157 results about "Patients symptoms" patented technology

The invention discloses a medical intelligent decision-making method based on Deepseek and a time sequence causal knowledge graph, and the method comprises the following steps: 1, constructing an initial static medical knowledge graph, and generating a dynamic time sequence causal knowledge graph; 2, finely adjusting and training the DeepSeek model to enable the DeepSeek model to adapt to the medical field; and step 3, receiving and analyzing the text uploaded by the patient, performing intelligent triage and disease risk prediction, and realizing accurate matching of patient symptoms and target departments and intelligent prediction and early warning of potential diseases. The method aims at providing accurate triage and disease risk prediction for patients, constructing a scientific and efficient medical intelligent decision-making mechanism and optimizing medical resource allocation.

The invention discloses an AI-based traditional Chinese medicine dialectical auxiliary diagnosis method and system and a medium thereof, and relates to the technical field of intelligent auxiliary diagnosis, and the method comprises the steps: collecting physiological monitoring data, tongue condition data and pulse condition signals of a patient, obtaining patient symptom information, mapping the patient symptom information to a preset traditional Chinese medicine pathogenesis classification model, and generating an initial symptom feature vector; calling a knowledge base containing traditional Chinese medicine prescription rules and compatibility medication taboo, performing data space-time alignment processing and dynamic combination calculation on the initial symptom feature vector, and generating a personalized candidate prescription set conforming to the traditional Chinese medicine compatibility taboo; according to dynamic combination calculation, a solution algorithm based on a constraint satisfaction problem is adopted, and multi-dimensional association rule matching is carried out in combination with the mapping relation between syndromes and modern medical symptoms; and outputting a prescription medication recommendation scheme containing traditional Chinese medicine compatibility, dosage and decoction methods based on the candidate prescription set. The method supports the formulation of an accurate personalized diagnosis and treatment scheme, and meets the requirements of high-quality traditional Chinese medicine personalized auxiliary diagnosis and treatment.

The invention discloses an intelligent emergency pre-examination triage system based on big data, and relates to the technical field of data analysis, the emergency pre-examination triage system comprises a data acquisition module, an evaluation index extraction module, an intelligent analysis module, a triage evaluation module, a triage decision module, a feedback interaction module and a patient information management module, all the modules are connected through electric signals. According to the intelligent emergency treatment pre-examination triage system based on the big data, the comprehensive evaluation model and the triage evaluation coefficient are integrated, and the symptoms of the patient are evaluated quickly and accurately, so that the triage level and the recommended treatment department are quickly determined, the emergency treatment efficiency is remarkably improved, the waiting time of the patient is shortened, and the patient experience is improved. According to the method, medical resources are configured more reasonably, meanwhile, more comprehensive patient portraits can be provided for medical staff according to multi-dimensional information such as medical history and examination results of patients, and making of more accurate diagnosis and treatment decisions is facilitated.

The invention discloses a symptom management and prognosis evaluation method and system for a tumor radiotherapy patient, and relates to the technical field of tumor radiotherapy patient management.The method comprises the steps that basic information of the tumor radiotherapy patient is obtained, prognosis evaluation is conducted according to the basic information, a first result is generated, and the risk category is judged according to the first result; obtaining symptom information of a tumor radiotherapy patient, evaluating the symptom severity according to the symptom information, and generating a second result; and performing symptom management according to the second result and the risk category, generating prognosis data, and updating the first result through the prognosis data. According to the invention, by fusing the dual contrast learning network and the dual-path decision tree integrated model, the dynamic association analysis and adaptive feature enhancement of the multi-dimensional medical data of the tumor radiotherapy patient are realized, so that a precise prognosis evaluation and a personalized symptom management scheme are provided for clinical practice.

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

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention provides a medical agent middleware-based full-specialized collaborative diagnosis and treatment method and system, and relates to the technical field of medical agents, and the method comprises the steps: building a basic medical knowledge graph through reinforcement learning, converting expert diagnosis and treatment experience into diagnosis and treatment probability distribution through a personalized treatment decision model, and forming an enhanced medical knowledge graph; and calculating a matching probability with a disease based on the symptom feature vector of the patient to generate a preliminary diagnosis decision, and selecting and adjusting a treatment scheme through reinforcement learning. According to the invention, general and specialized collaborative diagnosis and treatment can be realized, and medical decision accuracy and diagnosis and treatment efficiency are improved.

