302 results about "Data treatment" patented technology

The invention discloses a supply chain risk identification method and system based on a knowledge graph, belongs to the technical field of supply chain management and artificial intelligence crossing, and aims to solve the technical problem of how to realize dynamic monitoring, accurate identification and active early warning of supply chain risks, improve full star, real-time performance and interpretability of supply chain risk identification, and improve the risk identification efficiency. According to the technical scheme, the method comprises the following steps: collecting and treating multi-source data: collecting static background information and dynamic risk information of a supplier, and carrying out highly intelligent data treatment on the collected static background information and dynamic risk information of the supplier through a data treatment engine to ensure data quality and consistency; constructing a dynamic knowledge graph; intelligent risk identification: based on a graph topological structure and dynamic attributes, identifying key risk nodes and communities, tracing in time, marking risks, and performing early warning; decision support and visualization are carried out; and dynamically optimizing and feeding back.

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

The invention relates to the technical field of water quality data analysis, and discloses a river water quality pollution assessment method and system based on big data analysis, and the method comprises the steps: building a water quality parameter set; obtaining a preprocessed water quality data set; obtaining a global statistical analysis result through descriptive statistical analysis; carrying out hierarchical multi-time evaluation analysis to obtain a hierarchical evaluation result; obtaining a deviation value between the current water quality data and standard water quality data of a laboratory; reliable water quality characteristic data are obtained; a difference trend analysis result is obtained; performing relevance verification on the river water quality distribution map and the time change map of the target area according to the difference trend analysis result to form a comprehensive analysis result; and finally generating a comprehensive water quality pollution assessment report containing water quality traceability information and a comprehensive analysis result. According to the invention, a multi-parameter sensing technology and intelligent data processing are organically combined, so that the water quality of the river is efficiently, accurately and carefully analyzed, and the pollution degree is evaluated.

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 discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.

The invention discloses an intelligent coagulant adding method for a waterworks. The method comprises the following steps: acquiring test data and production statistical data of the waterworks in a historical time period; performing data processing on the test data and the production statistical data to obtain a target data set for training; training the initial addition model by using the target data set to obtain a target addition model; obtaining current monitoring data of the water quality of the water plant, and inputting the current monitoring data into the target adding model to obtain a predicted adding amount of the coagulant; on the basis of the factory water turbidity monitored in real time, the feedback regulating quantity of the predicted adding quantity is determined; and performing feedback adjustment on the predicted dosage based on the feedback adjustment quantity to obtain a target dosage corresponding to the current monitoring data, and adding the target dosage to the waterworks. The condition that the outgoing water quality does not reach the standard when the incoming water quality abnormally fluctuates is avoided, and stable water quality control of a water plant is ensured.

The invention relates to the technical field of data processing, and discloses a temperature effect modeling method and system for a concrete tower drum. The method comprises the steps of establishing a finite element model considering joint grading thermal resistance and prestress correction material parameters, adaptively adjusting a time step length according to a solar altitude change rate to calculate a radiation load, obtaining a convergence temperature field through bidirectional coupling iteration of a temperature field and a stress field, and extracting characteristic temperature to train a neural network to realize rapid prediction. According to the invention, the precision and efficiency of temperature effect analysis of the fabricated concrete tower drum are improved.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention discloses a traditional Chinese medicine intelligent diagnosis and treatment system and method. The diagnosis and treatment system comprises a medical thinking layer used for clinical information reasoning decision; the expert mixed framework layer is used for refined data processing, analysis and reasoning; the clinical tool layer is used for collecting and analyzing clinical information; the interaction layer is used for providing digital human, voice, image-text and other interaction modes for patients and doctors, and the medical thinking layer and the expert mixed framework layer are in communication interaction with the interaction layer and the clinical tool layer. The diagnosis and treatment system has the advantages that expert models in different fields are combined through the expert mixed framework layer to achieve refined and intelligent support and optimization of the traditional Chinese medicine diagnosis and treatment process, the traditional Chinese medicine clinical real diagnosis and treatment process is simulated, the medical thinking layer is applied to conduct clinical information reasoning and decision making, and the diagnosis and treatment efficiency is improved. And the reliability and interpretability of the traditional Chinese medicine intelligent diagnosis and treatment paradigm are improved, so that a grassroots traditional Chinese medicine doctor can be assisted to improve the differentiation accuracy, and the popularity of traditional Chinese medicine diagnosis and treatment services is promoted.

