178 results about "Optimal treatment" patented technology

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

The invention belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention belongs to the technical field of intelligent medical treatment, and discloses a chronic disease information management system based on a behavior interaction model. Comprising the following steps: acquiring background information, internal motivation and cognitive evaluation; based on the background information, a health education content library is constructed, and health education content is pushed in combination with cognitive evaluation; fusing the background information and the internal motivation to generate a multi-dimensional treatment scheme matrix; acquiring willingness information of a patient, performing quantitative evaluation on acceptance degrees of different treatment schemes, and screening out an optimal treatment scheme; acquiring and analyzing real-time interaction data, and dynamically formulating an emotion support strategy; patient health data are integrated, and the interaction effect is evaluated; according to the interaction effect, the health education content, the optimal treatment scheme and the emotion support strategy are intelligently optimized in sequence; behavior interaction is taken as the core, and accurate management of the whole life cycle of the chronic disease patient is realized, so that the health management effect of the patient is remarkably improved, and the complication risk is reduced.

The invention provides a self-adaptive clinical trial design method based on machine learning, relates to the technical field of clinical trials, and adopts a method integrating reinforcement learning, causal inference and data enhancement technologies to predict an optimal treatment scheme and adjust a distribution proportion in real time through a dynamic optimization module, and limits high-risk treatment selection in combination with dynamic ethical constraints. The problems that a traditional clinical test preset rule lacks real-time performance and neglects patient safety are solved, a data collection and enhancement module generates virtual patient data conforming to a causal relationship, real and virtual data are combined to form a unified data set, a high-quality data set is constructed, the data size is effectively expanded, the statistical power of a small sample scene is improved, and the accuracy of a clinical test result is improved. The termination rule module of comprehensive statistics, ethics and resources shortens the test period through weighted evaluation, significantly reduces the risk of improper allocation of high-risk groups, enhances the benefit of patients, ethics compliance and test result reliability, and expands the application range in rare disease research and personalized medical treatment.

The invention discloses a transcranial magnetic stimulation target region recommendation method based on craniocerebral position estimation. The method comprises the following steps: constructing a face key point cloud based on binocular stereo vision; reconstructing the face key point cloud into a cranial surface model by using a pre-trained generative model; the method comprises the following steps: collecting magnetic resonance imaging data of patients with various diseases, establishing a transcranial magnetic stimulation target region standardized template with disease specificity, and determining a stimulation target region of each disease in a standard space; and based on the transcranial magnetic stimulation target region standardization template, mapping the corresponding stimulation target region from the standard space brain region to the scalp of the patient according to the disease type of the patient so as to realize recommendation. According to the method, the graph neural network is innovatively combined with the SHAP algorithm, the scientificity and interpretability of target spot selection are improved, important brain region target spots are obtained by calculating the contribution value of classification model features to classification decision of each sample, and then intervention target spots are sorted and selected to obtain an optimal treatment target spot recommendation scheme.

The invention discloses an informatization process processing method for a thoracic pain stroke trauma treatment mode, and the method comprises the following steps: 1, information collection and preprocessing: collecting the physiological sign data of a patient through a mobile terminal, and analyzing the physiological sign data based on a deep learning model, so as to obtain a preliminary diagnosis result of an illness state; classifying and marking the patient according to the preliminary diagnosis result of the illness state to obtain a classification and marking result, and uploading the classification and marking result, the physiological sign data and the geographic position information of the patient to a regional medical collaborative server; step 2, multi-center cooperative distribution: after receiving the classification marking result, the physiological sign data and the geographic position information of the patient, a regional medical cooperative server performs calculation processing based on a preset medical resource scheduling model; and step 3, performing a treatment process: sending the optimal treatment path scheme to a medical information system of a target medical center by the regional medical cooperation server, triggering the target medical center to start an emergency plan, and deploying medical resources.

