96 results about "Medical quality" patented technology

The invention discloses an electronic medical record intelligent evaluation method based on complex quality control indexes, and relates to the field of medical information processing and artificial intelligence. The method comprises the steps that an original electronic medical record text is collected and preprocessed, and structured diagnosis and treatment information and an event sequence diagram are extracted; through prompt word chain construction and a semantic reasoning mechanism, a large language model is guided to intelligently evaluate complex quality control indexes in medical records. The complex quality control indexes comprise diagnosis basis sufficiency, treatment scheme rationality, key result and change record integrity, treatment measure integrity and causal relationship rationality. According to the method, technologies such as a medical knowledge graph, a graph neural network and semantic vector retrieval are utilized to realize external knowledge recall and causal reasoning support; a self-consistency reasoning mechanism, a self-reflection mechanism and a multi-model cross validation mechanism are introduced to improve the accuracy and credibility of an evaluation result; and finally, outputting a structured quality control report and a visual reasoning chain. According to the method, the intelligence and refinement level in a complex medical quality control task can be remarkably improved, high interpretability and practical value are achieved, and the method is suitable for application scenes such as hospital quality management, scientific research evaluation and medical document standardization.

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

The invention discloses an intelligent physical examination data quality control analysis method and system, and relates to the technical field of medical quality control. The problems that existing physical examination data are diversified in source, complex in format, single in quality control means and the like are solved. Structured data, medical images and texts are integrated, and standardized processing and data mapping are achieved through a unified platform. Static and dynamic rule libraries are constructed, and the threshold is dynamically adjusted in combination with individual features, so that personalized risk assessment is realized. Structured data anomaly detection is performed by adopting machine learning, an image quality problem is identified by utilizing a convolutional neural network, and text anomaly is processed and analyzed through a natural language, so that the anomaly detection accuracy is improved. The system realizes multi-dimensional quality control and intelligent early warning, enhances the integrity and credibility of physical examination data, supports precise health management and disease early warning, improves the quality control efficiency, meets the intelligent analysis requirements of large-scale multi-modal physical examination data, and promotes the application of intelligent health management.

The invention discloses a medical insurance cost prediction and optimization method based on big data analysis, and belongs to the technical field of medical data processing. The method comprises the following steps: constructing a multi-source data acquisition module, and integrating data of an HIS system, a medical insurance platform and wearable equipment by using an FHIR interface and a block chain; a BERT-BiLSTM-CRF model is adopted to fuse multi-modal data, a dynamic model group containing Transform anomaly detection and LSTM-ARIMA time sequence prediction is established, and the prediction error rate is reduced to 12% (reduced by 28% compared with that of a traditional method); a multi-objective optimization system is designed, medical quality and cost control are balanced based on an improved NSGA-II algorithm, the cost of a single disease is reduced by 18%-25%, and the quality standard reaching rate exceeds 95%; a hybrid cloud and homomorphic encryption module is deployed, and the data leakage risk is reduced by 90%; a policy sandbox system is constructed, medical insurance policy simulation deduction is supported, and the response time is shortened to 72 hours. According to the invention, the problems of data islands, low prediction precision and privacy risks are solved, and the whole-process intelligent management and control of medical insurance fees is realized.

The invention discloses a risk identification method for pediatric intensive care unit nursing, which relates to the technical field of medical early warning, and comprises the following steps: collecting multi-modal data, and carrying out time synchronization on the collected data; data features are extracted based on the data after time synchronization, fusion processing is carried out through linear weighting, an optimization target is determined, effective fusion of multi-modal data is achieved, and fused high-quality data is obtained; constructing a risk assessment model, calculating a risk value for each time point in combination with historical data, obtaining an overall risk value through weighted average processing, judging a risk level, and optimizing prediction accuracy through a self-correction algorithm; the state of a patient is monitored in real time, the early warning level is set, abnormal conditions are early warned according to the corrected risk value variation, feedback of medical staff is collected, and the system performance is continuously optimized. The method has remarkable clinical application value, and can effectively improve early warning accuracy, optimize medical resource allocation, reduce medical risks and improve overall medical quality.