The invention discloses a nursing health education method and system based on a rule engine, and a medium, belongs to the technical field of medical data processing and analysis, and is used for solving the technical problems that an existing health education scheme is not formulated as a personalized education scheme, and is lack of interactivity with a patient and long-term education effect evaluation and follow-up. The method comprises the steps of determining a health propaganda and education material set of each specialized disease under a corresponding nursing path based on medical health information; extracting a propaganda and education inference rule from the health propaganda and education material set, and building a propaganda and education rule engine according to the propaganda and education inference rule; obtaining full-time health data of a patient to be propagandized and educated, and segmenting the full-time health data through the sliding window to obtain target health data; performing propaganda and education reasoning rule matching on the target health data through a propaganda and education rule engine, and determining a health propaganda and education scheme of the patient to be propagandized and educated; and pushing the health propaganda and education scheme and the corresponding test scheme to the patient to be propagandized and educated.

The invention discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

The invention relates to the technical field of health data processing, in particular to a clinical patient self-health management recommendation method and system. Obtaining multi-dimensional health data of the target patient, including a clinical measurement index set and a self-report symptom set, and performing standardized preprocessing on the multi-dimensional health data to generate standardized health data; constructing a personal health state map based on the standardized health data; performing health risk traceability analysis according to the personal health state map, and identifying a key risk node set and a risk propagation path; generating an initial health management recommendation strategy set in combination with a predefined medical knowledge base and patient personalized constraints; and dynamically optimizing the initial health management recommendation strategy set based on the patient feedback data and the health state change information, and outputting an optimized personalized health management recommendation scheme. According to the invention, personalized, precise and dynamic patient self-health management recommendation in a clinical scene can be realized.

The invention discloses a rectal cancer tumor T stage differentiation labeling method based on an enhanced CT image, and relates to the field of medical image data processing, and the method comprises the following steps: a standardized manual labeling module which is used for constructing three-dimensional pixel-level labeling data of a rectal cancer tumor T stage according to a medical image and a pathological stage standard; the data set construction module is used for uniformly storing and organizing the original CT image, the annotation mask file and the matched label description document to form a structured data set which can be used for modeling; according to the rectal cancer tumor T-stage differentiation labeling method based on the enhanced CT image, a high-quality and standardized three-dimensional T-stage labeling data set is constructed, a three-dimensional pixel-level stage labeling process for the rectal cancer enhanced CT image is provided based on the AJCC eighth version tumor stage standard, the labeling content covers tumor focuses and normal intestinal wall structures around the tumor focuses, and the three-dimensional T-stage labeling data set is established. A plurality of high-annuity imaging department doctors perform independent blind marking, expert re-checking, quality rating and the like.

The invention relates to the technical field of medical data processing, and discloses an operation period treatment risk intelligent prompting method, which comprises the following steps of: acquiring physiological index time sequence data and a treatment scheme execution parameter matrix of a perioperative period patient, generating a composite state tensor through multi-source feature correlation calculation, constructing a risk-intervention correlation nodal topological map, and calculating the risk-intervention correlation nodal topological map. Inputting a risk transfer model to simulate a risk transfer path and outputting an association influence probability value set, establishing a bidirectional association equation through space-time association mapping operation to generate a risk-intervention coupling prediction curve, calculating a risk prompt factor, and performing operation on the risk prompt factor and an association influence probability value to generate a standardized thermodynamic diagram; and extracting the operation quantitative index set, dynamically comparing the operation quantitative index set with a preset safety threshold value, and generating an intelligent prompt scheme containing a risk early warning level and an intervention scheme adjustment strategy when the index breaks through the threshold value. According to the method, the accuracy and effectiveness of risk prompting during the operation period are improved.