The invention discloses a traditional Chinese medicine diagnosis and treatment aided decision-making method and system, and relates to the technical field of traditional Chinese medicine intelligent diagnosis and treatment aided decision-making, and the method comprises the steps: carrying out the wavelet transformation analysis of pulse condition waveform time series data, extracting a pulse condition dynamic change feature sequence, inputting a tongue image color change rate and a pulse condition feature change rate into a space-time convolution network, and obtaining a time-space convolution network; generating a tongue pulse dynamic association index and outputting a preliminary syndrome classification; fourier transform analysis is carried out on the spleen channel acupoint impedance spectrum, false-true state judgment is carried out in combination with tongue pulse dynamic correlation indexes, preliminary syndrome classification is corrected, and a false-true state quantification result and corrected syndrome classification are output; and matching the corrected syndrome classification with the traditional Chinese medicine knowledge map, adjusting the dosage according to a virtual-real state quantification result, reversely verifying the drug-syndrome specificity through a curative effect database, screening an optimal treatment scheme, and outputting a personalized diagnosis and treatment scheme. According to the method, the syndrome classification precision is improved, and an objective basis is provided for personalized dose adjustment.

The invention relates to the field of oral clinical orthodontics, and discloses a method for generating an oral correction scheme, comprising: acquiring a face composite model of an initial state of a patient, the face composite model comprising a dental jaw portion and a soft tissue portion; respectively obtaining P correction schemes of the patient, wherein each correction scheme corresponds to a target state of tooth jaw movement; correspondingly obtaining face prediction models of P target states according to the tooth jaw movement and deformation face composite models of the P correction schemes; and in the P correction schemes, selecting an optimal correction scheme as a target correction scheme according to evaluation of the face prediction model in the target state. The invention further discloses electronic equipment and a storage medium, the orthodontic scheme is determined by predicting the appearance, the predicted result is closer to the real effect after orthodontic treatment, more bases are provided for the patient to select the orthodontic scheme, the patient can conveniently determine the ideal orthodontic scheme from the face expression, and a more preferable orthodontic scheme is generated.

The invention belongs to the technical field of medical data processing, and relates to a urinary surgery diagnosis and treatment data intelligent processing method and system, and the system comprises a multi-modal data collection module which is used for collecting basic diagnosis and treatment data, behavior data and diagnosis and treatment process data of a patient; the diagnosis and treatment data intelligent analysis module is used for calculating the intention value of the patient for the diagnosis and treatment item and generating a diagnosis and treatment path diagram; the intelligent decision support module is used for generating personalized diagnosis and treatment suggestions in combination with a guide and a model; the doctor-patient interaction feedback module is used for collecting patient feedback and updating an analysis weight; and the follow-up management module is used for generating a follow-up plan and relapse early warning. Basic diagnosis and treatment data and patient behavior data are fused, and diagnosis and treatment requirements are reflected more comprehensively; the dimension weight is adjusted and analyzed in real time through patient feedback, and the personalized diagnosis and treatment precision is improved; a guide rule and a machine learning model are combined to assist a doctor to quickly generate an optimal treatment scheme; through the block chain and privacy protection technology, compliance use of the medical data is ensured.

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 present disclosure provides apparatus and methods to both infer stent expansion (vessel expansion or compliance) and probability of procedural success (vessel patency) from pretreatment diagnostic imaging at the point-of-care and also train an inference model to generate such an inference, thus allowing a physician to choose with greater certainty an optimal treatment tool and treatment protocol for treating a vessel of a patient.

The invention provides a special disease first-aid full-process auxiliary decision-making method based on multi-modal perception and AI, and relates to the technical field of medical first aid, and the method comprises the steps: obtaining first-aid field multi-modal data, constructing a feature dependence graph, and recognizing the type and symptom features of a special disease; indexing special disease diagnosis and treatment data based on the symptom feature vector, and determining a reference case and a treatment path; the action space is constructed in combination with clinical specifications, the expected income is predicted by applying Monte Carlo search, and the optimal treatment scheme is generated, so that the first-aid special disease recognition accuracy can be improved, the treatment decision time can be shortened, and the first-aid intervention effect can be improved.

The invention discloses a combined treatment medicine for acute myelogenous leukemia. The combined treatment medicine is prepared from vinaclaria, azacitidine and aclarithromycin. The medicine is applied to preparation of an acute myelogenous leukemia cell proliferation inhibitor; the medicine is applied to preparation of an acute myelogenous leukemia cell apoptosis inducer. The three medicines are combined to achieve a synergistic effect: the three medicines have a synergistic anti-tumor effect instead of a simple superposition effect, and a new combined medication mechanism is a result obtained through laboratory research and an initial effect of clinical application. The subsequent consolidation treatment scheme may also be used for part of initially treated patients with acute myelogenous leukemia, which are not suitable for enhanced chemotherapy or intense intense treatment by using targeted drugs, and relapse / refractory patients with acute myelogenous leukemia, and a better treatment scheme can also bring new hope and dawn to more patients.

