31 results about "Main diagnosis" patented technology

The invention relates to the technical field of medical information processing, in particular to a main diagnosis rationality judgment method and system based on artificial intelligence. The method comprises the following steps: acquiring diagnosis request data, and performing candidate main diagnosis generation on the diagnosis request data by using preset medical knowledge graph data and preset intelligent detection rule data to obtain candidate main diagnosis data; performing diagnosis execution parameter generation according to the candidate main diagnosis data to obtain candidate main diagnosis parameter data; acquiring historical diagnosis request data and historical candidate main diagnosis data, and performing diagnosis linkage processing according to the historical diagnosis request data and the historical candidate main diagnosis data to obtain diagnosis linkage network data; and performing node interaction processing on the candidate main diagnosis data according to the diagnosis linkage network data to obtain candidate main diagnosis weight data. According to the method, the intelligent level of diagnosis rationality judgment, the credibility of a scoring result and the adaptability of a clinical system are improved.

The embodiment of the invention provides a clinical resource reservation method and device, equipment and a storage medium. The method comprises the following steps: constructing a diagnosis and treatment portrait of any patient according to main diagnosis information of the patient; according to the historical occupation time sequence information of each clinical resource, predicting target occupation time sequence information of the clinical resource in a future time period; according to the diagnosis and treatment portrait of the patient and the target occupation time sequence information of each clinical resource, a corresponding resource reservation path is generated for the patient, and the resource reservation path comprises the optimal diagnosis and treatment action of the patient in each time step, the clinical resource occupied by the optimal diagnosis and treatment action and the resource reservation time. According to the embodiment of the invention, the method can achieve the efficient and reasonable reservation of clinical resources, gives consideration to the maximum utilization of the clinical resources and the minimum waiting time of the patient, guarantees the full and effective utilization of the clinical resources, and improves the medical treatment efficiency of the patient.

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 a DRGs payment-oriented diagnosis basis retrieval method and system, and relates to the technical field of DRGs payment, and the method comprises the steps: obtaining the identity information, employment information and historical diagnosis information of diagnosis doctors, carrying out the calculation of a comprehensive evaluation value for each doctor, carrying out the evaluation of the physical change characteristics and mental health degree of a patient, and carrying out the calculation of the comprehensive evaluation value. Forming a diagnosis feature set with disease information of a patient, a preliminary diagnosis result, a treatment scheme and identity information of a doctor, judging a treatment resource use index according to actual treatment data, and forming a treatment feature data set with rehabilitation of a main diagnosis disease and existence of complications; an assessment feature set is formed according to a treatment feature data set, a diagnosis feature set and a comprehensive assessment value of a doctor, assessment values of main diagnosis and auxiliary diagnosis are analyzed by establishing a diagnosis assessment model, and a retrieval data set is formed according to adjusted diagnosis results, actual treatment data and complications which are converted into clinical codes. And performing grouping through the established DRGs grouping model.

The invention provides a DRG group-entering recommendation method and device based on a large model, equipment and a medium, and the method comprises the steps: carrying out the disease extraction of a medical record of a to-be-recommended patient based on a medical model, obtaining a diagnosed disease, and carrying out the main diagnosis selection based on the diagnosed disease, a diagnosis disease list and the medical record, and obtaining a main diagnosis disease; dRG grouping recommendation is carried out based on main diagnosis diseases, the DRG grouping recommendation result of the patient to be recommended is obtained, the defects that current DRG grouping is poor in clinical adaptability, important information is prone to being missed, and accuracy and reliability are insufficient are overcome, diagnosis with insufficient basis is eliminated from the perspective of diagnosis treatment through a medical model, and therefore a diagnosis disease list is optimized and perfected, and the diagnosis efficiency is improved. On the basis, main diagnosis selection and DRG grouping recommendation are carried out according to medical records, accurate and reliable DRG grouping can be achieved, and the method has extremely high interpretability and adaptability in the clinical use process and can meet actual application requirements.

The present application relates to the technical field of natural language processing, and discloses a large language model clinical diagnosis capability fine-grained evaluation method and system. The method comprises: obtaining a patient medical record data set; synchronously performing the following tasks: scoring a question-standard answer-model answer triple based on a predetermined scoring dimension, calculating the medical knowledge score of the evaluated model; the evaluated model performs a recall task and a differential task, confirms or excludes a differential diagnosis list, and obtains a final main diagnosis; linking the differential diagnosis list and the final main diagnosis output by the evaluated model in the patient medical record; and analyzing factors affecting the clinical diagnosis capability of the evaluated model based on the linking result. The present application aims to deeply and synchronously evaluate the medical knowledge and reasoning capability of a large language model based on the same clinical diagnosis situation.

