical decision-making efficiency is improved, and the system is suitable for large-scale popularization and application. And meanwhile, through dynamic threshold updating and system self-iteration optimization, the adaptability and practicability of the system are continuously enhanced.

The invention relates to the technical field of medical data processing, in particular to an intelligent liver, gall, pancreatic and spleen disease nursing management system which comprises a parameter selection module, a feature extraction module, a risk assessment module, a node deduction module and a collaborative distribution module. According to the invention, through correlation analysis of disease types and physiological parameters, establishment of a monitoring parameter screening mechanism, avoidance of ineffective parameter interference, dynamic identification of key indexes, and quantitative evaluation of parameter deviation degree and organ function influence, the real-time evaluation capability of patient states is improved; risk level evolution trend prediction and intervention matching degree analysis are adopted, the timeliness and adaptation degree of nursing intervention measures are optimized, task pressure index calculation and role permission recognition are utilized, dynamic allocation of nursing resources among multiple roles is achieved, the response efficiency of a management system to an emergency state is enhanced, and the risk level evolution trend prediction and intervention matching degree analysis are combined. And the collaboration and the real-time performance of the nursing process are improved.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

The invention provides a tunnel disease monitoring data enhancement method and system based on CTGAN, and relates to the technical field of data enhancement, and the method comprises the steps: carrying out the disease type marking and disease feature preprocessing of a real data table of a tunnel site disease; extracting time sequence features and spatial position features from the preprocessed disease features; taking the time sequence features, the spatial position features and the disease types as conditional information to train a CTGAN, and using the CTGAN to generate synthetic data having similar distribution with the preprocessed real data table to obtain a new sample data table; after the new sample data table is generated, verifying a synthesis effect by adopting a mode of combining a statistical method and a support vector machine; according to the method, the confrontation network is generated by using the condition table, tunnel disease monitoring data is supplemented and optimized based on a real data table of tunnel field diseases, the problems of data scarcity and category imbalance are solved, and the training effect of a machine learning model is improved.

The invention relates to a lumbar vertebra protection method and system and a medium, and relates to the technical field of intelligent wearing. According to the scheme, personal basic information and past medical history of a patient are collected, the personal basic information comprises the age, the gender, the height, the weight and the BMI value of the patient, and the past medical history comprises past disease types and current disease courses; on the basis of the personal basic information and the past medical history, a lumbar vertebra supporting strength dynamic adjusting strategy is obtained, and the lumbar vertebra supporting strength dynamic adjusting strategy comprises a control strategy for supporting strength of the patient in all activity states, a control strategy for a supporting mode and recognized bad postures on the basis of the collected electromyographic signals of the patient; and controlling the waist support protection device based on the obtained dynamic adjustment strategy of the lumbar support strength, wherein the control comprises the control of the support strength, the control of the support mode and the early warning control of bad postures. Compared with the prior art, more targeted support strength and support mode control strategies for the patient can be obtained.

The invention relates to a medical diagnosis method and system based on multi-modal retrieval enhancement and guide guidance. The method comprises the steps that text information including reports and / or electronic health records and medical image information are obtained; encoding the medical image information and the text information by using an image encoder and a text encoder respectively to obtain visual features and text features; respectively utilizing a guide branch decoder and a label branch decoder, taking diagnosis guide features and disease type labels of samples in the training stage as queries of a Transform structure, taking splicing features obtained by splicing text features and visual features as keys and values, and decoding to obtain first prediction probability distribution and second prediction probability distribution of disease types, so as to obtain first prediction probability distribution and second prediction probability distribution of the disease types; and a final disease prediction result is obtained. According to the method, disease specificity knowledge is dynamically retrieved based on a multi-source medical knowledge base, redundancy and noise are removed through a large language model, a standardized and structured diagnosis guide is generated, and explicit guidance of the knowledge is achieved.

