194 results about "Disease course" patented technology

The invention provides a patient pathological data analysis and evaluation method for clinical nursing, and relates to the technical field of nursing informatization. Comprising the steps of collecting biochemical and pathological data of a patient and standardizing units and reference values, analyzing the dynamic trend of detection values and classifying change directions and amplitudes, matching pathological labels and establishing association with nursing items, comparing differences between nursing records and the pathological data and establishing a transverse index relationship, and judging whether the nursing task difference exceeds a threshold value or not and adjusting a nursing path generation scheme. By integrating multi-source pathological data and standardizing units and reference intervals, data consistency and comparability are ensured, the index fluctuation trend is dynamically tracked, the change direction and amplitude are quantitatively analyzed, disease course evolution is accurately captured, direct mapping between pathological categories and nursing items is established, logic binding is clear, intervention precision is improved, and the method is suitable for clinical application. Differences between nursing records and pathological data are transversely compared, nursing dynamic adaptation is optimized, a path scheme is rapidly adjusted through threshold judgment, and the lagging or misjudgment risk is reduced.

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

The invention provides a medical examination data analysis system and method based on artificial intelligence. The method comprises the steps of obtaining a time sequence data matrix of multiple examination indexes of a target patient at different time nodes through medical examination data of the target patient; labeling pathological labels of various examination indexes in the medical examination data, and determining medical semantic association features among different examination indexes of the target patient through the pathological labels; according to the medical semantic association features and the time sequence data matrix, determining a time sequence association relationship of different inspection indexes of the target patient on a pathological level, and generating a fusion feature vector of the health state of the target patient according to the time sequence association relationship; and an abnormal evolution feature of the current health state of the target patient is obtained by combining an abnormal detection model with the fusion feature vector, and then an auxiliary analysis result of the pathological risk of the target patient is output to medical personnel. By adopting the scheme of the invention, the dynamic change trend analysis of the disease course risk state of the patient can be realized based on the medical semantic perception ability.

The invention relates to a chronic heart failure dynamic course evolution prediction model construction method. Comprising the following steps: uniformly mapping continuous variables including LVEF and heart rate and event variables into a time trajectory frame through an event alignment and time domain nesting strategy; using a local change rate algorithm to identify inflection points including states before acute deterioration and intervention reactions in the course of disease of each patient; constructing a state fragment set for supporting hierarchical modeling in an evolution stage; a bidirectional fusion method of trajectory clustering and medical knowledge embedding is used to construct a state space with clinical interpretability including a compensation period, edge decompensation and an acute deterioration period; taking the trajectory vector as a main input, taking a state space as a prediction target, and introducing a dual-channel structure; predicting a future path based on the current state; the disease course track change of early medication / non-hospitalization / treatment scheme change is simulated; a doctor is supported to deduce a result; the change of the output state is analyzed through perturbation of the current trajectory, and key variables are found out.

The invention discloses an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

The invention relates to the technical field of medical image processing and artificial intelligence, and discloses an artificial intelligence-based retinochoroid disease course structure change monitoring system, which comprises an image acquisition unit, a feature extraction unit, a lesion segmentation unit and the like. The image acquisition unit acquires retina optical coherence tomography sequence image data and fundus color image data; the feature extraction unit extracts a retina choroidal structure feature tensor through a three-dimensional convolutional neural network; the lesion segmentation unit outputs a lesion area probability distribution diagram by using a U-Net segmentation network; the time sequence alignment unit is used for registering the multi-time-point images to generate a displacement change matrix; the dynamic analysis unit extracts related indexes; an abnormal scoring unit constructs a disease course progress score; the decision grading unit outputs disease course stage classification labels; the multi-modal fusion unit fuses the multi-modal features; the report generation unit generates a structured disease course monitoring report. And favorable support is provided for diagnosis and treatment of ophthalmic diseases and illness state tracking.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention discloses an electronic medical record data synthesis method based on semantic processing, and relates to the technical field of medical informatization, and the method comprises the following steps: S1, constructing a probabilistic medical knowledge graph; s2, generating a semantic representation vector; s3, constructing a multi-dimensional dynamic space-time atlas; s4, generating a discrete personalized disease course event sequence with space-time coordinates; s5, taking the discrete personalized disease course event sequence and the corresponding medical entity semantic representation vector as condition input, guiding the improved TSDiff model to execute an iterative denoising process, and outputting a multi-dimensional random disease course trajectory; s6, forming multi-modal electronic medical record data; and S7, performing multi-dimensional quality evaluation on the multi-modal electronic medical record data. According to the method, the limitations of logic inconsistency, modal splitting and model capability solidification in a traditional synthesis method are overcome, and an efficient and accurate solution is provided.