The invention relates to the field of traditional Chinese medicine clinical auxiliary diagnosis, and discloses an acupuncture point recommendation method, an acupuncture point recommendation model obtaining method, an acupuncture point recommendation model obtaining device, acupuncture point recommendation equipment and a medium, and the acupuncture point recommendation model obtaining method comprises the following steps: collecting patient medical record data to form a medical record data sample set; preprocessing the medical record data sample set to obtain a standard medical record data sample set; inputting the standard medical record data sample set into a preset language model, performing preliminary training on the language model based on the standard medical record data sample set, so that the language model establishes a semantic mapping relationship between the patient symptom information and the acupuncture points, and performing parameter fine tuning and strategy optimization on the preliminarily trained language model in sequence, obtaining an acupuncture point recommendation model; according to the acupuncture point recommendation model obtaining method, the accuracy and individual adaptability of acupuncture clinical treatment are improved, the diagnosis and treatment efficiency is optimized, and a scientific and systematic treatment scheme is provided for a patient.

The invention relates to the technical field of intelligent inquiry, in particular to a hospital intelligent inquiry method and system based on natural language processing, and the method comprises the following steps: based on the input text of a patient, analyzing the relation between text content and medical terms, identifying key symptom description, carrying out term adaptation, correcting context errors, and obtaining a context adjustment result. According to the method, the adaptation degree and correlation of medical terms are evaluated through accurately analyzed data, it is ensured that deduced medical suggestions are highly matched with patient symptoms, in the aspect of physical parameter analysis, through real-time monitoring and fluctuation analysis of physiological indexes of patients, health risks can be recognized earlier, medical responses can be adjusted in time, and the medical treatment effect is improved. Not only is the workload of medical staff relieved, but also the individuation level and efficiency of medical services are improved, and remarkable advantages are provided for accelerating the inquiry process and accurately positioning the health problem.

The invention discloses a multi-source confidence perception insomnia intelligent prescription recommendation method, a medium and equipment, and aims to solve the problem of low recommendation accuracy caused by insufficient multi-source heterogeneous data fusion, single feature representation and missing prediction confidence in the prior art. The method comprises the following steps: coding patient symptoms into a sparse binary matrix; extracting a symptom-traditional Chinese medicine prescription matrix and a symptom-symptom type matrix from the traditional Chinese medicine knowledge map; generating symptom-guided traditional Chinese medicine features and symptom type features through a double-layer graph convolutional neural network; calculating the prediction distribution probability of the traditional Chinese medicine prescriptions and syndromes; based on an energy function, quantifying uncertainty scores of the two; the multi-source probability is dynamically weighted and fused to improve the weight of a high-confidence result; and finally, outputting recommended prescriptions and syndrome type sequences. The accuracy and interpretability of insomnia prescription recommendation are remarkably improved through isomeric map modeling and multi-source confidence fusion, and the method is suitable for a traditional Chinese medicine clinical auxiliary decision making system.

The invention relates to the technical field of medical information, in particular to a tumor patient adverse drug reaction intelligent follow-up visit system based on an internet hospital, which comprises a patient file module for acquiring medical data, identifying bad data and recording the bad data in a medical record file to form an abnormal symptom report; the intelligent analysis module is used for comprehensively analyzing the abnormal symptom report by utilizing a trained machine learning model and outputting analysis data; according to the system and the method, continuous monitoring and active early warning of extrahospital adverse reactions of tumor patients are realized, the timeliness of finding the adverse reactions is remarkably improved, intervention measures can be taken before symptoms of the patients deteriorate, and the treatment safety of the patients is improved. And secondly, through intelligent analysis and grading, the burden of manually screening the patient condition by medical staff is greatly reduced, the system automatically finds abnormity from massive follow-up visit data, and the efficiency and accuracy of follow-up visit management are improved.