The invention discloses an intelligent medical record quality control method based on a rule and large language model hybrid engine, and relates to the technical field of natural languages, and the method comprises the steps: obtaining original electronic medical record data from a medical information system; performing post-structured processing on the original electronic medical record data, and converting an unstructured or semi-structured text in the original electronic medical record data into a unified structured data object readable by a machine; traversing a plurality of quality control points in a preset quality control rule base, and for each quality control point, adaptively selecting and executing a corresponding quality control path based on the converted structured data object; and based on the quality control results of all the quality control points, performing score calculation and generating a quality control report. According to the method, the automatic and deep quality control of the full-dose medical record can be realized, form inspection is covered, connotation logic analysis can be executed, the system is endowed with excellent flexibility and regional expansion capability, and reliable technical support is provided for improving the overall level of medical quality management.

The invention relates to the field of clinical terms and the technical field of digital coding, and discloses a data processing method for ICD coding recommendation of outpatient medical records, and the method comprises the following steps: carrying out the preprocessing of outpatient medical record texts, including cleaning, segmentation, coding standardization and punctuation standardization; carrying out medical entity identification and assertion identification on the preprocessed medical record text by adopting a medical entity identification model and an assertion identification model, generating structured information containing evidence fragment offset, and analyzing unstructured and spoken contents by carrying out preprocessing and medical entity identification and assertion identification on the medical record text, so as to obtain a pre-processed medical record text; key information of negative expression and time sequence dependence is captured, structured and standardized processing of medical entity information is guaranteed, the code error and missing risks in code recommendation are reduced, and the accuracy of medical quality control and medical insurance settlement is improved.

The invention relates to the technical field of peritoneal drainage, in particular to a peritoneal drainage system based on hemoglobin real-time monitoring and intelligent early warning. According to the technical scheme, the device comprises an abdominal cavity drainage tube, an optical sensing module, a microprocessor module and a wireless communication module, wherein the microprocessor module is configured to perform moving average filtering and dynamic trend modeling on hemoglobin concentration data; and triggering early warning according to a preset multi-level alarm condition. The hemoglobin concentration in the peritoneal drainage liquid is accurately monitored in real time, intelligent early warning and a personalized response mechanism are combined, the limitation of traditional manual observation is broken through, the timeliness and accuracy of bleeding risk identification are remarkably improved, the infection risk is reduced through the closed design, the medical care working process is optimized through the system integration capacity, and the medical care efficiency is improved. Data sharing and remote cooperation are achieved, intervention time windows are shortened, patient prognosis is improved, nursing resources can be released, medical quality is improved, and a comprehensive and efficient solution is provided for postoperative monitoring.

The invention relates to a diabetes management system, equipment and a medium. The method comprises the steps that a data processing module collects multi-modal medical data, dynamic time sequence features are extracted through a graph neural network to label semantic tags, and a structured patient information data set is formed; in the database construction module, a data fusion unit performs cross-modal feature alignment, redundancy elimination and feature enhancement on the structured data set, and establishes a dynamic and unified health record database; and the risk assessment unit extracts individual features by adopting an improved random forest algorithm based on the database, and generates a personalized health risk assessment report. And the health management module customizes a health management scheme through a generative adversarial network according to the evaluation report, and dynamically updates intervention measures according to real-time monitoring data. By adopting the system, the accuracy and individuation degree of risk prediction can be improved, accurate and dynamic whole-cycle health management service is provided for diabetics, and the management efficiency and the medical quality are effectively improved.

The invention provides a medical quality dynamic evaluation method and system based on machine learning. The method comprises the following steps: acquiring a cross-mechanism desensitization data set, constructing a medical quality causal diagram, generating an anti-fact intervention scene by using Do calculation, and outputting an interpretable causal relationship model; constructing a federated learning optimization framework, aggregating causal relationship model parameters of all medical institutions, introducing a doctor crowdsourcing labeling mechanism to correct a result, and generating a global medical quality dynamic evaluation model; and constructing a surgical digital twin model, mapping dynamic data, and generating a dynamic quality score. Through a federated learning framework, the limitation of single mechanism data is broken through, parameters are optimized, and the performance is improved; causal graph construction and anti-fact intervention scene generation are combined, causal logic is mined, and the result value is enhanced; mapping dynamic data based on a digital twinborn model to realize accurate dynamic scoring; a doctor crowdsourcing labeling mechanism is introduced, a closed-loop optimization system is formed, and finally a high-precision medical quality dynamic evaluation result is output.