The invention relates to the technical field of intelligent recommendation, in particular to an intelligent inquiry recommendation method and system, and the method comprises the following steps: extracting text organ word frequency features through TF-IDF, generating a matching degree matrix through cosine similarity, screening core features, analyzing positioning parameters through DICOM, constructing a co-occurrence matrix through collaborative filtering, screening associated feature pairs, indexing a recommendation library, and predicting a symptom path through LSTM. And generating a time sequence weight feature set and a PageRank iterative sorting recommendation table. According to the method, a multi-modal association system is constructed by fusing text features and image parameters, semantic tags and space coordinates are combined to filter and screen organ domain features in a collaborative manner, the matching precision of symptoms and resources is improved, a time sequence weight reconstruction model is adopted to capture symptom evolution features, and hidden state transition is adopted to enhance the disease course prediction capability; a three-dimensional decision model is constructed through feature node sorting, time, space and feature dimension unification is achieved, a personalized diagnosis and treatment scheme is optimized by fusing multi-dimensional features and dynamic weights, and the credibility and clinical applicability of a recommendation result are improved.

The invention provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

The embodiment of the invention aims to provide a control method and system for a self-adaptive electric pulse generating device. The method comprises the following steps: acquiring inflammation-related data of a user; according to the inflammation related data, extracting a biological feature vector of the user through a feature extraction module, and according to the biological feature vector, determining a disease course classification of the user through a disease course classification module, according to the biological feature vectors and the disease course classification, corresponding electric pulse parameters are determined through a parameter generation module; and according to the electric pulse parameters, applying a corresponding wide-frequency-domain electric pulse stimulation scheme to the electric stimulation applying part of the user through the self-adaptive electric pulse generating device. Through data fusion and closed-loop optimization, precise personalization and dynamic adaptability of parameter generation are realized, and the electrical pulse parameter configuration scheme is promoted to span from a static template to an intelligent dynamic mode.

The invention relates to a nephrotic patient health management method and system based on a neural network prediction model, and the method comprises the steps: obtaining the multi-dimensional health data of a target nephrotic patient at each stage, constructing a time sequence feature matrix to extract the change trend of the whole course of disease, and dividing the course of disease into a disease alleviation stage and a disease deterioration stage; analyzing the health data of the two stages to obtain an improvement factor and a deterioration factor; outputting a renal function state prediction result by using a neural network prediction model fusing an attention mechanism and a knowledge graph, and generating an initial health management scheme by combining the result, a preset health management rule base and patient preferences; and optimizing the initial scheme based on the improvement factor, the deterioration factor and a reinforcement learning algorithm to obtain an optimized health management scheme. Complete-cycle closed-loop management of data acquisition, trend analysis, factor identification, prediction and early warning, scheme generation and dynamic optimization of nephropathy patients is realized, and the accuracy and individuation level of nephropathy health management are improved.

A method, a system, and a non-transitory computer-readable medium provides an interactive patient-level data visualization and analysis tool that illustrates a patient's health trajectory across multiple organ systems. Data from an electronic medical record system and one or more research databases are integrated into an analytics platform. A visualization tool plots the patient's health trajectory and overlays data from an entire user-defined disease cohort as a reference group to visualize a disease course of the patient compared to courses of other patients, with a same disease, selected by a user.

The invention relates to a lumbar vertebra protection method and system and a medium, and relates to the technical field of intelligent wearing. According to the scheme, personal basic information and past medical history of a patient are collected, the personal basic information comprises the age, the gender, the height, the weight and the BMI value of the patient, and the past medical history comprises past disease types and current disease courses; on the basis of the personal basic information and the past medical history, a lumbar vertebra supporting strength dynamic adjusting strategy is obtained, and the lumbar vertebra supporting strength dynamic adjusting strategy comprises a control strategy for supporting strength of the patient in all activity states, a control strategy for a supporting mode and recognized bad postures on the basis of the collected electromyographic signals of the patient; and controlling the waist support protection device based on the obtained dynamic adjustment strategy of the lumbar support strength, wherein the control comprises the control of the support strength, the control of the support mode and the early warning control of bad postures. Compared with the prior art, more targeted support strength and support mode control strategies for the patient can be obtained.