The invention discloses an electronic medical record analysis method and system based on natural language processing, and the method comprises the steps: obtaining an electronic medical record of a user, dividing the electronic medical record into a plurality of information blocks, and enabling each information block to correspond to a certain element in the electronic medical record, extracting an embedded vector representation of each information block through a natural language model; setting a text mapping model of the electronic medical record, and obtaining global representation of the electronic medical record according to the embedded vector representation of the information block; setting a time sequence dynamic acquisition model of the electronic medical record, and acquiring the embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; setting a disease and symptom prediction model, and predicting the disease type of the user according to the global representation of the electronic medical record and the embedded vector representation of each time point; and according to the prediction result, recommending a drug to the user, setting a curative effect evaluation model of the recommended drug, and evaluating the curative effect after the user takes the drug.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention discloses a brain disease risk prediction method and system based on big data analysis, and belongs to the technical field of brain disease risk prediction. The method comprises the following steps: carrying out standardized preprocessing and tagged classification on brain disease related big data to generate a feature data set; mining specific disease characteristics and risk factors in the set, and carding an association rule; training a risk prediction sub-model for each disease type based on the data, and building a multi-sub-model hierarchical prediction system; and collecting to-be-predicted object data, matching a disease type, and calling the corresponding sub-model to complete risk assessment. The system comprises multiple modules for collaborative operation, and a full-process closed loop of data storage, feature processing, model management and result output is realized. According to the scheme, the pertinence, the accuracy and the efficiency of risk prediction are improved, the traceability of the whole process and the dynamic optimization of the model are realized, and reliable technical support is provided for early screening and risk early warning of brain diseases.

The invention discloses a disease type prediction method and system based on a medical bill and a pseudo-label mechanism, and the method comprises the steps: obtaining a medical bill set which comprises a medicine list, a doctor-seeing department and patient portrait information, and carrying out the standardization preprocessing, and generating standardized bill data; respectively inputting the standardized bill data into a dynamic reasoning knowledge base and a pre-trained large language model, generating a first pseudo-label disease set through a rule reasoning engine, and generating a second pseudo-label disease set through the guidance of a semantic understanding cue word template; and fusing the two pseudo-label disease category sets to obtain a fused pseudo-label, training a multi-label disease category classification model by taking the fused pseudo-label as a target, and finally realizing disease category prediction of the to-be-predicted medical bill data. According to the invention, through a dual pseudo-tag generation mechanism fusing rule reasoning and a large language model, the problem that the medical bill data lacks a real disease category tag is solved, the accuracy and reliability of disease category prediction are improved, and a reliable solution is provided for deep utilization of the medical bill data.

The invention relates to the technical field of machine learning, and discloses a thyroid disease risk prediction method and system based on machine learning. The method comprises the steps of collecting medical record data of a thyroid disease patient, establishing a classification model based on the medical record data, and outputting a thyroid disease type corresponding to the medical record data; establishing a word vector model and a machine learning model, extracting a first feature quantity and a second feature quantity from the medical record data, screening out a first key feature and a second key feature of each thyroid disease from the first feature quantity and the second feature quantity, fusing the first key feature and the second key feature to obtain a comprehensive feature vector, and establishing a risk prediction model; acquiring medical record data of the current observation object, inputting the medical record data into the risk prediction model, and predicting and generating a thyroid disease risk level and a disease type of the current observation object based on the medical record data; if the risk level is a medium risk or a high risk, an optimal adjustment vector is generated, health adjustment suggestions are obtained based on the optimal adjustment vector, and the prediction accuracy of the thyroid disease risk is improved.