The invention discloses a real-time feedback and self-adaptive phototherapy parameter adjustment method based on AI, and the method comprises the steps: S1, collecting multi-source data of a patient in a phototherapy process, and forming a multi-source biological feedback data set; s2, constructing an AI algorithm model, inputting the multi-source biological feedback data set into the AI algorithm model, and outputting the change trend of the physiological state of the human body; s3, setting a phototherapy effect evaluation index system, comparing the change trend of the physiological state of the human body with a preset phototherapy effect evaluation index, and calculating a deviation value between a current phototherapy parameter and a parameter required for realizing an optimal treatment effect; s4, dynamically adjusting the phototherapy parameters through a reinforcement learning algorithm according to the deviation value, and determining a parameter combination of phototherapy in the next stage; and S5, the optimized phototherapy parameters are sent to phototherapy equipment for adaptive dynamic adjustment. The problems that phototherapy parameters cannot adapt to individual differences and physiological state changes of patients, so that the treatment effect is poor, and safety and comfort are insufficient are solved.

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 provides a phototherapy device and a using method of the phototherapy device, and belongs to the technical field of medical instruments, and the device comprises an electroencephalogram collection module which is used for collecting an electroencephalogram signal of a target person and converting the electroencephalogram signal into a digital signal; the phototherapy module is used for outputting light with target power to the skull of the target person after starting to work; the temperature detection module is used for detecting the scalp temperature of the target person; and the data analysis and control module is used for controlling the phototherapy module to start working under the condition of determining that the target person enters a non-rapid eye movement sleep window period based on the digital signal, and controlling the phototherapy module to stop working under the condition of determining that the scalp temperature exceeds a preset temperature threshold. The optimal treatment window of the target person can be determined by identifying the electroencephalogram signals, and the technical problem that an existing phototherapy device is poor in use effect is solved.

The invention discloses an orthodontic auxiliary decision-making method based on fuzzy comprehensive evaluation and an operation system thereof, which are used for providing a personalized orthodontic treatment scheme, and specifically comprise the following steps: firstly, comprehensively analyzing key factors influencing an orthodontic treatment effect, screening out representative evaluation indexes, and determining the evaluation indexes; an evaluation system of orthodontic treatment decisions is constructed; then, respectively calculating a subjective weight and an objective weight by adopting an analytic hierarchy process and an entropy weight method, and optimizing weight distribution in combination with a game theory to obtain a comprehensive weight of each index; and finally, based on a fuzzy comprehensive evaluation method, constructing a trapezoidal membership function, establishing a fuzzy relation matrix, and determining an optimal treatment scheme through a maximum membership principle. Scientific and accurate auxiliary decision support can be provided for orthodontic doctors, the individuation level of a treatment scheme is effectively improved, and the developed operation system application can improve the diagnosis efficiency of young doctors.

The invention provides a depression prediction method of a grey prediction model and a related product. The depression prediction method comprises the following steps: acquiring a grey prediction model based on a grey system theory; acquiring multiple groups of historical diagnosis data and current diagnosis data of the target user; substituting multiple groups of historical diagnosis data and current diagnosis data of the target user into the grey prediction model, and calculating a prediction error of the grey prediction model; and in response to the prediction error meeting the preset requirement, predicting the future depression degree score of the target user according to the multiple groups of historical diagnosis data of the target user, the current diagnosis data of the target user and the gray prediction model. The method achieves the accurate prediction of the future depression degree change of the target user through the gray prediction model, and achieves the purpose of providing a diagnosis basis for the early intervention or the timely adjustment of the intervention method to achieve the optimal treatment effect.