The invention discloses an APG settlement method and system based on outpatient disease category grouping, and aims to solve the problems of difficulty in automatic identification of outpatient main diagnosis and main operation and insufficient consistency with policy versions. The method comprises the following steps: constructing a heterogeneous evidence graph comprising a doctor-seeing inner layer, an individual longitudinal layer and an APG and policy layer, adopting a graph Transform constrained by relation perception and a monotonous time sequence attention mask, setting diagnosis and operation interactive decoding, introducing a probability soft logic rule bound with a policy version, and carrying out joint differentiable first K item selection, and main items are determined, and main group and additional group mapping and settlement calculation are completed, so that the technical effects of accurate main item identification, consistent time sequence, policy compliance and explainable end-to-end settlement are realized.

The invention discloses a circular non-coding RNA (Ribonucleic Acid) molecule circSENP6 and application thereof. The nucleotide sequence of the circSENP6 is shown as SEQ ID NO. 1. The invention also discloses a preparation method of the circular non-coding RNA molecule circSENP6. The circular non-coding RNA molecule circSENP6 can be used as a diagnostic biomarker for coronary atherosclerosis, is used for biological identification and clinical detection of coronary atherosclerotic heart disease, and can be used for early discovering coronary heart disease patients, reducing the death rate of coronary heart disease and improving the prognosis of patients by using the expression level of the gene for diagnosis. At present, the main diagnosis method for coronary atherosclerotic heart disease has the risks of high operation danger coefficient, large radiation and the like, and by collecting peripheral blood of a patient for detection, the method has the advantages of small trauma, good repeatability and the like, and is easier to accept.

The invention discloses application of FGL1 protein in preparation of a kit for diagnosing vertigo. Clinically, diagnosis of vertigo depends on diagnosis experience of doctors, complaint of patients becomes a main diagnosis basis, the doctors perform preliminary diagnosis according to physical examination, medical history inquiry and the like and then perform clear diagnosis according to etiological examination, diagnosis time is long, and preliminary diagnosis subjectivity is high. It is found that the FGL1 protein can effectively diagnose and distinguish healthy subjects and patients with vertigo, the diagnosis accuracy is high, the FGL1 protein can be used for being developed into a kit for diagnosing vertigo, and compared with an existing diagnosis method, the diagnosis efficiency and objectivity are obviously improved.

The application provides a method and device for training a model, and a method and device for predicting a sub-diagnosis intention. An electronic device supports obtaining a target main diagnosis intention of a patient in a process of queuing for diagnosis, and predicting and outputting, based on a prediction model of a sub-diagnosis intention, a sub-diagnosis intention in which a patient inputs a sub-named entity from a plurality of sub-diagnosis intentions cascaded with the target main diagnosis intention, so that the patient can input a sub-named entity to the output sub-diagnosis intention, to collect answers of the patient to questions in advance automatically by using time of the patient in the process of queuing for diagnosis, and facilitate automatic recording of a disease of the patient, so as to avoid omission and misdiagnosis or missed diagnosis. Furthermore, after the patient enters a diagnosis stage, a doctor can directly obtain answers of the patient to questions, without real-time questioning of the patient by the doctor, so as to save time of the doctor and the patient in the process of diagnosis.

The invention provides an elevator brake fault diagnosis method based on multi-scale feature distillation, and relates to the field of fault diagnosis of elevator brakes. The method comprises the steps that multi-channel signals related to a braking event of an elevator brake are collected, the multi-channel signals comprise PVDF strain, acceleration time domain and high-frequency acoustic emission transient pulse multi-channel signals, and standardized signals are obtained through time alignment, signal slicing, windowing and normalization processing; inputting the data into a main diagnosis network, extracting and fusing multi-scale features, completing fault type and severity double-task diagnosis, training the network in combination with a real label, and generating a soft label; a lightweight edge diagnosis network is constructed, and a deployable diagnosis model is obtained based on real label and soft label distillation training; and collecting real-time signals, processing the signals and inputting the signals into the diagnosis model to obtain fault types and severity. According to the invention, the fault identification and severity evaluation precision can be improved, and the real-time online diagnosis of the elevator brake can be realized.