The invention provides a hospital adverse event risk identification method and system based on causal reasoning, and the method comprises the steps: responding to an operation request of a user for reporting a hospital adverse event based on the optimization of medical advice data processing and a medical quality management process in an operation risk identification scene, inputting the detailed information of the event through a formatted form, and carrying out the operation of the event. Through multi-node, multi-auditor and multi-department cooperative auditing, an adverse event form reported by a user is subjected to approval, flow and summary analysis, medical advice data is subjected to structured storage by adopting visual enhancement OCR, dynamic knowledge graph verification and a time sequence causal reasoning model, a time window division algorithm is adopted, and medical advice data is obtained. The causal discovery algorithm constructs an unplanned re-operation association network, early warning of adverse events of operation complications is carried out, and compared with a traditional mode, the medical advice processing efficiency, the transcription error rate and the unplanned operation detection rate are all greatly improved.

The invention relates to the technical field of medical quality control, and particularly discloses a medical staff medical operation standard monitoring and reminding system. The intelligent analysis engine identifies operation and tracking steps, detects deviation and evaluates risks; the grading reminding intervention module performs multi-mode reminding according to risk grades; the system management and feedback module establishes a file and generates a report; and the security and privacy protection module ensures data security. Through the multi-modal data acquisition module, data is acquired from multiple dimensions of images, actions, environments, equipment interaction and the like, and comprehensive monitoring of operation of medical staff is realized. The intelligent analysis engine can accurately identify the operation type, track the operation steps, detect the operation deviation and evaluate the risk level, and provides an accurate basis for subsequent reminding intervention. According to the invention, whole-process standard monitoring, real-time reminding and personalized feedback of medical operation are realized, and the operation standardization and safety are improved.

The invention relates to the technical field of information technology management, and discloses an intelligent medical quality monitoring and case analysis system and method, and the system comprises an interface layer which is used for accessing an HIS system, an inspection image device and a patient terminal, and supports the real-time collection of multi-mode medical data of texts, images and audios; the intelligent agent layer is used for deploying functional modules including pre-inquiry, intelligent hospital guide and family doctor question and answer, the intelligent agent layer is provided with a personalized analysis module, and the personalized analysis module is used for analyzing personalized elements including the age, gender, living environment and growth experience of a patient through a self-adaptive algorithm; the core layer integrates a medical record reduction engine and a medical hidden danger identification module, and constructs a dynamic health record based on the whole course of disease data of the patient; and the model layer comprises a large language model, a multi-modal model and an inference model. According to the invention, the accuracy of medical record analysis and the pertinence of medical hidden danger identification can be improved.

The invention discloses a complex medical quality management and control index automatic calculation method based on an intelligent agent. According to the index semantic model construction method provided by the invention, the automatic conversion of the medical quality control indexes from a natural language to structured semantics is realized, so that the index definition has computability and mobility, the dependence of manual analysis and script configuration is eliminated, and the standardization, generalization and reuse efficiency of the index definition is remarkably improved. Through multi-source data semantic packaging and an MCP service abstraction mechanism, semantic unification and interface standardization of multi-source heterogeneous data such as electronic medical records, inspection information, disease course records and medical advice management are achieved, and a semantic data layer capable of achieving cross-system access is constructed; the problems of data dispersion, field isomerism and interface incompatibility in a traditional system are effectively solved.

The invention relates to a medical quality control dynamic scoring system, device and equipment based on multi-modal perception and federal evolution, and the method comprises the steps: obtaining original scene information through multi-modal input based on a medical equipment recognition model, carrying out the scene analysis and semantic fusion, generating structured medical scene data, and carrying out the dynamic scoring of the medical quality control according to the structured medical scene data. Multi-source evidence verification is carried out through a medical scoring rule base, a quality evaluation result is generated through weight dynamic adjustment, difference analysis is carried out through comparison with a manual correction result according to the quality evaluation result, and model parameters and scoring rule updating are generated. Multi-modal input is fused based on a medical special perception layer, anti-interference scene analysis and equipment semantic binding are achieved, multi-source evidences are verified in real time through a dynamic scoring engine, deduction weights are dynamically adjusted, subjective item scoring floating is compressed, an identification model is updated through federation, and a rule base is thermally loaded in real time, so that the anti-interference scene analysis and equipment semantic binding is achieved. And automatic closed-loop processing of problem items is realized.