The invention relates to the technical field of medical data processing, in particular to an AI-based anorectal operation auxiliary system, which comprises a patient health data analysis module, a patient health data analysis module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the patient health data analysis module is used for classifying personal information of a patient and health data associated with anorectal diseases on the basis of basic physiological parameters of the patient, past anorectal disease history, medicine allergy history and operation records; and performing correlation analysis on the anorectal disease history and the physiological parameters of the patient to generate preoperative health feature information. According to the method, the physiological parameters of the patient and the past medical history are subjected to correlation analysis, key health indexes related to the operation can be accurately extracted, potential relations in the data can be mined, and the application effect of the data is optimized. By analyzing the combination mode of different health data, the probability of occurrence of common risks in the operation can be predicted, and the accuracy of medical decision making is enhanced. In addition, key operation steps of the surgery are matched with the health state of the patient, potential operation difficulties are recognized, and the scheme is optimized in a targeted mode.

The invention provides a coronary heart disease recurrence risk assessment method and device, equipment and a storage medium, and relates to the technical field of medical data processing. The method comprises the following steps: acquiring dynamic behavior data, physiological data and static risk indexes of a target patient; calculating a treatment compliance index and a rehabilitation health index of the target patient based on the dynamic behavior data and the physiological data of the target patient; and inputting the treatment compliance index, the rehabilitation health index and the static risk index into a trained coronary heart disease recurrence risk scoring model to obtain a coronary heart disease recurrence risk score of the target patient. According to the method, objective and quantitative recurrence risk scores can be obtained, more accurate decision support is provided for clinicians, and early warning and personalized intervention can be realized, so that the prognosis of patients is improved, and the medical cost is reduced.

The invention relates to the technical field of data processing, in particular to a multi-modal medical data intelligent association analysis system based on deep learning. The system comprises a data acquisition module used for acquiring a plurality of first data and a plurality of second data of a target patient, the first data being structured medical data, and the second data being unstructured medical data; the keyword extraction module is used for extracting a plurality of keywords of each piece of second data; the clustering module is used for clustering the multiple pieces of second data based on the multiple keywords of each piece of second data to obtain multiple class clusters; the time sequence analysis module is used for performing time sequence analysis on the treatment time period based on the plurality of class clusters so as to determine a plurality of diagnosis and treatment time slices; and the correlation analysis module is used for performing correlation analysis on the second data and the first data in each diagnosis and treatment time slice according to the plurality of class clusters so as to obtain a correlation analysis result. According to the invention, the association analysis effect of the multi-modal medical data can be improved.

The invention discloses a remote medical scheme decision-making method and system based on big data analysis, and relates to the technical field of data processing, and the method comprises the steps: obtaining a user monitoring portrait; establishing an illness condition tracing analysis channel; executing illness condition tracing credible optimization of the user according to the illness condition tracing analysis channel and the user monitoring portrait, determining a plurality of illness condition tracing credible factors, inputting the illness condition tracing credible factors into a remote medical scheme decision space, and obtaining a matched medical scheme domain; performing mixed reality intervention optimization on the matched medical scheme domain based on the user monitoring portrait to obtain a first optimized medical scheme domain; and performing multi-level risk optimization on the first optimization medical scheme domain according to the multi-level risk scoring model to obtain a target medical scheme. The technical problem that in the prior art, comprehensive analysis and accurate decision support for multi-source health monitoring data is lacked in remote medical scheme making is solved, and the technical effect of improving the individualized matching degree and decision accuracy of the remote medical scheme is achieved.

The invention relates to the technical field of image data processing, in particular to an oral cavity knowledge graph construction method and device, equipment and a storage medium. Preprocessing the oral CT original sectional image data based on a preset adaptive threshold method and a preset rigid registration algorithm to obtain standard image data; performing feature extraction on the standard image data based on a preset deep learning model and a preset principal component analysis method to obtain a structured feature vector; constructing the structured feature vector, the structured data and the unstructured data according to a preset entity linking method, a preset inference rule and a preset graph neural network model to obtain an optimized knowledge graph; an optimized knowledge graph is constructed through CT image preprocessing, deep learning feature extraction and multi-source data fusion, an image-to-structured knowledge conversion link is realized, and a comprehensive, reliable and convenient knowledge data support is provided for precise diagnosis and treatment of oral diseases.