The invention relates to the technical field of medical informatization, and discloses a team construction method and system for whole course case management, and the method comprises the steps: integrating medical data and non-medical Internet of Things data through a cross-domain data fusion engine to generate a patient behavior map, carrying out the reverse deduction of wearable equipment data as an inducement based on a reverse reasoning algorithm, and triggering hierarchical intervention, detachable combination and dynamic pricing of points are realized by using a block chain smart contract; according to the system, the data island limitation of traditional medical monitoring is avoided, the risk inducement can be reversely locked before the physiological indexes are abnormal through the implicit association between the smart home power utilization frequency and the medicine use record, accurate early warning is achieved, and the safety of the system is improved through multi-modal data collaborative analysis and deep coupling of sandbox rehearsal decision and the smart contract. And the technical jump from passive treatment to active health management is realized.

The invention relates to the technical field of disease course trend modeling, in particular to a subarachnoid hemorrhage disease course trend modeling system fusing multi-source data. Four kinds of signals of intracranial pressure, blood flow velocity, cerebrospinal fluid pressure and electroencephalogram of a monitored object are synchronously collected, an instantaneous phase is extracted through Hilbert transform, a time window is adaptively adjusted according to the brain blood vessel conduction delay characteristic of an individual, and phase locking indexes among three pairs of signals are calculated. A multivariate coupled oscillator model is established, phase track topology invariant features are extracted, and comprehensive trend indexes are generated through tensor fusion. An individualized four-dimensional phase entropy baseline mode is established, and a double-layer early warning mechanism is adopted: when second derivative continuous symbol overturning occurs in all three phase locking indexes, early warning is directly performed, and when any two phase locking indexes are overturned, a trend index needs to be synthesized for confirmation. And predicting a state level, a trend level and an expected evolution trajectory based on a support vector regression model. According to the invention, precise disease course prediction and early warning are realized, and a basis is provided for clinical decision making.

The invention relates to the technical field of medical monitoring, in particular to an intelligent monitoring and health management system for liver, gall and pancreas postoperative drainage fluid, which comprises a data acquisition module for acquiring core specific indexes of liver, gall and pancreas special drainage fluid, visual images of the drainage fluid, dynamic flow and physiological data of a patient; the intelligent analysis module constructs a hepatobiliary pancreatic postoperative exclusive multi-modal model, deeply couples postoperative disease course time sequence characteristics to identify risks at different stages after the operation, and triggers risk assessment when the operation is abnormal; the early warning module is provided with a light, medium and heavy three-level mechanism, and carries out resource adaptation type grading accurate pushing to a responsible physician and a nurse station terminal in combination with a medical care real-time load and a spatial distance. The health management module fuses individual features of the patient and real-time monitoring data, and constructs a personalized rehabilitation scheme in combination with rehabilitation feedback of the patient; and the data synchronization module transmits model update parameters through an encryption channel, and perfects the complete-cycle health archive of the patient. Therefore, the problems that in the prior art, an early warning mechanism is rigid, and data security and full-period management are insufficient are solved.

The invention discloses an intelligent adverse drug reaction acquisition system and method based on clinical practice, and the method comprises the steps: obtaining a medication record, a disease course text and a nursing record of a patient, and an inspection index; s2, performing identification and relation extraction on the data acquired in the S1, and constructing a time-sequenced knowledge graph of medicine-indications-test abnormality-clinical bad symptoms; training a Transform prediction model through a time sequence knowledge graph, complementing a knowledge graph relationship, and predicting a missing relationship and an entity; s3, monitoring the change of physiological indexes of the patient after medication in real time, performing dual-channel early warning, adding the detected adverse reaction into the prediction model in S3, and performing update training on the prediction model; a time sequence knowledge graph is constructed, the correlation between the medicine and the adverse reaction is comprehensively and systematically presented, powerful support is provided for clinical research and supervision, and the medicine safety monitoring level is improved.

