134 results about "Treatment strategy" patented technology

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

The invention provides an artificial intelligence and cognitive behavior combined depression treatment intervention and early warning system, which comprises the steps of collecting patient interaction data, physiological perception data and third-party data through a dynamic on-demand authorization mechanism, and extracting four-dimensional feature indexes of cognition, emotion, behavior and physiology; constructing a first psychological matrix of cognition, emotion, behavior and physiological dimensions, dynamically adjusting dimension weights by adopting a Bayesian network to generate a second psychological matrix, and fusing weighted feature scores, change rates and historical mean values to output psychological health state vectors; generating a basic scheme group based on the state vector matching treatment strategy library; and calculating the unit time fluctuation intensity of the mental health state vector in real time, and triggering three-level early warning in combination with a self-adaptive Z-score safety threshold. According to the method, multi-modal data fusion analysis, dynamic personalized intervention and active risk prevention and control are realized, and the accuracy and safety of depression management are improved.

The invention relates to the technical field of medical instruments, in particular to an electromagnetic focusing extracorporeal shock wave pancreatic lithotripsy device and method.The electromagnetic focusing extracorporeal shock wave pancreatic lithotripsy device comprises a device body, a bed body is installed on one side of the device body through a supporting frame, and a lithotripsy notch is formed in the side, close to the device body, of the bed body; a large C-shaped arm and a small C-shaped arm installed on the inner side of the large C-shaped arm are installed at the position of the notch of the device body, an X camera imager is installed at the upper end of the large C-shaped arm, and a shock wave generator and an ultrasonic probe installed on one side of the shock wave generator are installed on the small C-shaped arm. An energy adjusting module used for controlling the shock wave generator and a safety detection module used for detecting the skin temperature are arranged in the device body, and a displayer and a human-computer interaction interface are arranged on one side of the device body. The technical scheme in the prior art has limitations on shock wave generation, positioning precision, energy regulation and control and treatment strategies.

The invention discloses a hemodialysis patient home management system based on an informatization technology, and relates to the technical field of medical informatization, and the system comprises the following components: a data collection and analysis module which collects the information of vital signs, dialysis data, diet intake and living habits of a patient in real time through a wearable device and an intelligent home sensor, according to the method, a decision tree algorithm is utilized to predict dialysis requirements and complication risks, a linear regression model is combined to predict dialysis effects, scientificity and effectiveness of a dialysis scheme are effectively improved, a Q-learning algorithm is introduced to dynamically adjust the dialysis scheme, and the dialysis efficiency is improved. The treatment strategy is continuously optimized according to the real-time feedback of the patient, the comfort of the patient is improved, the long-term effect of dialysis treatment is remarkably improved, the workload of medical staff is relieved, and the self-management ability of the patient is enhanced.

The invention relates to the technical field of hepatic fibrosis treatment strategy optimization, in particular to a hepatic fibrosis treatment strategy optimization system and method based on deep reinforcement learning. The strategy generation module is in communication connection with the database module; the simulation module is in communication connection with the strategy generation module and is used for simulating the application effect of the treatment strategy through a simulation system; the evaluation module is in communication connection with the simulation module and is used for determining an effect value of treatment strategy application through the simulation module and transmitting the effect value to the strategy generation module; the scheme recommendation module is in communication connection with the strategy generation module and is used for recommending the treatment strategy to a doctor; the invention constructs a comprehensive, dynamic and intelligent hepatic fibrosis treatment decision support system. The system not only can process high-dimensional and non-linear medical data, but also can capture dynamic characteristics of disease progression, so that a major span from static decision to dynamic optimization is realized, and the overall efficiency and expandability of the system are greatly improved.

The invention provides a method for constructing a chronic unpredictable negative stress model, the chronic unpredictable negative stress model and application of the chronic unpredictable negative stress model in mental disease research, and belongs to the field of brain organ in-vitro model construction. Through dual verification of a microelectrode array and single cell sequencing, an in-vitro brain organ model capable of simulating chronic unpredictable negative stress core characteristics is successfully constructed, and a potential neural network function reconstruction mechanism and a cell molecule basis of the in-vitro brain organ model are disclosed. The comprehensive research normal form breaks through the limitation of a traditional single technology platform, and an accurate and efficient innovative research platform highly related to human is provided for pathogenesis research of mental diseases, especially chronic stress related diseases such as depression and anxiety and development of novel treatment strategies.