The invention relates to the technical field of medical treatment, in particular to preoperative witness service equipment and a preoperative witness service method, which realize standardized management of the whole process of preoperative conversation, ensure that preoperative risk notification of each operation is strictly executed according to a standard process, reduce medical dispute risks caused by inadequate notification and improve the safety of medical treatment. The medical quality and management level of hospitals are improved, the intelligent system is easy and convenient to operate, the time and energy of preoperative communication can be saved, meanwhile, accurate risk informing prompts are provided, medical staff are assisted in better performing informing obligations, the working efficiency and the medical service quality are improved, the preoperative conversation content is clearly and completely recorded and presented, and the medical quality of the hospitals is improved. Therefore, the system can comprehensively and detailedly know operation risks and related matters, enhances the trust to hospitals and medical staff, and reduces misunderstanding and disputes caused by information asymmetry. Therefore, the problem that an existing preoperative conversation recording mode is not standard and is easy to tamper is solved.

The invention discloses a patient safety management informatization method and system based on a medical quality control model, and relates to the technical field of modern medical treatment, and the method comprises the steps: obtaining patient condition data based on a hospital information system; the method comprises the following steps: collecting data of a patient, training the collected data based on a dual-machine learning algorithm in combination with a boosting method, establishing a high-precision risk prediction model, and based on calculation of the risk prediction model, outputting complication, complication risk and death risk prediction of the patient; summarizing model prediction results to form a comprehensive risk assessment report; and early warning information is pushed to medical personnel in real time to assist in early intervention. Through a double-machine learning algorithm and a boosting method, the risk prediction precision is improved, medical negligence is reduced, the system can generate personalized early warning reports in real time, assist doctors in intervention and reduce complications, complication and death risks, through comprehensive risk assessment and personalized suggestions, medical resource allocation is optimized, the treatment effect is improved, and the risk prediction efficiency is improved. And medical misdiagnosis is reduced.

The invention provides a radiographic inspection real-time quality inspection system and device based on artificial intelligence and cloud edge collaboration, and relates to the technical field of radiographic inspection quality inspection.The system mainly comprises a data acquisition and preprocessing module, an AI analysis module and an early warning response and feedback module which are sequentially arranged; the data acquisition and preprocessing module comprises a medical image unit, a hospital information unit, an equipment parameter unit and a data association unit; the AI analysis module comprises a medical image recognition unit, an anatomical feature and left and right identifier analysis unit and a data fusion comparison and risk assessment unit. According to the scheme, abnormal conditions such as mismatching between the inspection application and the actual operation and image artifacts can be recognized and early warned in an extremely early stage, so that the probability of operation errors is remarkably reduced, medical quality accidents are effectively avoided, and the standardization and safety of radiation inspection are improved.

The invention relates to the technical field of medical diagnosis and treatment, and particularly provides a diagnosis and treatment scheme recommendation method and system, a terminal and a storage medium, and the method comprises the steps: collecting medical data of historical patients, the medical data comprising basic information, symptoms, examination results, diagnosis and treatment schemes and healing results of the patients; features are extracted from the basic information, symptoms and examination results of the patient, the information gain of each feature is calculated in combination with the information entropy of the diagnosis and treatment scheme, and the feature with the information gain larger than an information gain threshold value is particularly selected as the important feature influencing the diagnosis and treatment scheme; and training a decision tree model based on the important features, the diagnosis and treatment schemes and the healing results, inputting the important features by the decision tree model, and outputting one or more recommended diagnosis and treatment schemes. The scientificity and rationality of diagnosis and treatment scheme making can be improved, doctors are assisted in making higher-quality decisions, and the medical quality is improved.

The invention relates to the technical field of medical management, in particular to a medical quality assessment management system based on artificial intelligence. The system is based on a Donabedan structure-process-result model and comprises a data acquisition module, a preprocessing module, a feature extraction module, a medical quality evaluation module, an evaluation result display and feedback module, a management decision module and the like. The data acquisition module collects multi-source related data, after the data is preprocessed by the preprocessing module, the feature extraction module extracts features according to three dimensions of supportability guarantee, initiative behaviors and result output, the medical quality evaluation model outputs evaluation scores and details of each dimension according to the features, and the display and feedback module visually displays results and collects and feeds back. And the management decision module generates targeted suggestions accordingly. The artificial intelligence technology is utilized to overcome the defects of a traditional evaluation mode, comprehensive, objective and accurate evaluation and effective management of the medical quality are achieved, and a hospital is assisted to improve the medical service level.