The invention discloses a personalized physical examination period dynamic estimation system and method based on big data, and relates to the technical field of medical health data processing and intelligent evaluation. The estimation method comprises the step of constructing an integrated implementation framework including a multi-source data integration link, a multi-party cooperative processing link, a three-step modeling evaluation link and a quantitative feedback adjustment link. According to the personalized physical examination cycle dynamic estimation system and method based on big data, a medical institution, wearable equipment, family genetic disease history and government affair medical treatment are integrated through a cross-domain data fusion platform, and a multi-dimensional evaluation basis covering physiology, behavior, heredity and resource collaboration is constructed; and then through a three-step modeling process of a hybrid model training engine, the problem of insufficient data is solved through similar group data adaptation, nonlinear correlation features are mined through multiple decision trees, a risk level is finally output, and a period is dynamically adjusted in combination with medical resource assessment.

The invention provides a phytophagous insect food web DNA molecular data automatic analysis method and device based on high-pass sequencing and a storage medium, and relates to the field of molecular biological information detection.The method comprises the steps that sequence splicing, screening and species identification are carried out on obtained double-end sequencing data and local and downloaded DNA databases through an automatic system, and a DNA molecular database is obtained; generating an Excel table containing species names and a DNA bar code sequence file; performing comparative analysis on the double-end sequencing data by adopting matching splicing, and generating a contiguous group sequence based on a local DNA database; if the matching splicing cannot generate the effective sequence, generating a new gene file by adopting non-parameter splicing, and performing gene annotation in combination with the downloaded DNA database; all analysis steps are connected in series through standardized parameter input, including gene screening through threshold values and generation of insect recipe identification results. According to the method, the sequencing data can be subjected to full-process automatic analysis through a one-key command, and the efficiency of food web authentication high-throughput sequencing data processing is greatly improved.

The present invention discloses a system and method for healthcare diagnostics using foundation models with uncertainty triage, designed to deliver reliable, explainable, and safety-assured diagnostic outcomes across multimodal clinical data. The invention integrates a foundation model processor pretrained on diverse medical datasets with an uncertainty estimation processor configured to quantify epistemic and aleatoric uncertainties in diagnostic predictions. A triage control unit dynamically classifies cases into high, medium, and low-confidence categories based on computed uncertainty indices, ensuring that only high-confidence cases are automatically finalized, while uncertain or ambiguous cases are routed for clinician review. The system further incorporates a feedback adaptation processor that recalibrates model parameters and uncertainty thresholds based on expert feedback, maintaining alignment with clinical reliability standards over time. Implemented as a hardware-integrated diagnostic device, the invention supports real-time inference, secure data handling, and interpretability visualization through uncertainty heatmaps and attention overlays.

The invention discloses a medical insurance intelligent auditing method and system based on a multi-modal large model, and relates to the technical field of big data processing, and the method comprises the steps: recognizing a material name and a personal name, and auditing the compliance of a material needed for business handling according to the integrity and correctness of the material; identifying first expense detail data according to the expense detail list material and the expense identification cue word; matching the first expense detail data with a medical insurance catalog vector database to obtain a closest catalog entity, and combining the closest catalog entity with the first expense detail data to form second expense detail data; obtaining medicine knowledge from the medicine specification, matching the medicine knowledge with the medical insurance directory vector database, and associating the directory code of the optimal directory entity into the corresponding medicine knowledge; and for each record of the second expense detail data, performing expense rationality auditing according to the corresponding intelligent auditing rule and medicine knowledge. Content acquisition, medical project matching and intelligent auditing of the electronic material are realized, and the auditing speed and accuracy are improved.

A method and a system for analyzing the particle size of fly ash are provided. The method includes performing drying treatment on the sample to ensure that the water content is lower than 0.5%; uniformly dispersing the particles by using ultrasonic wave or airflow dispersion technology; measuring the particle size by laser particle size analyzer to generate cumulative distribution curve; calculating the median particle size and specific surface area by cumulative distribution curve. The system of the disclosure has an automatic operation function, and may automatically complete sample treatment, particle size measurement, data analysis and result output. This method improves the accuracy of measurement and data processing, and is suitable for efficient analysis of a large number of samples, which has a wide industrial application prospect.