A system and method for patient diagnosis and implementation of orthodontic apparatuses and treatments. The patient can be located remotely from a remote treatment system, thereby allowing for automatic diagnosis and treatment of the patient without interaction with a medical professional or associated staff. A neural network of an artificial intelligence engine (AI engine) of the remote treatment system can utilize images and other data captured by a patient, as well as patient responses to questionnaires, to derive information regarding the patient's symptoms, and the severity of those symptoms. The AI engine can further be adapted, including via use of historical information regarding a plurality of patients and information from the patient, to obtain a diagnosis and treatment recommendation. The AI engine can further be configured to determine, based on analysis of patient provided images, an appropriately sized orthopedic apparatus, which can be sent to the patient for implementation.

The invention discloses a tear total IGE detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring ocular surface images, physiological parameters and environmental allergen data, and acquiring historical medical data from a hospital information system; preprocessing and feature extraction are performed, patient symptom description is analyzed in combination with a natural language processing technology, and related image block features are recognized; adjusting a space-time attention weight based on frequency domain analysis and transfer learning, inputting a lightweight Transform model, and predicting a tear total IgE concentration and an allergy risk score; whether a trace tear sampling program is started or not is judged, and the actually measured concentration of total tear IgE is detected through a disposable micro-fluidic chip; and integrating all information by using a Bayesian adaptive filtering algorithm to determine the total tear IgE level. By implementing the method provided by the invention, the sampling amount is extremely small, the detection time is short, the environmental interference can be dynamically corrected, and the absolute concentration value is output, so that the dual requirements of clinical precise diagnosis and treatment and home monitoring are met.

The invention relates to the technical field of diagnosis and treatment, in particular to a traditional Chinese and western medicine combined intelligent decision diagnosis and treatment method, system and equipment. According to the method, firstly, the symptoms of a patient are coded and converted into feature vectors, then vector matching is carried out in a pre-constructed traditional Chinese and western medicine combined knowledge graph, applicable diagnosis and treatment information is screened out by calculating the similarity and setting a threshold value, and a preliminary scheme is formed; reading individual information of the patient, and analyzing in combination with the medication taboo rule base to obtain personalized treatment parameters; and finally, performing optimization adjustment based on the initial scheme and the personalized parameters to generate a final diagnosis and treatment suggestion. Deep fusion of traditional Chinese and western medicine knowledge is realized, a diagnosis and treatment scheme can be dynamically adjusted according to individual features of a patient, and the accuracy and effectiveness of diagnosis and treatment are improved.

An application for tracking cancer therapy symptom triggers stored on non-transitory computer readable media including an input module for inputting variables from a user in electronic communication with an output variable module, an analysis module for analyzing input variables and output variables, and an output module for presenting results to the user. A method of tracking triggers of symptoms in cancer patients, by a user inputting data about nutrition, medication, lifestyle, symptoms due to cancer therapy, pain, and user defined metrics in an application stored on non-transitory computer readable media, performing an analysis on the data, and outputting a result from the data identifying daily activities that effect the user's symptoms due to cancer therapy. A method of adjusting treatment of a cancer patient.

By providing a nursing care plan creation support system that enables even new nurses to easily create complete nursing care plans, searches for nursing care plans for similar cases from accumulated nursing care plan records, and enables automatic or semi-automatic creation of nursing care plans using additional learning in a large-scale language model, it becomes easier to create high-quality nursing care plans in a short amount of time. [Solution] The nursing care plan creation support system comprises a nursing diagnosis classification recording means, a nursing outcome classification recording means, a nursing intervention classification recording means, a nursing outcome classification feature vector recording means, a nursing intervention classification feature vector recording means, a nursing diagnosis item selecting means, a nursing outcome item selecting means, and a nursing intervention item selecting means. The nursing diagnosis classification feature vector recording means converts any portion of each classification record document into a feature vector and records it, and the nursing diagnosis item selecting means inputs the patient's symptoms and findings and a group of items of the content of each selected classification into a large-scale language model and selects items appropriate to the patient's symptoms and findings.

