176 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 relates to the technical field of medicine quality management, in particular to a medicine quality management method and system based on big data and artificial intelligence, and the method comprises the steps: obtaining the quality influence parameters of a medicine product in the production, transportation and storage processes through a multi-modal data collection device; noise is removed through data cleaning, data dimension reduction is carried out through principal component analysis and an auto-encoder, the analysis efficiency is improved, and the efficiency problem of mass data processing of a traditional method is solved. Time sequence analysis is carried out through the long-short-term memory network, abnormal nodes are recognized in time, manual sampling inspection is replaced, and accurate early warning is achieved. The graph neural network constructs a path tracking model, analyzes the relevance between nodes, traces the quality problem, and reckons the batch of affected drugs. Based on a traceability result, an XGBoost model generates quality risk scores of all links by analyzing historical and real-time data and sets a threshold value, and quality risks are early warned in real time.

The invention provides a medical quality intelligent evaluation and early warning prediction method and system based on a multi-dimensional index, and relates to the technical field of medical quality evaluation, and the method comprises the steps: calculating the fluctuation feature value of a medical quality impact factor, constructing a quality sensitivity scoring model, and carrying out the dynamic sampling to extract a quality feature vector. And performing feature fusion by calculating the clinical association strength and the feature importance score of the feature group to obtain a medical quality score. And performing multi-scale wavelet transform on the medical quality score, extracting a quality evolution feature sequence, calculating a department quality state vector, constructing a quality propagation graph, and identifying a quality control node. And finally, a multi-objective constraint optimization equation is constructed based on the risk propagation prediction sequence, a quality intervention strategy set is solved, and an optimal intervention scheme is screened, so that intelligent assessment, early warning and prediction of medical quality are realized, and the medical quality management level is effectively improved.

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 discloses a patient medical satisfaction evaluation system based on big data, and relates to the technical field of big data analysis. The medical satisfaction evaluation system comprises a data acquisition and processing module, an evaluation index management module, a satisfaction questionnaire survey module, a multi-dimensional evaluation module, a satisfaction grading module and a service quality optimization module. According to the big data-based patient doctor-seeing satisfaction degree evaluation system, through real-time collection and analysis of patient satisfaction degree data including a medical quality evaluation index and a doctor-seeing experience evaluation index, registration waiting time, diagnosis and treatment waiting time and an endemic area environment score are monitored, service bottlenecks and patient dissatisfaction fields are rapidly identified, and the patient satisfaction degree evaluation efficiency is improved. The hospital can adjust resource distribution in time, the overall quality of medical services is evaluated through the patient complaint score and the doctor-patient communication transparency score, effective communication between doctors and patients is promoted, transparency is improved, and therefore the satisfaction degree and credibility of the patients for the medical services are improved.

The invention relates to the technical field of medical report generation, in particular to an intelligent anesthesia record sheet automatic generation system. According to the system, patient information is automatically recognized through the OCR unit, and real-time collection and structured storage of physical sign data, medication records and surgical operation are achieved in combination with a medical equipment interface and a voice recognition technology; time sequence data analysis and multi-source data fusion modeling are adopted to construct a medical event causal relationship graph so as to improve the accuracy of anomaly recognition and patient sign evaluation; based on a time axis construction unit and a structured template engine, an anesthesia record list is automatically analyzed and filled, and the manual input workload is greatly reduced; and finally, in combination with block chain evidence storage and a multi-level encryption technology, the security and non-tampering property of the anesthesia record list are ensured, the reliability of medical data management is improved, the recording, monitoring and storage efficiency of the anesthesia process is comprehensively optimized, and the medical quality and the patient safety are improved.

The invention relates to the technical field of hospital risk assessment, and discloses an index analysis system based on hospital risk assessment, comprising: a dynamic data acquisition module configured with a hospital multi-source heterogeneous data interface, the data acquisition module is used for acquiring medical quality monitoring data, patient safety event data, financial operation data, personnel behavior data and medical equipment operation data in real time. According to the system, the dynamic data acquisition module is arranged, hospital multi-dimensional data is acquired in real time through a multi-source heterogeneous data interface, a comprehensive real-time data basis is provided for risk assessment, the standardization processing module performs data cleaning, format standardization and dimension alignment, a cross-department associated data set is constructed, the data quality and availability are improved, and the risk assessment efficiency is improved. A dynamic analysis unit of the intelligent risk assessment engine calculates indexes in real time based on a sliding time window mechanism, generates a multi-dimensional risk evolution trend map, and can monitor risk changes of different time scales.