The invention relates to the technical field of medical artificial intelligence, and discloses a non-small cell lung cancer immunotherapy risk assessment method and system and a storage medium, and the method comprises the steps: constructing an optimal strategy tree model capable of dividing a patient into a plurality of subgroups based on a historical patient data set, and determining an initial optimal treatment strategy for each subgroup; determining a subgroup to which a new patient belongs and an initial treatment strategy thereof according to the model; in the treatment process, the deviation degree between the individual response trajectory of the patient and the average response trajectory of the subgroup to which the individual response trajectory belongs is calculated; and when the deviation degree exceeds a preset threshold value of the subgroup, triggering an adaptive adjustment mechanism, and outputting an updated treatment suggestion. By establishing a dynamic monitoring and closed-loop feedback mechanism based on the subgroup benchmark, the technical problem that an existing scheme is difficult to carry out prospective identification and dynamic intervention on individualized risks is solved, so that the accuracy, safety and individualized level of non-small cell lung cancer immunotherapy are improved.

The invention relates to the technical field of medical cosmetology, discloses a laser hair removal method based on multi-parameter feedback, and aims to solve the problems of unstable curative effect and insufficient safety caused by single parameter of traditional laser hair removal. The method comprises the following steps: synchronously acquiring multi-dimensional physiological parameters such as skin type, hair follicle depth and epidermis temperature through multispectral imaging, high-frequency ultrasound and temperature sensing; constructing a personalized treatment parameter model based on a deep neural network, and generating initial energy density, pulse width and cooling parameters; laser output is regulated and controlled in real time by using a fuzzy logic controller in the treatment process, and it is ensured that thermal injury accurately acts on the hair papilla; performing adaptive optimization on the treatment strategy in combination with reinforcement learning, and continuously updating the model through a closed-loop learning mechanism; meanwhile, a treatment history database is established, curative effect prediction and parameter backtracking are supported, precision, individuation and high safety of laser depilation are achieved, depilation efficiency is remarkably improved, and the risk of skin adverse reaction is reduced.

The invention discloses a layering and preference learning-based personalized dynamic treatment strategy generation system for Parkinson's disease, which comprises a dynamic state and preference representation module, a preference-based reward function learning module, a layering treatment strategy generation module and a treatment path simulation and interactive output module, the system learns preferences from doctor-patient interaction and generates layered, dynamic and explainable personalized long-term treatment strategies, so that making of personalized long-term treatment decisions is achieved, and the problems that in an existing Parkinson's disease reinforcement learning scheme, an award function is fixed, and long-term planning ability is lacked are solved.

The invention provides an IVIG drug resistance prediction method in KD based on an interpretable machine learning model, and belongs to the technical field of biological information processing. Comprising the following steps: S1, data acquisition; s2, data processing; s3, constructing a model; s4, a model interpretation module; and S5, webpage deployment. According to the invention, based on the large-scale electronic medical record data of the Kawasaki disease patient, a machine learning model with good interpretability is constructed for predicting the IVIG resistance, and an online webpage calculation tool is established for clinical doctors to input key indexes in real time and automatically output the IVIG resistance risk probability of the patient. According to the tool, 11.6% of the optimal prediction threshold value is set, doctors can be helped to identify high-risk individuals, treatment strategies can be adjusted in time, and coronary artery complications are reduced.

The invention discloses a case misdiagnosis identification method and device and a storage medium, relates to the technical field of medical technology and machine learning, and discloses the steps that multi-source heterogeneous medical detection data of a to-be-identified case and a disease diagnosis result and a treatment strategy of a doctor on the to-be-identified case are acquired; based on the multi-source heterogeneous medical detection data, the disease diagnosis result and the treatment strategy, diagnosis and treatment path matching is conducted on a pre-constructed typical disease knowledge base, the corresponding diagnosis and treatment path deviation degree is obtained, and the typical disease knowledge base comprises multiple disease entries and reference diagnosis and treatment paths corresponding to the disease entries; and inputting the multi-source heterogeneous medical detection data, the disease diagnosis result, the treatment strategy and the diagnosis and treatment path deviation degree into a misdiagnosis recognition model, and performing misdiagnosis recognition on the to-be-recognized case based on the misdiagnosis recognition model to obtain a corresponding misdiagnosis recognition result, thereby solving the technical problem of low misdiagnosis recognition accuracy of the actual diagnosis and treatment path of the case in the prior art, and improving the accuracy of the misdiagnosis recognition. And the misdiagnosis identification accuracy is improved.