The invention relates to a clinical symptom nursing strategy intelligent recommendation system and method based on artificial intelligence, and the method combines historical clinical symptoms and nursing big data to construct a corresponding nursing strategy intelligent recommendation model, thereby enabling the nursing strategy intelligent recommendation model to recognize the feature relationship between different symptom features and corresponding nursing strategies. And enabling the model to recognize the symptom characteristics of a clinical patient and intelligently recommend a corresponding nursing strategy. The system can also combine model output and nurse feedback, fuse and recommend a clinical symptom nursing strategy most related to patient symptoms, so that nurses can make preliminary assessment and deal with the appearing symptoms to confirm nursing problems, sort critical and urgent conditions, develop nursing measures, highlight nursing key points and improve the nursing efficiency. And potential complications can be prevented or treated in time. In clinical nursing work, clinical nurses can be effectively guided to perform judgment thinking and carry out predictive nursing, so that the nursing work is homogenized, and the nursing quality and safety are improved.

The invention discloses an allergic rhinitis diagnosis and allergen traceability system based on dynamic text guidance, and belongs to the technical field of intelligent medical treatment. According to the method, the problems that in the prior art, patient description is inaccurate, allergens are various in variety and have differences, and allergens are difficult to accurately determine and trace are solved, the initial situational sub-graph is verified by introducing periodic features of the patient, the causal contribution degree of the initial situational sub-graph is evaluated through a Bayesian reasoning algorithm, and a mode association result library is formed; according to the method, the crossing of the allergic rhinitis diagnosis and tracing from the traditional static judgment to the dynamic accurate inference is realized, and a personalized and scientific allergen avoidance scheme is provided for patients; through similarity retrieval, environmental information injection verification and calculation of a mode goodness-of-fit score, under the condition that patient symptoms are in multi-factor mixing, a mixed causal graph is created through a graph fusion technology, and the mode goodness-of-fit score is calculated again, so that the accuracy of obtaining an allergen traceability result in practical application is ensured.

The invention discloses a dementia intervention scheme recommendation method and system based on a knowledge graph, and relates to the technical field of intelligent medical treatment and artificial intelligence. And constructing a dementia intervention knowledge graph containing patients, symptoms, intervention measures and environmental elements. And performing intelligent semantic reasoning on the short-term state, the long-term behavior mode and the nursing environment of the patient by fusing multi-level reasoning and situational reasoning, generating a candidate intervention scheme matched with the current state of the patient, and outputting a personalized recommendation result. In the intervention implementation process, the knowledge graph association relation is dynamically updated based on patient behavior feedback and symptom changes, and continuous optimization and self-adaptive adjustment of an intervention scheme are achieved. According to the method, the individuation level, long-term adaptability and safety of dementia intervention recommendation are improved, and the method is suitable for clinical and long-term nursing scenes.

The invention discloses an intelligent hospital guide system, which belongs to the field of medical information and is characterized in that medical terms are converted into uncomfortable symptom description which is easy to recognize by a patient by deeply analyzing characteristics of common diseases and combining a natural language processing technology, so that the user friendliness of the system is improved; approximate Bayes inference is carried out by using cosine similarity, an improved C4.5 decision tree algorithm is combined, a decision tree is dynamically constructed to generate accurate question and answer logic, accurate matching and department recommendation of patient symptoms are realized, and the accuracy and efficiency of hospital guidance can be improved; in conclusion, the system provided by the invention effectively solves the defects of traditional manual hospital guide, improves the registration accuracy, and promotes the overall improvement of the medical service quality by reducing the hospital guide cost and improving the working efficiency.

The invention discloses a liver, gall and pancreas patient symptom evaluation system based on multi-dimensional data, and relates to the technical field of liver, gall and pancreas symptom evaluation. The visual management platform is in communication connection with a data acquisition module, an abnormal symptom classification module, a symptom risk analysis module, a symptom risk classification module and a report generation module, and all the modules are in electric signal connection; the data acquisition module is used for collecting and preprocessing multi-dimensional index data of a liver function index, a biliary system index and a pancreas index of a patient. By integrating the multi-dimensional data of the liver function index, the biliary system index and the pancreas index, the health condition of the liver, gall and pancreas patient can be more comprehensively reflected, compared with a traditional single-dimensional evaluation method, the abnormal symptoms of the patient can be more accurately recognized, the possibility of misdiagnosis and missed diagnosis is reduced, and the diagnosis accuracy is improved. And personalized symptom risk assessment can be carried out according to specific data of each patient, so that the medical decision is more accurate.