The invention relates to the technical field of medical risk assessment, in particular to a cardiovascular disease risk prediction and assessment system based on combination of electronic medical records and medical images, which adopts a dynamic weight fusion mechanism and is used for adaptively adjusting weight distribution of different data sources according to disease courses of patients in a feature fusion process. The influence of different patients and different data sources on risk prediction can be reflected more accurately, and the accuracy of a prediction result is improved.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a human body state characteristic value analysis method, device and equipment and a medium. Performing system state analysis on an endocrine system, a circulatory system and a respiratory system of the user according to the physical examination data to obtain a system state characteristic value set, constructing a physiological index vector and a disease course characteristic vector according to the physical examination data, and constructing a complication incidence matrix according to case data; performing complication association analysis on the user according to the physiological index vector, the disease course feature vector and the complication association matrix to obtain a complication feature value, analyzing a criticality feature value of the user according to a system state feature value set and the complication feature value to obtain a hazard feature value set, and performing weighted summation on the hazard feature value set to obtain a complication feature value set; and obtaining a human body state characteristic value. And the accuracy of human body state characteristic value analysis is improved.

The invention discloses a clinical decision-oriented medical data analysis system, and relates to the technical field of medical data analysis, the clinical decision-oriented medical data analysis system comprises a patient end, a medical diagnosis end and a medical decision-making system, the medical decision-making system comprises a data acquisition unit, a data analysis unit, a decision-making output unit and a decision-making optimization unit; according to the method, the medical data is acquired, the disease course prediction model is established according to the historical diagnosis and treatment data of the patient, the disease course prediction result is obtained in combination with the medical data, and the medical seeing ability evaluation coefficient of the patient is obtained through comprehensive calculation based on the personal economic data of the patient and the social income data of the patient. The method comprises the following steps: judging the doctor-seeing ability of a patient according to a preset doctor-seeing ability evaluation interval, outputting a target decision scheme which can be loaded by the patient according to the doctor-seeing ability level of the patient, and when the scheme adaptation degree reaches a preset scheme evaluation threshold value, marking the target decision scheme as an optimizable scheme. And controlling and outputting a target optimization scheme by simplifying the examination process and the drug dosage in the treatment process.

The invention discloses an application of a traditional Chinese medicine composition in preparation of a medicine for preventing and treating atherosclerosis combined with hyperhomocysteinemia and / or hyperlipidemia. The traditional Chinese medicine composition comprises the following components: ginseng, lucid ganoderma, safflower, musk, calculus bovis, bear gall, pearl, venenum bufonis, radix aconiti carmichaeli and borneol. According to the traditional Chinese medicine composition disclosed by the invention, through reasonable compatibility of all the components, at least one of indexes such as aorta plaque area, vasomotor inner diameter difference, intima-media thickness, blood fat level, homocysteine content and the like in atherosclerosis combined with hyperhomocysteinemia and / or hyperlipidemia can be effectively improved; the symptoms of atherosclerosis combined with hyperhomocysteinemia and / or hyperlipidemia are relieved from multiple aspects, and the progress of the disease course is blocked. The traditional Chinese medicine composition is controllable in quality, convenient for standardized production, safe and reliable in prescription and free of toxic and side effects on human bodies.

The invention provides a medical record generation optimization method based on type differentiation, and belongs to the field of natural language processing, and the method comprises the steps: constructing a medical record type classification system, designing an exclusive generation model and a scene rule for different types of medical records such as a first disease course, a daily disease course and a stage knot, and combining with a clinical feedback dynamic optimization generation strategy, thereby achieving the purpose of optimizing the medical record generation. The medical record generation method is suitable for scenes such as clinical medical record writing, medical teaching medical record construction and medical AI auxiliary diagnosis and treatment systems of hospitals at all levels.

The invention provides a big data resource service-based patient hierarchical intervention method and system, which are applied to the technical field of medical information, and are used for acquiring full-cycle health data of a patient, generating and updating a patient disease course trajectory sequence, analyzing health index change and trend under short, medium and long time scales, and calculating health scores and steady-state coefficients, so as to realize hierarchical intervention of the patient. A layered intervention scheme is generated, detection and personalized intervention of the health state of the patient are achieved, the timeliness and pertinence of medical intervention can be improved, and the disease management effect is optimized.