The invention discloses an ultrasonic anesthesia positioning method and system based on AI preoperative evaluation and a storage medium, and relates to the technical field of medical assistance. The method comprises the following steps: comprehensively collecting body health data of a patient, including a physical examination result, a laboratory examination result and a medical history, reasoning the body health data of the patient based on an operation risk knowledge graph model, and determining an operation risk level; if the operation risk level is higher than a preset risk level value, the operation risk is evaluated again in combination with past operation case information, and if the operation condition is met, the crowd is judged to be the high-risk crowd of the operation; an ultrasonic image of an in-vivo structure of an operation high-risk crowd is collected by utilizing an ultrasonic technology, the ultrasonic image is recognized by utilizing an image recognition AI algorithm, the relationship between an anatomical position and an adjacent part is determined, and then an anesthesia position is determined. The medical quality and the service level can be improved, and safer and more efficient surgical treatment experience can be brought to patients.

The invention discloses a case data analysis system based on a large model, and the system comprises a data collection module, a preprocessing module, a large model training and optimization module, an analysis module, a visualization and interaction module, a security and privacy protection module, a data preprocessing module with an innovative algorithm, a large model training module with an adaptive learning rate formula, and a disease risk prediction module with an evaluation formula. System functions and performance indexes are clear, equipment and medium requirements are clear, the steps of the method are complete, and high efficiency, accuracy and safety of case data analysis are guaranteed; the case data processing efficiency and accuracy are improved, and powerful support is provided for medical decision making. A large model is accurately analyzed, visual and convenient to check, and a security module guarantees data privacy. The medical quality can be improved, the treatment scheme can be optimized, and the method has important application value.

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 relates to an intelligent and efficient business rule hot loading system and method, and the method comprises the following steps: 1, defining a rule model, 2, building a rule library, building the rule library containing various medical quality control rules according to the actual demands of medical quality control, and updating and expanding the rules in the rule library according to the demands. The method comprises the following steps of: 1, monitoring the change of a rule to adapt to the requirements of different medical scenes, 3, monitoring the change of the rule, 4, dynamically loading a new rule, and 5, caching the rule.

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 discloses a clinical pathway monitoring method and system based on a DRG / DIP payment mode, and the method comprises the steps: determining the disease type of a target patient, and formulating a medical insurance version clinical pathway form for the target patient based on the disease type; determining a diagnosis and treatment item coefficient of each clinical pathway in the medical insurance version clinical pathway form through the historical treatment data, and optimizing the medical insurance version clinical pathway form according to the diagnosis and treatment item coefficients; obtaining related diagnosis and treatment opinions of professional doctors for the optimized medical insurance version clinical pathway form, and determining a final implementation clinical pathway according to the related diagnosis and treatment opinions; and acquiring a path progress monitoring index, a path quality monitoring index and a path cost monitoring index of the final implementation clinical path, and monitoring the medical process of the target patient according to the path progress monitoring index, the path quality monitoring index and the path cost monitoring index. Medical insurance cost index and clinical path management double-index system monitoring is created, hospitals are assisted in controlling unreasonable increase of medical cost, and medical quality is guaranteed.

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 discloses a medical quality safety management system, and belongs to the field of medical management. The medical quality safety management system comprises an adverse event management module which is used for front-line medical personnel to input adverse events and a manager to read the beginning and the end of the adverse events in real time; the data summarization and analysis module is used for reading the indexes in the events recorded by the adverse event management module, respectively counting the indexes to form a form, and analyzing and calculating the incentive proportion of the adverse events by using a K-means clustering analysis algorithm; and the data statistics module is used for generating an adverse event analysis report by utilizing a CART model according to an analysis result of the data summarization analysis module. According to the technical scheme, the medical staff can input the adverse events by logging in the account number, the manager can read the beginning and the end of the adverse events in real time by logging in the account number, analysis is carried out through the clustering analysis algorithm and the CART model, the report is generated, and therefore the workload of the medical staff is relieved.

The invention relates to the field of medical informatization, in particular to an intelligent medical guidance system. The intelligent medical guide system integrates multiple modules of information acquisition, data processing, position positioning, path planning, information pushing, human-computer interaction and the like into a whole, and the whole-process intelligentization of patient identification, positioning, navigation and information service is realized. The system collects a facial image and card type information of a patient and generates an identity recognition result; planning a navigation path according to the position coordinates and the hospital structure; and personalized treatment reminding is pushed. The human-computer interaction interface displays paths and prompts and receives a patient instruction. The system improves hospital operation efficiency and medical quality, innovates man-machine interaction and data security, improves medical experience, provides powerful support for hospital management and medical service quality improvement, and realizes organic combination of accurate and personalized medical service and efficient hospital management.

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.