The invention provides a medical data exception analysis processing method and system, a terminal and a medium, and belongs to the technical field of medical data processing, and the method specifically comprises the steps: obtaining and preprocessing medical original data, preliminarily screening abnormal data according to a preset rule, marking types, and storing the data in a cache region according to clinical priorities. And for the abnormal data in the cache region, matching historical cases, extracting clinical, equipment and time associated information, and outputting an analysis report. And generating an instruction packet containing an exception type, an interface parameter and an execution time limit, wherein the instruction packet is used for correcting data, calibrating equipment and rechecking a result, synchronously recording a log, triggering upgrading by a new exception and generating a new instruction packet. And when the exception appears repeatedly, generating a reverse verification instruction packet, performing sandbox reproduction, comparing differences, updating the knowledge base and the like, verifying the correction validity, and performing iteration if the correction is not passed. A medical data exception processing closed loop is formed, the processing efficiency and accuracy are improved, the medical quality and the patient safety are guaranteed, and the system adaptability is enhanced.

The invention discloses an accurate value incentive accounting method for a DIP disease category subdivision value portrait under DRG, and belongs to the technical field of medical payment and hospital management, and the method comprises the following steps: 1) a data fusion model: integrating DRG grouped data, a DIP score, a clinical path execution rate, cost accounting and patient satisfaction data, and constructing economic, clinical and social three-dimensional value portraits; 2) dynamic grouping optimization: introducing a machine learning algorithm, and dynamically subdividing DIP disease species into subgroups according to complication severity, treatment mode difference and resource consumption dispersion; after disease category subdivision, the matching degree of the DIP score and the actual cost is improved by 20%-30%, the medical insurance payment refusal risk is reduced, payment accuracy improvement is achieved, through strategic disease category support, the CMI value is averagely improved by 0.15, the difficult case recovery rate is increased by 12%, medical quality optimization is achieved, and through linkage excitation of the balance contribution rate and the patient satisfaction degree, the medical quality is improved. The medicine consumption ratio is reduced by 5-8%, the hospitalization day is shortened by 1.2 days, and the operation efficiency is improved.

This invention discloses a method, apparatus, system, and storage medium for relocating hospital departments, including: step S1, data modeling and parameter initialization; step S2, constructing a multi-objective optimization model; step S3, constructing a constraint system; step S4, dynamic priority calculation and critical path identification; step S5, relocation scheduling optimization solution; and step S6, generating the optimal relocation plan. The technical solution of this invention enables systematic optimization under multi-objective constraints, simultaneously considering economic cost, continuity of medical services, and resource utilization efficiency, while meeting the requirements of medical quality and safety.

The invention provides a curative effect driving type medical performance and payment system oriented to active medical treatment. Active medical economics and DIKWP (data / information / knowledge / wisdom / intention) five-layer semantic modeling are fused. The system semantically converts diagnosis and treatment behaviors into a semantic bill, constructs a target chain by taking a patient purpose as a core, continuously tracks causal contribution of each content unit to a curative effect, and quantifies a doctor service value by a semantic value increment; therefore, payment according to effects and remuneration according to effects are realized through a fair payment engine, medical insurance and an intelligent contract are compatible, and automatic settlement and dispute arbitration are realized. The system can be used for internet hospitals, chronic disease management and medical insurance payment reform, the medical quality, transparency and fund efficiency are improved, and supervision and policy optimization are supported.

This invention discloses an automatic collection and analysis method for medical quality control indicators in critical care departments. The method includes: establishing a case feature database based on historical monitoring data; constructing and training a quality control indicator model based on the case feature database; establishing a model update database to store newly input cases; periodically inputting data from the model update database into the quality control indicator model; and determining whether the medical quality control indicators in critical care departments are within the acceptable range based on the output of the quality control indicator model display module and providing feedback to relevant personnel.