This invention provides a method and system for predicting diabetes by integrating knowledge graphs and deep learning. The method constructs an entity vector library of physical examination data, and obtains vector representations of patient physical examination data based on this library, resulting in physical examination data vectors. It then constructs a diabetes domain knowledge graph; based on this knowledge graph, it extracts entities and relationships from patient symptom descriptions and represents them as vectors, obtaining symptom description vectors, and thus a matrix representing the patient's condition; finally, it integrates a self-attention mechanism and a convolutional neural network to construct a classification prediction model, which yields the prediction results. Compared with existing methods, this invention, by simultaneously considering patient physical examination data and symptom descriptions and based on a dual-driven approach of knowledge and data, achieves higher accuracy and interpretability.

A system and method for patient diagnosis and implementation of orthodontic apparatuses and treatments. The patient can be located remotely from a remote treatment system, thereby allowing for automatic diagnosis and treatment of the patient without interaction with a medical professional or associated staff. A neural network of an artificial intelligence engine (Al engine) of the remote treatment system can utilize images and other data captured by a patient, as well as patient responses to questionnaires, to derive information regarding the patient's symptoms, and the severity of those symptoms. The Al engine can further be adapted, including via use of historical information regarding a plurality of patients and information from the patient, to obtain a diagnosis and treatment recommendation. The Al engine can further be configured to determine, based on analysis of patient provided images, an appropriately sized orthopedic apparatus, which can be sent to the patient for implementation.

The invention relates to the technical field of medical instruments, in particular to a multi-mode biological feedback ear vagus nerve self-adaptive stimulation device and method.The device comprises a host, a stimulation electrode, a signal collection bracelet and an electroencephalogram collection module, and the host comprises core hardware configuration and a power management system; the signal acquisition bracelet comprises an ECG module, a PPG module, a GSR module, a six-axis acceleration sensor and the like. The ear vagus nerve stimulation parameter can be dynamically adjusted through real-time analysis of multi-mode data such as electrocardio, electroencephalogram, heart rate variability, respiration-heart rate synchronism and emotional state, personalized precise treatment is achieved, meanwhile, the wearing comfort and the durability of the device are improved, and the application range is wide. The traditional Chinese medicine composition can be used for auxiliary treatment of various diseases such as inflammatory bowel disease, epilepsy, chronic insomnia, depression, chronic pain, anxiety disorder, post-traumatic stress disorder and autonomic nerve dysfunction, and the functions of a nervous system are adjusted and the symptoms of a patient are improved by accurately stimulating the vagus nerve in the auricular concha area.

The invention discloses a traditional Chinese medicine prescription diagnosis decision method and system, and relates to the technical field of decision diagnosis, and the method comprises the steps: collecting the symptom description data, geographic position data and seasonal climate data of a patient, carrying out the standardization processing of the symptom description data, and generating a symptom feature vector; performing time sequence analysis on the symptom feature vector by using a recurrent neural network, extracting a symptom evolution feature matrix, calculating an environmental influence weight based on geographic position data and seasonal climate data, and performing feature fusion on the symptom evolution feature matrix and an environmental influence weight vector to obtain a comprehensive feature vector; inputting the comprehensive feature vector into a pre-trained BERT model to obtain a semantic feature vector, and calculating a syndrome matching degree; and calculating the prescription probability according to the symptom evolution vector, the environmental influence weight and the syndrome matching degree, and outputting the probability distribution of each prescription to obtain a prescription decision result. The accuracy and objectivity of diagnosis decision are improved, and standardization and intelligence of the traditional Chinese medicine diagnosis and treatment process are further achieved.

The invention belongs to the technical field of outpatient service management, and discloses a generative AI-based outpatient service management method and system, and the method comprises the steps: obtaining the basic information, symptom description and historical medical record data of a patient when the patient sees a doctor through an AI outpatient service management system, and carrying out the semantic understanding and preliminary triage prediction of the symptom of the patient through a generative AI model, and generating possible disease types and corresponding department suggestions. According to the invention, semantic understanding and triage prediction are carried out on the symptoms of the patient through the generative AI, and intelligentization of the appointment process is realized. Patients do not need to manually select cumbersomely, and the system automatically generates department suggestions, so that the appointment time is greatly shortened, and the convenience and efficiency of appointment of the patients are improved. The constructed patient priority evaluation model and the department resource state model enable the calculation of the reservation time to be more scientific and reasonable, coordinate the time arrangement of the reservation and the preliminary diagnosis stage, effectively avoid reservation conflicts and long-time waiting, and improve the overall efficiency and accuracy of outpatient reservation.