The invention relates to pattern recognition and anomaly detection in the technical field of computers, and discloses a laying hen disease early-stage AI early warning method and a laying hen disease early-stage AI early warning system. The method comprises the following steps: obtaining a laying hen behavior video stream, an excrement image and body temperature data, and carrying out preprocessing such as timestamp alignment and equipment number mapping to generate a training and reasoning input structure; then target tracking, key point coding, region segmentation, contour feature coding and body temperature fragment statistical coding are executed to extract a multi-source feature sequence; performing time window alignment, scale normalization, pattern recognition and anomaly detection processing on the multi-source features to realize disease type recognition; and finally, generating an early warning event based on rule triggering and threshold judgment, and forming closed-loop disposal through prevention and treatment scheme retrieval and instruction arrangement. According to the method, collaborative analysis and early abnormity early warning of multi-source heterogeneous data are realized, and the disease recognition accuracy and early warning timeliness are remarkably improved.

The invention discloses a multi-modal data processing method and device based on an attention mechanism, equipment and a medium. Living habits of a user and living and medical characteristics of genes are combined on the basis of image data and electronic medical records. The method is advantaged in that the problem of difficult fusion of heterogeneous multi-source data is solved through uniform characterization of features of four-modal data, adaptive adjustment of attention weight of each modal is carried out through the entity association relationship in the entity relationship knowledge graph, and fusion precision of full-modal fusion features is improved. Through the anti-illusion prediction model, disease type and probability prediction is carried out for individual differences (user tags and full-modal fusion features) of target users, and the accuracy of disease prediction results is improved. The multi-modal data processing method can be applied to user body evaluation in the financial field and disease type prediction in the medical field so as to improve the risk prediction accuracy in the financial field and the disease type prediction accuracy in the medical field.

The invention relates to a road disease duplicate removal method based on computer vision, which comprises the following steps: S1, acquiring an existing disease of which the disease type and position are the same as those of a to-be-detected road disease, recording the existing disease as a potential duplicate disease, if the existing disease does not exist, judging the existing disease as a new road disease, otherwise, continuing to judge through the following steps; s2, respectively acquiring definition parameters, contrast parameters, resolution parameters, angle parameters and confidence parameters of the to-be-detected diseases and the potential repeated diseases; s3, acquiring a comprehensive index P of the to-be-detected disease and the potential repeated disease; and S4, acquiring a comprehensive index difference P, if the comprehensive index difference of the two is within a threshold range, determining that the to-be-detected disease and the potential repeated disease are the same disease, otherwise, determining that the to-be-detected disease and the potential repeated disease are new road diseases. And performing similarity judgment on the graph by adopting a multi-parameter comprehensive index so as to eliminate repeated diseases and prevent repeated disease reminding.

The invention provides a disease consumable recommendation method and system based on multi-agent cooperation, electronic equipment and a medium, and relates to the technical field of medical consumable recommendation. According to the technology of the invention, the related data of disease consumables are obtained based on the data sensing agent, and feature extraction is carried out on the related data of the disease consumables; constructing a consumable knowledge base based on the consumable related data; based on the extracted features, matching the AI decision agent in a consumable knowledge base, and outputting a plurality of intelligent recommendation results; the multiple intelligent recommendation results are fused, a medical consumable recommendation scheme is generated, and the service execution agent executes related services based on the medical consumable recommendation scheme. The disease type consumable recommendation technology can realize full-process intelligent management from disease type identification to consumable recommendation, has strong learning ability, reasoning ability and decision-making ability, and can provide accurate and personalized consumable recommendation services for different disease types.

The invention provides a special disease first-aid full-process auxiliary decision-making method based on multi-modal perception and AI, and relates to the technical field of medical first aid, and the method comprises the steps: obtaining first-aid field multi-modal data, constructing a feature dependence graph, and recognizing the type and symptom features of a special disease; indexing special disease diagnosis and treatment data based on the symptom feature vector, and determining a reference case and a treatment path; the action space is constructed in combination with clinical specifications, the expected income is predicted by applying Monte Carlo search, and the optimal treatment scheme is generated, so that the first-aid special disease recognition accuracy can be improved, the treatment decision time can be shortened, and the first-aid intervention effect can be improved.