The application provides a DRG grouping recommendation method, device, equipment and medium based on a large model, wherein the method comprises the following steps: based on a medical model, extracting a disease from a medical record of a patient to be recommended to obtain a diagnosed disease, and selecting a main diagnosis based on the diagnosed disease, a diagnosis disease list and the medical record to obtain a main diagnosis disease; and performing DRG grouping recommendation based on the main diagnosis disease to obtain a DRG grouping recommendation result of the patient to be recommended, which overcomes the defects of poor clinical adaptability, easy omission of important information, and insufficient accuracy and reliability of the current DRG grouping, starts from the perspective of diagnosis and treatment, excludes diagnoses based on insufficient medical models, thereby optimizing and improving the diagnosis disease list, and on this basis, the main diagnosis is selected and the DRG grouping is recommended according to the medical record, which can realize accurate and reliable DRG grouping, has strong explainability and adaptability in the clinical use process, and can meet the actual application requirements.

The invention discloses a main diagnosis quality control method based on an inference thinking mode, which comprises the following steps: firstly, collecting massive original electronic medical record data, and then combining the collected medical record data with different LLM models to construct a fine tuning data set; then selecting an LLM base model, and performing SFT fine tuning on the LLM base model by adopting a LoRA technology to obtain an LLM fine tuning base model; continuously carrying out GRPO reinforcement learning on the LLM fine tuning base model by adopting a LoRA technology to obtain a quality control model after reinforcement learning; and finally inputting to-be-analyzed electronic medical record data into the quality control model, and outputting a corresponding quality control analysis result by the quality control model. The method has the remarkable effects that various demands of high efficiency, high precision, high interpretability, high coverage rate, low maintenance, clinical easiness in use and the like are simultaneously met in the same technical framework, and the technical blank of the main diagnosis quality control field is filled; and a landing, sustainable and extensible normal form-level solution is provided for medical insurance settlement, performance assessment and medical data management.

The invention provides a medical record homepage main diagnosis recommendation method and system based on rule-model collaboration, and belongs to the technical field of medical information. The method comprises the following steps: extracting, analyzing and standardizing structured and unstructured data in a home page of a medical record to generate structured data for feature calculation; based on the structured data, constructing a multi-dimensional feature vector including clinical priority, ICD coding attributes, resource consumption and patient basic information; enhancing the multi-dimensional feature vector through a rule-model cooperation mechanism to form an enhanced feature vector; and inputting the enhanced feature vector into a trained machine learning sorting model to obtain a recommendation probability of each candidate main diagnosis, and outputting a main diagnosis recommendation list based on the recommendation probability. According to the invention, the accuracy and efficiency of main diagnosis selection are effectively improved, and the risk of medical insurance payment refusing and the workload of doctors are reduced.

The present invention relates to the technical field of medical information processing, and particularly to a method and system for judging the rationality of the main diagnosis based on artificial intelligence. The method includes the following steps: obtaining diagnostic request data, and using preset medical knowledge graph data and preset intelligent detection rule data to generate candidate main diagnoses for the diagnostic request data to obtain candidate main diagnosis data; generating diagnostic execution parameters according to the candidate main diagnosis data to obtain candidate main diagnosis parameter data; obtaining historical diagnostic request data and historical candidate main diagnosis data, and performing diagnostic linkage processing according to the historical diagnostic request data and the historical candidate main diagnosis data to obtain diagnostic linkage network data; performing node interaction processing on the candidate main diagnosis data according to the diagnostic linkage network data to obtain candidate main diagnosis weight data. The present invention improves the intelligent level of the judgment of the rationality of the diagnosis, the credibility of the scoring result and the adaptation ability of the clinical system.

The invention provides a vehicle fault prediction method based on AUTOSAR, and the method comprises the steps: A, carrying out enhanced data collection and diagnosis execution, recording key parameter snapshots and supporting a periodic broadcast mode through expanding a diagnosis event manager DEM and a diagnosis communication manager DCM of a basic software layer of the AUTOSAR; b, light-weight in-vehicle data aggregation, wherein prediction required data signals distributed on different electronic control units (ECU) are aggregated to an application layer of a main diagnosis ECU through an environment RTE during operation of the AUTOSAR; step C, deploying and executing loosely coupled intelligent prediction, packaging a prediction algorithm into an independent AUTOSAR software component SWC, and subscribing real-time data flow and accessing historical fault data by the SWC through an RTE port; and D, performing fault prediction by adopting a hybrid prediction model in combination with the physical model and the data-driven model. The safety and reliability of the vehicle are effectively improved, and the full-life-cycle maintenance cost is reduced.