The invention relates to the technical field of multi-source medical data integration, in particular to an integration method and system for integrating multi-source medical data sets. Comprising the following steps: S1, collecting multi-source medical data into an LIMS system, and associating the multi-source medical data according to a patient identifier; s2, extracting historical patient diagnosis symptoms according to the patient identifier to establish a patient symptom model, inputting associated medical data after association according to the historical patient diagnosis symptoms, and outputting a symptom related report through the patient symptom model; the medical knowledge graph is extracted from the multi-source medical data, accuracy analysis is carried out on disease related reports, correction is carried out according to different types of disease related reports, the accuracy of diagnosis reports is greatly improved, then the corrected reports are integrated into a disease diagnosis model, training and optimization are carried out through a machine learning algorithm, and the accuracy of the diagnosis reports is improved. The model can learn a large amount of medical knowledge and diagnosis modes.

The invention discloses an intelligent auxiliary inquiry medical large model training method and system, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the following steps: S1, collecting medical inquiry data; s2, data annotation and cleaning; s3, constructing a medical large model; s4, optimizing the medical pre-training model; s5, task specific fine tuning; and S6, designing an inquiry system. The model trained by the intelligent auxiliary inquiry medical large model training method can efficiently capture important information when a large number of complex medical languages are processed, and the ability of understanding and reasoning the symptoms of a patient is improved; according to the invention, intelligent on-time suggestion generation can be carried out based on real-time data, questioning and adjustment can be carried out according to symptom information provided by a patient, and the doctor seeing efficiency and accuracy are greatly improved.

The invention relates to the technical field of tumor treatment, in particular to a tumor patient symptom group collaborative management system, comprising a data acquisition module used for acquiring patient information and environment information through an intelligent device; the feature extraction module is used for data acquisition of the data acquisition module, and the feature extraction module performs feature extraction on the patient information and the environment information to obtain time domain features, frequency domain features and nonlinear features corresponding to symptoms; the evaluation platform identifies the core symptom group of the patient according to the time domain feature, the frequency domain feature and the nonlinear feature and calculates a total load index; the evaluation platform also performs symptom deterioration early warning; the collaboration platform is used for storing a disease knowledge base and identifying and obtaining a corresponding multi-mode intervention scheme according to the condition of a patient; and the patient terminal is used for pushing the multi-mode intervention scheme to the patient. According to the invention, the symptom group of the tumor patient can be managed as a whole, a personalized and real-time intervention scheme is provided for the patient, and the treatment effect is improved.

The invention discloses a Chinese patent medicine for treating lung nodules and application thereof, and belongs to the technical field of Chinese patent medicines. The Chinese patent medicine prescription comprises 3-5 parts of angelica sinensis, 4-7 parts of medicated leaven, 4-5 parts of fructus aurantii, 1-4 parts of radix curcumae, 1-2 parts of bulbus fritillariae cirrhosae, 1-2 parts of ligusticum chuanxiong hort and 0.5-1.5 parts of scalded leech. The Chinese patent medicine can adjust the formula according to different nodule types of patients, selects the formula better for the symptoms of the patients, can accelerate the rehabilitation of the patients, strengthen the body resistance, reinforce qi and nourish yin, promote blood circulation to remove blood stasis, clear away lung-heat and remove stasis, clear away heat and toxic materials, sooth the liver and regulate qi, reduce phlegm and dissolve phlegm, relieve pain and remove stasis, treat both symptoms and root causes, strengthen the body resistance and remove stasis to treat pulmonary nodules, and can improve the immunity of human bodies. The maximum nodule length diameter can be reduced, the nodule malignant risk probability is reduced, and the development of lung nodules can be safely and effectively inhibited.