The invention discloses a clinical treatment scheme evaluation method and system based on artificial intelligence, and relates to the technical field of medical care, and the method comprises the steps: building an evaluation model; acquiring a plurality of treatment schemes; initial information of the patient is obtained, the initial information and the treatment scheme are input into the evaluation model, and an optimal treatment scheme is obtained; when the evaluation model evaluates the treatment schemes, each treatment scheme is divided into a plurality of components, each component is analyzed, and a characteristic label of each component is determined; updating information of the patient is obtained, the change content is analyzed, the characteristics of the change content are determined, and the corresponding components in the optimal treatment scheme are replaced with the components with the characteristic labels matched with the characteristics in the treatment scheme. The treatment scheme is evaluated by adopting an artificial intelligence technology, the subjective influence of doctors is reduced, and the optimal treatment scheme can be adjusted in time, so that the optimal treatment scheme keeps the maximum fitness with the patient at any time.

The invention relates to the technical field of medical treatment, and discloses a lung cancer personalized treatment scheme recommendation method and system and a storage medium. The method comprises the following steps: acquiring clinical and molecular indexes of a patient, and constructing a simplified feature set; distributing weights for treatment targets according to the simplified feature set, constructing and optimizing a scheme evaluation matrix, and generating a preliminary scheme sorting list; through threshold screening and patient feature matching degree verification, a verified scheme set is obtained; the schemes are classified based on gene mutation and driver gene features, and classified optimization scheme subsets are obtained; constructing an interaction model to analyze interaction influence of toxic and side effects and life quality on curative effects, and dynamically adjusting scheme scores; and if the score is lower than a threshold value, triggering iterative optimization, obtaining a scheme list after iteration, and further determining an optimal treatment scheme according to treatment collaboration and target balance. According to the method, intelligent and closed-loop optimization from multi-source data to personalized treatment decision is realized, and the personalization and accuracy of a treatment scheme are improved.

The ability to improve the adjusting of technical parameters in therapy plans found by Pareto navigation constitutes a technical problem, along with facilitating the decision-making of the planner (also called user) and simplifying the mathematical calculation of the solution. Proposed is a way to find and establish a therapy plan among a plurality of plan proposals for cancer therapy, each plan proposal representing as a solution one point in a navigation space (N) containing a plurality of setting parameters and establishing in a machine space (M) a setting for radiation doses to be administered by a radiation therapy machine for cancer therapy. The navigation space (N) contains at least one Pareto front (200) behind or on which available treatment plans are located as possible solutions and among which a user navigates, whereby a first plurality of available solutions calculated in advance as plan proposals is provided in a memory and retrieved or read out from the memory for display. The retrieved or read-out plan proposals are shown on a display and used by the user for navigation, whereby a large number of Pareto-optimal solutions with physical values or evaluations of dose values can be found among the points. Moreover, such solutions are also shown that are almost Pareto-optimal, within a tolerance range of at most ±5% from the Pareto optimum. In this way, a large first set of points with the physical values or evaluations of dose values is created and then stored in the memory. From this, a second set of points is calculated, which defines clinical targets. A decision space (D) is provided and shown in which at least the clinical targets are plotted on target axes (76, 876, 776). This is without the possibility of navigating in the decision space (D) on the target axes of the clinical targets, whereas binary selections in the decision space are made in the manner of restrictors to specify which plan proposals are not used as therapy plans. This enables a desired therapy plan to be found and determined from the large number of plan proposals.

The present invention relates to a human limb impedance measurement and electrotherapy device, which includes a main control module, an impedance measurement module, a therapy module, a therapy evaluation unit, a mode switching module, an output voltage and therapy current monitoring circuit, an adaptive control unit, a channel selection module and a sensor electrode array. This device can achieve wide-range, high-precision, multi-frequency point and multi-channel human limb impedance measurement, accurately locate human limb acupoints by comparing the relative impedance magnitudes of each point in the measurement area, and can be converted into a therapy mode to perform electrotherapy at the located acupoint points to achieve precise therapy; during the therapy process, it tracks the therapy progress in real time and ensures the best therapy effect through adaptive control; and after the therapy is completed, it measures the impedance data of the therapy area again, and evaluates the therapy effect by comparing the impedance values before and after the therapy area; the upper computer supporting the device has perfect functions and simple operation, and can quickly locate the lesion points through color assignment display processing.