The application discloses a kind of multi-granularity semantic coverage detection and step deviation diagnosis method based on knowledge graph ontology constraint.The method inquires standard step sequence and each standard step pre-associated check item, atomic semantic slot, weight and order constraint from knowledge graph according to current job task, and obtains to be audited step sequence;According to knowledge graph ontology, the semantic analysis and standard concept mapping of to-be-audited step are carried out, and the filling step semantic slot set is obtained;Combining vector similarity generates candidate to-be-audited step set for each standard step;In the range of candidate set, check item coverage audit and atomic semantic slot coverage audit are carried out respectively, and check item coverage score and atomic semantic coverage score are obtained;Based on the score, order constraint penalty term and forced atomic slot missing penalty term, fusion coverage score is calculated, and coverage state is determined according to fusion coverage score and forced atomic slot coverage condition;According to the preset priority of missing step or pre-step missing, order error, over-generalized merging, split expression, effective merging, redundant step from high to low, determine main diagnosis type;Finally, through knowledge graph ontology, step deviation type is associated to corresponding harm, consequence and control measure and output.The method can improve the accuracy, explainability and traceability of process step audit.

The invention relates to the technical field of magneto-rheological seat diagnosis, and discloses a fault diagnosis method based on a magneto-rheological seat. The method comprises the following steps: acquiring current state data of the magnetorheological seat, and establishing a fault feature database mapped with the current state data; configuring diagnosis targets according to the fault feature database, wherein the diagnosis targets comprise vibration abnormity diagnosis, pressure distribution diagnosis and response time diagnosis; then determining a target classification identifier in the diagnosis target, wherein the target classification identifier comprises a main diagnosis target, an auxiliary diagnosis target and a reference target; after key diagnosis parameters are selected through the main diagnosis target and the auxiliary diagnosis target, diagnosis target influence analysis of the key diagnosis parameters is executed; and finally, after parameter optimization constraints are established according to the diagnosis target influence analysis result, parameter directional optimization is executed, and magneto-rheological seat fault diagnosis is completed by using a parameter directional optimization result. According to the method, the diagnosis efficiency and accuracy can be improved, and stable operation of the magneto-rheological seat is guaranteed.

The present invention provides a method and system for screening medical records with incorrect primary diagnoses, including: obtaining all entities in each medical record in a set of medical records to be screened; the entities include: symptoms, diseases, and signs; calculating a weight value of each entity in each medical record based on the primary diagnosis of each medical record; screening out a first set of medical records with incorrect primary diagnoses from the set of medical records to be screened based on the weight values; the first set of medical records is a set of medical records in which the weight value of each entity is less than a preset threshold. The present invention alleviates the technical problem of low screening accuracy of medical records with incorrect primary diagnoses existing in the prior art.

The invention provides an electronic medical record disease classification coding method and device, and the method comprises the steps: carrying out the preprocessing of electronic medical record data, and obtaining diagnosis and treatment information; inputting the diagnosis and treatment information and an external standard data set into the trained first large model, and outputting at least one piece of first diagnosis information; inputting the at least one piece of first diagnosis information and an external standard data set into a plurality of trained second large models, and respectively obtaining at least one piece of second diagnosis information output by each second large model; and judging whether the proportion of the same second diagnosis name in the first sequence and the second ICD-11 code combination in the plurality of second large models is greater than or equal to a specified threshold value or not, and if so, combining the same second diagnosis name in the first sequence and the second ICD-11 code combination as the main diagnosis. The invention provides an electronic medical record disease classification coding method and device, which are used for solving the problems of low coding accuracy and incomplete disease coverage in the prior art.

The invention relates to the technical field of data processing, in particular to an automatic treatment information classification and collection method for medical system performance assessment management, and the method comprises the steps: obtaining a plurality of dimension data in the treatment information of each patient, taking any department as a target department, and taking the treatment information of the target department as target treatment information; determining an association degree between each piece of target treatment information and the target department, so as to screen out a main diagnosis and treatment direction of the target department from each type of disease in the target treatment information, thereby determining a performance assessment participation degree of each piece of dimension data of each piece of reference treatment information; the method is used for dividing all dimension data of each piece of reference treatment information into a plurality of classes. According to the method, the classification result is more accurate and credible by analyzing the participation degree of the data of each dimension of the doctor seeing information in the performance assessment.