The invention discloses an emergency treatment resource scheduling method based on a multi-stage dynamic game, and relates to the technical field of medical emergency management, and the method comprises the steps: according to a three-party dynamic game model corresponding to a pre-hospital first-aid team, a hospital department and a transfer unit, and the medical multi-modal data of each patient in a current treatment scene, carrying out the multi-stage dynamic game; determining a pre-hospital first-aid team treatment strategy, an in-hospital department treatment strategy and a transfer unit treatment strategy; according to the three-party treatment strategy, robust scheduling based on meta-learning is carried out on current existing emergency treatment resources, and a robust scheduling strategy is determined; iCU bed scheduling, operating room scheduling and ambulance scheduling are executed according to the robust scheduling strategy. According to the method, emergency treatment resource allocation balance under the dynamic game of multiple participants is realized, meanwhile, sudden disturbance can be quickly adapted through the meta-learning framework, the purpose of effectively allocating emergency treatment resources in response to emergencies is also realized, and the adaptability of a robust scheduling strategy to the emergencies is improved; and the resource scheduling strategy has interpretability and stability.

Disclosed are systems and methods for building and using a machine-learning model to facilitate intelligent selection of treatment strategies for subjects suffering from hemophilia. Sensor data (e.g., that characterizes movement of or a physiological characteristic of particular a subject) can be used as an input to a machine-learning model workflow. The sensor data may affect a model selection, model configuration, model result, pre-processing and / or post-processing. A result of the workflow may inform or influence a treatment selection, treatment schedule, treatment dosage and / or activity recommendation for the particular subject.

The invention belongs to the technical field of air pressure treatment, and discloses a self-adaptive multi-point monitoring intelligent pulse anti-thrombus air pressure treatment system. Comprising the following steps: continuously collecting physiological parameters of a plurality of monitoring points of a patient, and constructing a parameter sequence; performing dynamic trend analysis on each parameter sequence in sequence, and extracting a corresponding physiological feature set; sequentially analyzing each group of physiological feature set, constructing a physiological feature sequence, comprehensively evaluating the risk score of each monitoring point, and dynamically evaluating the thrombus risk index of each monitoring point based on the risk score; fusing the thrombus risk index and the physiological feature sequence of each monitoring point, intelligently formulating an optimal treatment strategy, and carrying out differential parameter configuration on pulse air pressure treatment equipment; precise evaluation and personalized management of the physiological state of the patient in the air pressure treatment process are achieved, the treatment accuracy and effectiveness are improved, and the comfort and compliance of the patient are enhanced.

The invention discloses a construction method and application of a preeclampsia animal model based on an RASA3 target, and belongs to the technical field of biomedicine.The preeclampsia animal model is constructed by injecting Ad-RASA3 into a pregnant mouse and monitoring pathological indexes, related to preeclampsia, of the pregnant mouse. According to the method, the animal model with preeclampsia-like clinical symptoms can be stably induced, the abnormal change of the uterine spiral artery remodeling process can be systematically monitored, the method is high in operation controllability and model stability, and an effective tool can be provided for deeply researching the pathogenesis of preeclampsia and developing a novel diagnosis and treatment strategy.

The invention discloses an intelligent process automatic regulation and control method for orthopedic treatment. The method comprises the following steps: S100, acquiring a multi-dimensional data set of a patient; s200, generating a treatment strategy set based on the multi-dimensional data set; the step S200 comprises the following sub-steps: S201, inputting the three-dimensional skeleton model into a deep learning network for pathological feature extraction, and obtaining skeleton defect region positioning and generating a dynamic strategy; and S202, generating a treatment strategy set and performing priority management. Compared with the prior art, the method has the following advantages and effects that various data of the patient are collected and processed, the personalized treatment strategy is generated, bone defects and cross-layer injuries can be efficiently recognized, dynamic risk assessment and treatment strategy optimization are carried out, and therefore the accuracy and effectiveness of orthopedic treatment are improved.