The invention discloses a whole course health management system based on artificial intelligence, and the system comprises a user mobile terminal which is used for receiving a rehabilitation plan, obtaining the health data of a user through a data collection device, and transmitting the health data of the user to a background data management module in real time; the doctor mobile terminal is used for acquiring the health data of the user, performing illness state analysis on the user based on the health data of the user, generating a rehabilitation plan when the health data is abnormal, and synchronously transmitting the rehabilitation plan to the background data management module and the user mobile terminal; and the background data management module is used for realizing data interaction between the user side and the doctor side, and performing real-time analysis and risk early warning on the health data of the user based on the time sequence prediction model or / and the risk assessment model. According to the method and the system, the whole course of disease health management with collaboration, intellectualization and individuation can be provided.

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 provides a tumor patient group psychotherapy method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting demographic and disease course information, psychological scale scores, physiological signals and voice-text-face multi-modal data of a patient; emotional features are jointly recognized through a deep model, psychological needs are evaluated, clustering is carried out in combination with disease course stages, and patients are automatically distributed to homogeneous treatment groups. In the implementation process, the speaking balance degree, the topic dominant rate and the intra-group cohesion are calculated in real time, and if indexes cross the boundary, a guide instruction is generated to adjust discussion. And the system adaptively selects and dynamically adjusts a cognitive behavior therapy according to the emotion and interaction state, accepts a commitment therapy or a positive pressure reduction script until the emotion returns to a safety interval, carries out closed-loop summarization on intervention effect data, and outputs a report containing an emotion trend, interaction quality and an intervention effect. According to the method, real-time accurate evaluation, immediate intervention adjustment and continuous optimization can be realized.

The invention discloses a traditional Chinese medicine teaching and clinical simulation method and system based on a six-channel transmission theory, and particularly relates to the technical field of teaching simulation. The method comprises the following steps: acquiring disease course record data and symptom time sequence data, reconstructing a symptom evolution chain of each case and a corresponding six-channel identification label, and extracting six-channel disease course node time sequence characteristic data; the method comprises the following steps: constructing a single-path six-path transmission candidate network and a six-path transmission candidate topological network to obtain standard single-path six-path transmission network data and multi-branch six-path transmission topological structure data, and performing graph fusion and path weight re-calibration to generate six-path transmission topological network model data; and judging whether the current disease course node meets a trans-meridian bifurcation simulation triggering condition or not in combination with the symptom combination of the patient, and generating corresponding teaching feedback data and contrast learning data, so that a six-meridian transmission multi-branch nonlinear evolution process is presented in informatization teaching and clinical simulation scenes.

The invention provides a chronic respiratory system disease risk early warning method and device based on artificial intelligence and a medium. The method comprises the following steps: firstly, acquiring physiological monitoring information of wearable equipment of a patient; inputting the physiological monitoring information into the risk prediction model to obtain a risk early warning result; wherein the risk prediction model is established according to patient medical record information, follow-up visit information and historical physiological monitoring information; the risk prediction model is a Transform-LSTM (Long Short Term Memory) double-branch integrated model. According to the method, physiological monitoring information collected by wearable equipment in real time is input into a Transform-LSTM double-branch integrated model constructed on the basis of patient medical record, follow-up visit and historical physiological monitoring multi-source information, so that a long-distance dependency relationship of multi-modal data is captured by means of a Transform branch to identify a potential risk trend in a stable period; and the time sequence dynamic characteristics of physiological monitoring information are captured through an LSTM branch to perceive short-term signal mutation in an acute exacerbation period, so that accurate early warning of the whole course risk of the chronic respiratory system disease is realized.

The invention provides a non-small cell lung cancer biomarker and application thereof, and particularly relates to application of a reagent for detecting snoRNA SNORD78 or a fragment thereof in preparation of a kit, and the kit is used for diagnosing non-small cell lung cancer and / or screening non-small cell lung cancer tissues. Samples obtained from healthy paracancerous lung tissues and non-small cell lung cancer patients in different staging stages are screened, analyzed and verified to find that snoRNA SNORD78 or fragments thereof have significant expression difference in healthy lung tissue samples (paracancerous) and non-small cell lung cancer tissue samples; moreover, obvious expression difference also exists in healthy individual tissues of the non-small cell lung cancer and individual tissues of different stages of the non-small cell lung cancer, a new molecular marker is provided for indicating the non-small cell lung cancer and the disease course development thereof, and a new thought is provided for preventing and / or treating the non-small cell lung cancer.