The utility model relates to the technical field of medical instruments, in particular to a disposable hard bronchoscope which comprises an operation end and a bronchoscope tube, one end of the bronchoscope tube is an inclined opening, the operation end is detachably connected with the bronchoscope tube, the bronchoscope tube is a hard tube, the operation end is provided with a main examination port, a jet vent, an auxiliary vent and a blocking portion, the main examination port is communicated with the bronchoscope tube, and the jet vent is communicated with the auxiliary vent. The spraying ventilation opening, the blocking part and the auxiliary ventilation opening are formed in the two sides of the main examination opening, the spraying ventilation opening is communicated with the main examination opening, an air bag assembly used for expanding the trachea is arranged on the endoscope tube and comprises an annular air bag and an air inlet tube, the other end of the air inlet tube is connected with a connecting assembly, and the connecting assembly comprises a three-way connector. The disinfection device disclosed by the utility model has the beneficial effects that the disinfection cost is effectively reduced, and cross infection is prevented; the endoscope tube is communicated with the main examination port, one light source port is omitted, the number of connectors is small, the inner wall is smooth, and airflow is stable.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent triage system and method based on artificial intelligence questions and answers, and the system comprises an information collection module, a triage question module, a multi-department triage module and a triage response module which are in communication connection in sequence. The method corresponds to the system. According to the application, the patient information is accurately acquired through the information acquisition module, and the symptom information is refined through interdisciplinary dynamic questions and answers of the triage question module to generate the optimized disease information, so that the accuracy of association between the symptom and the department is improved; the multi-department triage module quantifies the distribution probability and support degree of each department to realize advanced pre-judgment of interdisciplinary collaboration; the triage response module is combined with real-time response of departments to dynamically determine main diagnosis and support departments, referral caused by misjudgment of a single department is reduced, triage efficiency is improved, the problems that interdisciplinary disease triage is difficult and cooperation is lagged are solved, medical resource configuration is optimized, and patient treatment experience and medical service quality are improved.

The invention relates to the technical field of natural language processing, and discloses a large language model clinical diagnosis capability fine-grained evaluation method and system. The method comprises the following steps: acquiring a patient medical record data set; synchronously executing the following tasks: scoring the question-standard answer-model answer triple based on a predetermined scoring dimension, and calculating a medical knowledge score of the evaluated model; executing a recall task and an identification task by the evaluated model, and confirming or excluding the identification diagnosis list to obtain final main diagnosis; linking a differential diagnosis list in the medical record of the patient and output by the evaluated model with the final main diagnosis; and analyzing factors influencing the clinical diagnosis capability of the evaluated model based on a link result. The invention aims to deeply and synchronously evaluate the medical knowledge and reasoning ability of the large language model based on the same clinical diagnosis situation.

The application belongs to the technical field of intelligent management of ancient buildings, and provides an ancient building wood component diagnosis and prevention intelligent management method combined with digital twinning, comprising: processing the main diagnosis surface image of the wood component by using a damage diagnosis model to obtain damage state recognition results; integrating the static attribute data of the wood component, and environment exposure data, damage state recognition results and maintenance data to obtain a structured digital twinning archive; obtaining the feature vector of the wood component at each historical time point based on the structured digital twinning archive, and stacking them in time sequence into a three-dimensional feature tensor; inputting the three-dimensional feature tensor into a multi-task time series prediction model to obtain the future damage prediction result of the wood component and the damage influence weight of the historical time point; calculating the SHAP value of the index; generating damage cause description; matching the damage cause description with a decision rule library to obtain the maintenance decision instruction of the wood component and sending it. The accuracy and forward-looking nature of the future damage prediction result of the wood component are improved, and a decision closed loop is formed.