The invention discloses a rehabilitation evaluation system and method based on data feedback, and the method comprises the steps: obtaining a continuous online rehabilitation video as rehabilitation training video frame data, and constructing a rehabilitation training motion data set; extracting joint angle features and skeleton point distance features from the action data set by adopting a label classification technology, and obtaining distance measurement of each human body joint skeleton point coordinate; matching the time sequence with the action features by adopting a dynamic adjustment algorithm for the action feature combination, and constructing a classification evaluation model of rehabilitation training actions; a data feedback mechanism is introduced into the classification evaluation model of the rehabilitation training actions, the relation between rehabilitation requirements and rehabilitation evaluation is mapped, the whole system achieves detailed analysis and dynamic adaptation of the rehabilitation training actions, accurate rehabilitation suggestions can be provided according to individual differences, the effectiveness of a treatment strategy is ensured through real-time monitoring and adjustment, and the rehabilitation training effect is improved. And the rehabilitation effect and efficiency are greatly improved.

The invention relates to the technical field of cell biology, in particular to application of a composition containing a mesenchymal stem cell secretor in pulmonary fibrosis. A systematic pharmacodynamic experiment verifies that the composition can remarkably reduce the content of a key fibrosis promoting factor TGF-beta1 in the blood of a pulmonary fibrosis model rat and effectively improve the blood-gas exchange function at the same time, and the blood oxygen saturation (SaO2) is remarkably increased, and the partial pressure of carbon dioxide (pCO2) is remarkably reduced. The improvement of the key indexes proves that the composition has a definite anti-fibrosis effect and a lung function protection effect, and a brand new treatment strategy is provided for pulmonary fibrosis diseases which lack special-effect treatment clinically at present. The technology not only has a remarkable clinical transformation value, but also develops an innovative research thought for the lung disease treatment field based on the action mechanism of the stem cell secretor, and has important scientific significance and market development prospect.

The invention provides a femoral head ischemic necrosis risk prediction method and device based on machine learning, and the method comprises the steps: collecting the related information of a patient, screening a mapping relation between an outcome variable and a prediction variable according to standardized text data and a C ox regression analysis method, and building a text machine learning model; carrying out image segmentation and feature labeling on the standardized image data, establishing a focus positioning model, and carrying out training and verification by using sample image data; and fusing the text machine learning model and the image recognition model to obtain a multi-modal femoral head ischemic necrosis risk prediction model, predicting an ischemic necrosis focus position and a staging diagnosis result of a patient, and generating a corresponding risk assessment report. Through a machine learning model, whether the patient is more likely to develop into ANFH or not can be predicted, and a personalized treatment strategy is formulated. In combination with iconography data, lifestyle information and clinical data, the A I can construct an accurate prediction model to help doctors to formulate a more effective treatment scheme.

The invention discloses a distribution and micro collaborative low-voltage diagnosis method and system based on digital twinborn and association mining. The method comprises the following steps: constructing a digital twinborn model of a distribution and micro collaborative power distribution network; analyzing low-voltage spatial-temporal distribution characteristics, screening a governance area, simulating the governance area by using a digital twinborn model, selecting a corresponding governance strategy to adjust the digital twinborn model for simulation again after mining a low-voltage cause, and verifying a governance effect of the selected governance strategy based on a simulation result; and for the governance strategy with the governance effect meeting the requirement, calculating a comprehensive score according to the corresponding simulation result, and taking the governance strategy with the highest comprehensive score as a low-voltage governance strategy. According to the method, the distribution micro-grid collaborative digital twin is constructed, diagnosis and treatment of the low-voltage problem are carried out based on the digital twin and association mining, and the voltage treatment level of the distribution micro-grid is improved.