An apparatus and method for optical skin treatment procedures and in-treatment monitoring thereof. The apparatus is operable in two modes: a treatment mode for delivery of treatment light to an area of a patient's skin abutted to a light guide of the apparatus; and a diagnostic mode for acquiring one or more images of the abutted area of skin. Switching between the two modes may be made in a short time, facilitating monitoring during treatment. In exemplary embodiments, the apparatus sends image data to an analyzer, which analyzes images and computes an optimal treatment course, at least optimal parameters (intensity, pulse width, repetition rate) for the next delivery, and sends the optimal treatment course to the controller for implementation by the apparatus in real time.

The invention discloses an MICP intensity prediction method and system based on machine learning and multi-feature fusion, and relates to the field of solidified data analysis. An accurate quality prediction mapping model and an abnormal compensation parameter mapping model are constructed based on historical data; the system has the capabilities of predicting curing quality from process parameters and intelligently recommending an optimal disposal scheme from an abnormal state; by setting a dual-threshold quality control mechanism and a real-time abnormity monitoring and early warning system, various abnormal conditions in the curing process can be actively identified and timely responded, and complete closed-loop processing from parameter deviation detection to automatic compensation measure execution to hardware standby switching can be realized. The stability of the solidification process and the consistency of the engineering quality are effectively guaranteed, the requirement for manual intervention and the misjudgment risk are greatly reduced, powerful technical support is provided for standardization, normalization and industrialization development of the sand solidification technology, and the engineering efficiency and economic benefits are remarkably improved.

The present invention relates to the field of characterizing microbiota present on human skin. More specifically, the present invention relates to a device for revealing a skin-microbiota complex in an immunochromatographic manner, the device allows for rapid and meaningful biological determination of two members of skin-microbiota symbiosis by means of immunological characterization of biomarkers available on the surface of the microbiota-skin interacting group. Artificial intelligence is then used to recommend optimal care that is suitable for the skin type on the one hand and compatible with its microbiota on the other hand, thereby reducing the risk of adverse effects. Similarly, in the case of skin flaws and / or microbiota imbalances, the present invention will recommend optimal corrective treatments to ensure compatibility with or be able to modulate microbiota.

The present invention provides an immunodynamics generation device that leads to the management of a health condition, and early detection, early treatment, and optimal treatment of a disease. This immunodynamics generation device comprises at least one immunodynamics processing unit, a storage unit that stores at least clinical data, and a display unit. The immunodynamics processing unit implements control to acquire basic clinical features and immune subgroup dynamics information on the basis of the clinical data, carry out monitoring simultaneous to the formation of a classification model by implementing self-learning on the basis of the immune subgroup dynamics information and the classification model, and compare the overall relationship between the basic clinical features and the immune subgroup dynamics information on the basis of the classification model, thereby evaluating the immunodynamics information of a subject, and simultaneous thereto, displaying the evaluation results of the immunodynamics information on the display unit.

The invention discloses a apoplexy patient monitoring system based on cell therapy and negative oxygen ion conditioning, and particularly relates to the technical field of medical monitoring. According to the method, the multi-dimensional physiological parameters of the patient are converted into the quantized physiological expression coefficients, the scheme adjustment signaling is accurately triggered, and the optimal treatment scheme is matched from the mapping library based on the monitoring data and the individual information; wherein the mapping library comprises a mapping relationship between different physiological parameter combinations and corresponding cell therapy schemes and negative oxygen ion conditioning scheme combinations, and then evaluating scheme effects through subsequent monitoring data to determine whether to optimize the schemes, so that monitoring and treatment are deeply coordinated, and the treatment effect is improved. The problems that most of apoplexy patient monitoring systems in the prior art can only achieve a single physiological parameter monitoring function and are not effectively combined with treatment means, and a treatment scheme cannot be made according to the real-time physiological state of a patient are solved.

The invention relates to the technical field of data management, in particular to a first-aid data intelligent management sharing platform based on the Internet of Things. The acquisition module is used for acquiring a target first-aid data set of a target first-aid demander, a historical first-aid data set of a historical first-aid demander and a to-be-matched hospital set of the target first-aid demander; and the second acquisition module is used for acquiring the target matching degree corresponding to each hospital in the to-be-selected hospital set, and taking the hospital corresponding to the maximum target matching degree as the emergency treatment matching hospital of the target emergency treatment demander. And the probability that the first-aid demander cannot obtain effective first aid within the optimal treatment time can be reduced.