To provide a warning device for an abnormal position of an endotracheal tube.SOLUTION: A warning device for an abnormal position of an endotracheal tube that monitors accuracy of an installation position of an endotracheal tube includes: a monitoring module 1 for collecting chest X-ray image materials of a patient; an object detection module 2 for detecting an object based on a deep learning model; a position evaluation module 3 for interpreting the position appropriateness of the endotracheal tube based on a result of the object detection module; and a display module 4 for displaying an evaluation result on the position appropriateness of the endotracheal tube and providing a necessary warning as a clinical reference. Thereby, the position of the endotracheal tube is evaluated by an artificial intelligence calculation model using a chest X-ray image captured for the patient. A main objective is to enable clinical medical staff to quickly and accurately determine the appropriateness of the position of the endotracheal tube, reduce the rate of unplanned slipping of the endotracheal tube and the incidence of single-lung ventilation, and improve the medical quality, thereby improving the safety and vital sign stability of the patient.SELECTED DRAWING: Figure 1

The present application relates to the medical care form technical field, propose a kind of medical care intelligent form generation method and system based on multimodal big model, comprising: collecting the physical examination of clinical business system in medical care patient, diagnosis record, diagnosis voice data and other multimodal data;Again, generate physical change sequence by multimodal big model in combination with physical examination data, construct diagnosis feature vector;Subsequently, the vector is mapped to medical quality index system, to generate visual form framework;Then, extract diagnosis record key elements and fill into framework, update form information in combination with diagnosis voice data;Finally, medical care intelligent form adapted to each system is output by multimodal big model, and is synchronized to medical care work terminal.The present application realizes the effective fusion of multimodal medical data, and the generated form can completely reflect the whole picture of diagnosis and treatment and the dynamic evolution of signs, improve the clinical work efficiency and medical quality.

The invention discloses a public institution internal data sharing method based on multi-source data fusion, and relates to the technical field of data sharing. According to the method, four-quadrant accurate division is carried out on multi-source fusion three-medicine structured data on the basis of sensitive risk scores and utility influence tolerance, differential noise adding strategies are matched in a targeted mode, and three kinds of deviation verification of medical quality, medical insurance fund risks and medicine supply and demand results and noise iterative optimization are combined, so that the multi-source fusion three-medicine structured data is obtained. And finally, through block chain intelligent contract encryption sharing, the problems that a traditional differential privacy technology is rigid in noise intensity setting and is difficult to adapt to data of different privacy levels are solved, dynamic balance between privacy protection strength and data availability is realized, meanwhile, the security and credibility of a data sharing process are guaranteed, and the data sharing efficiency is improved. On the premise that the multi-source heterogeneous public institution internal data meets the privacy security requirement, the practical value of supporting core services such as medical quality evaluation, medical insurance supervision and drug supply and demand allocation is reserved to the maximum extent.

The utility model belongs to the field of drainage tubes, and particularly relates to an abdominal cavity drainage tube capable of measuring pressure. A pressure measuring tube is arranged on the side wall of the drainage tube, a blocking piece and a compressible elastic piece are arranged in the pressure measuring tube, the blocking piece can move downwards along the inner wall of the pressure measuring tube, the elastic piece is connected below the blocking piece and connected with a pressure sensor at the lower end of the pressure measuring tube, and the pressure sensor is connected with a pressure value display screen through a wire. Pressure value scales are further arranged in the pressure measuring tube, a small hole is formed in the lower end of the pressure measuring tube, and the drainage tube is provided with a branch for a wire to penetrate out to be connected with a display screen. The intraperitoneal pressure measuring device can directly and accurately measure intraperitoneal pressure change, and provides reliable illness state judgment basis for medical staff. Scales inside the pressure measuring tube facilitate visual observation of pressure changes, the structure is simple, operation is convenient, complex operation and additional equipment are not needed, the workload of medical staff is reduced, the working efficiency is improved, and the medical quality and the rehabilitation effect of a patient can be improved.

The invention discloses a rib fracture intelligent auxiliary decision-making system based on multi-modal fusion, and the system comprises a database construction module which is used for obtaining a multi-dimensional special disease database, and a data processing and fusion module which is used for preprocessing multi-modal data and generating a structured data set. The intelligent detection module constructs a rib fracture intelligent detection network for fracture detection, the risk prediction module is used for constructing a vertical multi-modal network to output a risk probability and an intervention recommendation vector, and the scheme generation module generates an individual intervention path and dynamically updates a risk prediction and intervention scheme in combination with follow-up visit data; according to the method, the vertical multi-modal network for rib fracture is constructed, decision suggestions are given in combination with multi-modal data fusion, the early recognition and early warning capability of fracture of a fracture patient is improved, meanwhile, modeling is performed according to large samples and feature fusion which varies from person to person, the consistency of intervention path recommendation after fracture among different doctors is improved, and the accuracy of intervention path recommendation after fracture is improved. And the medical quality control capability is improved.