The application discloses a sensitized ferroptosis lipid nano regulator and a preparation method and application thereof, and belongs to the technical field of new adjuvants and new dosage forms of drug preparation combined treatment. The lipid nano regulator is co-assembled by a GPX4 inhibitor and a FASN inhibitor through intermolecular forces, and is modified with an oxidation-reduction sensitive PEG modifier, a molar ratio of the GPX4 inhibitor and the FASN inhibitor is 10:1-1:10, a mass ratio of the sum of the GPX4 inhibitor and the FASN inhibitor to the PEG modifier is 10:90-90:10, and the intermolecular forces include pi-pi stacking force, hydrophobic force and hydrogen bond. The co-assembled nano preparation provides a new strategy and more choices for the development of drug delivery, and meets the urgent needs of high-efficiency and diverse ferroptosis treatment strategies in preparations in the clinic.

The invention belongs to the technical field of large language models, and discloses a man-machine cooperation method, device and equipment in psychotherapy, which introduces an integrated modeling research normal form, proposes a calculation and evaluation framework of MI intervention quality in combination with an MI four-stage theory, and comprises MI quality, appropriate and improper MI strategies and related language clues. And the heuristic effect of the LLM in improving the task capability of motivating interviews (MI) is detected by combining customized CoT prompts and an RAG technology. The calculation evaluation framework provided by the invention provides a quantifiable evaluation framework for MI practice of human and generative AI, and reveals a complex relationship between a treatment strategy and an intervention effect; the ability and limitation of the LLM in the MI field are verified at the practice level, the prompt project based on man-machine cooperation has a remarkable effect on improving the LLM ecological effectiveness, and a technical path is provided for standardization of digital psychological health services and ethical risk prevention and control.

The invention discloses a GLP-1 (32-36) engineered exosome and application of the GLP-1 (32-36) engineered exosome in drugs and instruments for preventing and / or treating diabetic lower limb arterial lesion. The preparation process and related characteristics of the GLP-1 (32-36) engineered exosome are respectively as shown in figures (1) and (2). The exosome is prepared by treating human pluripotent stem cells by using GLP-1 (32-36). The GLP-1 (32-36) engineered exosome has relatively high activity in plasma, and the enterotrypsin synthetic peptide can effectively improve the yield of the exosome and further optimize the function of the exosome, so that a stronger therapeutic effect of promoting angiogenesis after ischemia is exerted. The GLP-1 (32-36) engineered exosome disclosed by the invention also has safety and can be used for a treatment strategy of diabetic lower limb artery complications.

The invention provides a chest pain type determination system, a program product, an electronic device and a storage medium, the program product comprises a computer program or instruction, and when the computer program or instruction is executed by a processor, the following method steps are executed: determining a chest pain risk level according to multivariate medical information of a patient; if the level is the first level, analyzing multivariate medical information according to first guide information for any preset type of high-risk chest pain disease to obtain a preliminary result; according to the preliminary results corresponding to the various types of high-risk chest pain diseases, determining a target high-risk chest pain type; if the first level is the first level, analyzing the multivariate medical information by referring to second guide information for any preset type of low-risk chest pain disease to obtain a preliminary result; and determining a target low-risk chest pain type according to the preliminary results corresponding to the various types of low-risk chest pain diseases. And determining a corresponding diagnosis and treatment strategy for each target chest pain type. Therefore, the target chest pain type can be quickly and accurately determined, and a diagnosis and treatment strategy can be given, so that a doctor can be assisted to quickly and accurately diagnose and treat.

The invention relates to the technical field of software security governance, in particular to an open source component governance method and system based on multi-dimensional measurement and feedback optimization, and the method comprises the steps: collecting multi-source heterogeneous data of an open source component, carrying out the feature extraction, and constructing a basic measurement matrix containing security, compliance, efficiency and collaborative dimensions; respectively calculating a security dimension index and a compliance dimension index which represent a risk defense boundary, and an efficiency dimension index and a cooperation dimension index which represent business delivery kinetic energy, and generating a multi-dimensional efficiency state vector; mapping to a governance efficiency evaluation space, and generating an efficiency deviation characteristic value by calculating the coupling collaboration degree and restriction loss among the dimension indexes; and reversely deducing the adjustment gradient of the control strategy parameter to generate a dynamic correction instruction for the strategy execution component, issuing the dynamic correction instruction to the strategy execution component to update the control rule in real time, and executing a treatment operation on the open source component. According to the method, the treatment effect of the software can be effectively quantified, and the treatment strategy is guided to carry out targeted adjustment and continuous optimization.