The invention discloses an emergency method for extreme weather prediction of a power system. The method comprises the following steps: comprehensively analyzing data from a meteorological satellite, a ground meteorological station and a radar detection system by using a multi-source data fusion algorithm; developing a precise weather prediction model by integrating numerical weather prediction, machine learning optimization and data visualization technologies; carrying out vulnerability analysis on the key components of the power system to establish a risk assessment model, and assessing the operation risk of the power system under the extreme weather event; based on a risk assessment result, a real-time monitoring system and an automatic early warning mechanism are established, and an early warning signal is sent to an electric power system operator in time; strategy measures are formulated and implemented according to different weather situations, so that reliable power supply of the key area is ensured; and according to geographical and climate conditions of different regions, configuration and function optimization are carried out on the power management software. Through accurate meteorological data processing and model prediction, the weather prediction accuracy is improved, and early warning information is sent out in time.

The invention relates to a hospital big data laboratory data compliance dynamic auditing and error correction method and system, and relates to the technical field of data processing. The method mainly comprises the following steps: auditing original medical data through an auditing rule set to obtain an auditing label corresponding to each piece of original medical data; according to the audit tag, storing the index position, the audit tag, the data hash value, the timestamp and the node digital signature corresponding to the original medical data into a public chain or a private chain in the block chain; obtaining corresponding abnormal original medical data through an index position stored in a private chain in the block chain, and determining a data exception type of the abnormal original medical data corresponding to the index position; determining an error correction scheme according to the data exception type and the corresponding abnormal original medical data; and error correction is carried out on the abnormal original medical data through an error correction scheme.

The invention discloses a medical data interpolation method and system based on feature association, and belongs to the technical field of medical data processing, and the method comprises the steps: building a feature relationship condition model through quantifying the association strength between data in a to-be-interpolated data set according to the variable type of the data; according to the asymmetry degree of the to-be-interpolated data set on data distribution, selecting a corresponding data transformation function to perform pre-interpolation on missing values in the to-be-interpolated data set to obtain a pre-interpolation result; regularizing the pre-interpolation result based on the importance degree of each data in the data set to be interpolated; and correcting the pre-interpolation result after regularization according to the characteristic relation condition model and the medical rationality constraint to obtain an optimal interpolation result of the data set to be interpolated. Therefore, by implementing the method, the problem that the data interpolation result is low in precision and difficult to adapt to the actual application scene in the prior art can be solved.

The invention discloses a newborn rare disease intelligent screening and diagnosis system based on multi-omics data fusion, relates to the technical field of medical data processing, and aims to solve the technical problems that a traditional diagnosis method is long in time consumption and low in accuracy and cannot meet the requirement for rapid and accurate diagnosis of newborn rare diseases. The data acquisition module is used for acquiring clinical omics data from a doctor's advice database through a neonatal medical record, and acquiring blood samples to acquire gene data and metabonomics data and marking when the medical record records that the neonatal suffers from a rare disease; the data processing module is used for preprocessing the clinical omics data according to the neonatal medical record to obtain a training set and a test set, and preprocessing the gene data and the metabonomics data according to the blood sample; and the diagnosis module is connected with the data processing module. By constructing a three-level modular architecture, the problem of missed diagnosis caused by traditional multi-source data isolated analysis is effectively solved, and the accuracy rate of newborn rare disease diagnosis is remarkably improved.

The invention provides a data processing method and device based on clinical test data, in the application, a prior probability and a historical likelihood of a symptom corresponding to target clinical test data can be determined through a historical clinical database, the prior probability reflects a basic epidemic rate of an indication in a population, and the historical likelihood of the symptom corresponding to the target clinical test data can be determined through the historical clinical database. According to the historical likelihood, association rules between symptoms and indications are mined from historical cases, the association rules and the indications are dynamically updated through a Bayesian formula, an inference chain conforming to clinical logic is formed, then, a complex multi-feature joint probability estimation problem is converted into product calculation of single-feature statistics through conditional independent assumption, and a probability estimation result is obtained. The problem of calculation feasibility under high-dimensional data is solved, probabilistic output provides a quantitative basis for auxiliary determination of indications, a data-driven statistical rule is converted into a clinically understandable auxiliary support tool, and objectivity, consistency and scientificity of diagnosis decisions are effectively improved.