The application discloses a DRG high set identification method, medium and equipment based on bill backtracking and causal imprint, and the method comprises the following steps: acquiring medical charging detail data of a to-be-audited medical record, extracting diagnosis and treatment project features and mapping to a medical knowledge graph to obtain a feature atom set; inputting the feature atom set into a large language model to output a real disease phenotype as a pseudo label, and performing semantic comparison with a reported main diagnosis to identify main diagnosis high set; performing score sensitivity grading on other diagnoses reported, screening high-value complication entities, verifying resource consumption matching degrees of the high-value complication entities through a clinical path constraint library, and identifying fictitious complications; extracting main charging items and a low-value charging imprint rule library, identifying virtual increase of main trunk intervention projects through counterfactual causal comparison; and finally, calculating a comprehensive abnormality index by comprehensively considering deviation, matching degree and deviation, and outputting an audit report. The application automatically identifies DRG high set abnormal behaviors from multiple dimensions, and improves the accuracy and comprehensiveness of the audit.

The invention discloses a meta-learning bearing fault diagnosis method and system based on an adaptive task generator. Defining a performance index sequence, and training a self-adaptive task generator through the historical performance index sequence, so that the self-adaptive task generator has the capability of dynamically generating a task sampling probability; constructing a collaborative meta-learning optimization framework of the main diagnosis model and the self-adaptive task generator, and realizing collaborative evolution of the main diagnosis model and the self-adaptive task generator through alternate iteration of internal loop rapid adaptation and external loop generalization evaluation; the task difficulty is dynamically adjusted by quantifying a difficulty index and an annealing mechanism, so that the task difficulty is gradually increased; and performing multi-round iterative optimization until model performance convergence, and outputting a final main diagnosis model and a self-adaptive task generator model. Through historical performance driven task generation, collaborative optimization of the main model and the generator and dynamic difficulty adjustment, the diagnosis precision and generalization ability of the model in a small sample scene are improved.

The application discloses a disease diagnosis automatic coding method and system, and belongs to the technical field of medical event recognition. The method comprises the following steps: acquiring and structuring preprocessed disease diagnosis documents, displaying the document directory and content; using a diagnosis coding model to extract diagnosis names, evidences and surgical operations from the documents, and displaying the diagnosis names, evidences and surgical operations in a multi-window in a highlighting mode; supporting auditing, commenting, editing, merging and traceability positioning of the diagnosis and evidence information; automatically generating a diagnosis coding result, sorting the diagnosis coding result, modifying the main diagnosis and adjusting the sequence of the associated evidences; the system supports DRG / DIP simulation grouping display, and assists in coding sequence decision-making; the coding result can be saved, collected and uploaded to a related system, and a complete coding closed-loop process is constructed. The application can greatly reduce the work burden of coders, improve medical insurance settlement efficiency and data quality, and has good clinical application value and popularization prospect.

The present application relates to a kind of settlement based on grouping recommendation system and method of list, belong to DRG grouping technical field.The present application discloses grouping recommendation system includes in situ fuzzy grouping module, ectopic fuzzy grouping module, QY (ambiguity) grouping module, missing code recommendation module, grouping fit degree module, and grouping recommendation method steps are as follows: in situ fuzzy grouping is carried out, and grouping range is expanded;Ectopic fuzzy grouping is carried out, and grouping range is further expanded;QY (ambiguity) grouping supplement is carried out, and grouping is carried out with the main diagnosis code of surgical coding collocation, obtains the possible existence of real grouping;Missing code grouping supplement is carried out, and in situ fuzzy grouping is carried out, obtains the possible existence of real grouping;The fit degree of S1-S4 all groupings is calculated respectively, and grouping fit degree value is arranged in descending order form and is arranged in list.The present application can recommend the most suitable grouping to doctor by analyzing the position of ICD coding, classification relationship.

The invention relates to a medical insurance settlement method based on a DRG model, and the method comprises the steps: firstly determining a basic weight according to a basic DRG group of a main diagnosis disease in a target case; then, aiming at the comprehensive disease feature vectors corresponding to the main diagnosis disease and the whole complications, applying a concurrent synergistic effect model to obtain corresponding concurrent superposition coefficients; according to the regional basic rate under the DRG model, the medical insurance payment amount corresponding to the target case is calculated and obtained; according to the design scheme, by introducing a concurrent superposition coefficient, precise quantification is achieved for cost increase caused by the nonlinear synergistic effect among multiple complications, the fairness and precision of DRG payment are remarkably improved, the reasonable income of medical institutions for treating complex cases can be guaranteed, the method can adapt to medical technology progress and cost change, and the method is suitable for popularization and application. And core technical support is provided for scientific and refined treatment of a medical insurance payment system.