The invention belongs to the technical field of biological medicine, and particularly relates to a new Spp1 + Carmil1 + and Spp1 + Cav1 + macrophage subgroup, a screening method and application in diagnosis and treatment of rheumatoid arthritis (RA). Aiming at the function limitation of a traditional M1 / M2 classification model, the invention proves the following new subgroup pathological interaction mechanism: osteoclast (ACP5 +) specifically releases TNFSF11 (RANKL), and is co-localized with Spp1 + Carmil1 + and Spp1 + Cav1 + subgroup space; the Spp1 + Carmil1 + subgroup secretes IL-18, and space interaction exists between the Spp1 + Carmil1 + subgroup and Treg (Foxp3 +) and Th17 (Il17a +) cells. The invention provides a novel cell marker and a targeted treatment strategy for accurate diagnosis and treatment of RA.

The application relates to the technical field of medical health data processing, and discloses a blood potassium management and monitoring method and system based on multivariate input, which comprises the following steps: firstly, obtaining demographic data, medical history data, test data, medication data and medication adjustment event data of a patient, and performing pretreatment; then, based on the pretreated data, determining whether to enter a potassium supplement path or a potassium reduction path, and performing risk stratification on the blood potassium state of the patient, and outputting a risk stratification result and a treatment strategy; then, according to the determined path and the risk stratification result, performing corresponding medication decision to generate a medication adjustment scheme, and based on the medication adjustment scheme, generating a review suggestion for the patient; finally, showing the analysis result containing the risk stratification result, the treatment strategy, the medication adjustment scheme and the review suggestion to a user end, and recording an operation log. The application meets the clinical demand for fine, individualized and whole-process management of blood potassium.

The invention provides application of kestose in preparation of a medicine for treating heart failure related diseases, and belongs to the technical field of biological medicine. The prominent application value of the natural compound kestose in prevention and treatment of heart failure related diseases is disclosed for the first time. The traditional Chinese medicine composition has the direct effects of remarkably improving cardiac insufficiency and relieving myocardial injury; in the aspect of product application, a brand-new choice of a heart failure treatment medicine derived from a natural product is provided for clinic, the medicine has a good myocardial protection effect, a solid foundation is laid for developing a targeted treatment strategy aiming at a specific pathological link of chronic cross-induction, and the application has a wide application prospect. Important theoretical significance and wide clinical application prospects are realized.

The application discloses a 7T magnetic resonance head and neck tumor radiotherapy response evaluation system, belongs to the field of medical imaging and tumor radiotherapy, and comprises an ultrahigh-field multi-modal image acquisition module, a multi-phase accurate registration and radiomics feature extraction module, an early response biomarker identification module and an adaptive treatment decision support module. The high resolution, high signal-to-noise ratio and multi-nuclide imaging capability of the 7T ultrahigh-field magnetic resonance are utilized to acquire multi-modal images before and during treatment, more than 200 features are extracted through accurate registration and radiomics analysis, the treatment response is predicted based on the Delta feature and the deep learning model 2 to 3 weeks after the start of radiotherapy, the prediction accuracy reaches 87.5%, the prediction is 3 to 4 weeks earlier than the conventional evaluation, decision support is provided for timely adjustment of the treatment strategy, a closed-loop feedback mechanism is established, the clinical verification results are used to optimize the parameters of each module reversely, and continuous improvement is realized.

The invention discloses a deep learning-based lung cancer adjuvant chemotherapy benefited patient identification method and system, and relates to the technical field of lung cancer precise treatment and artificial intelligence crossing, and the method comprises the steps: analyzing and preprocessing preoperative and postoperative registration logs of a patient to obtain basic information of the patient, a pathological image of the patient and a treatment strategy of the patient; the method comprises the following steps: performing segmentation processing on a patient pathological image by adopting nnformer to obtain a focus region segmented image, performing feature extraction and enhancement on the focus region segmented image to obtain image omics features, and constructing an improved 3D survival analysis model; and analyzing the basic information of the patient, the radiomics characteristics and the treatment strategy of the patient by adopting an improved 3D survival analysis model to obtain an identification result of the lung cancer adjuvant chemotherapy benefited patient. According to the method, the quantitative survival risk score and the adjuvant chemotherapy sensitivity conclusion are output, and an interpretable reference basis is provided for clinical decision making.