305 results about "Disease course" patented technology

The invention discloses a children MPP (Mycoplasma Pneumoniae) auxiliary diagnosis system based on multi-modal time series data modeling, and aims to improve the auxiliary diagnosis, clinical typing and severe prediction capabilities of mycoplasma pneumonia and reduce the workload of doctors. The MPP dynamic representation method based on multi-modal time sequence modeling is innovatively provided, multi-modal data such as images, electronic medical records and inspection indexes are fused, the time sequence feature embedding technology is adopted, and the evolution trend of the disease course can be effectively captured. Furthermore, deep fusion of different modal data is realized by using a progressive cross-modal semantic alignment strategy and a modal attention gating mechanism, and the problem of insufficient diagnosis accuracy caused by incompatibility of data modals in a traditional method is solved.

The invention relates to a disturbance of consciousness disease course monitoring system. The multi-dimensional data acquisition module is used for acquiring electroencephalogram (EEG), heart rate variability (HRV) and skin conductance (EDA) physiological signal data of a patient in real time through a plurality of sensors arranged on the body of the patient, and preprocessing the acquired data through edge computing equipment; the self-adaptive disease course prediction model is based on the physiological signal data acquired by the multi-dimensional data acquisition module, combines the historical disease course data of the patient, trains a personalized disease course prediction model by utilizing a self-adaptive algorithm, and adaptively updates model parameters through a machine learning algorithm in the disease course development process of the patient, so that the disease course prediction accuracy is improved. The dynamic prediction of the progress of the disturbance of consciousness is realized; the continuous disease course monitoring module continuously obtains physiological signals of a patient through a high-frequency sampling technology, recognizes key events in the disease course in real time through an anomaly detection algorithm, the key events comprise sudden consciousness decline and epileptic seizure, generates early warning signals and triggers an emergency response mechanism.

The invention relates to the technical field of remote medical monitoring, in particular to a medical patient remote monitoring system which comprises a health monitoring module, a disease course prediction module, a physiological regulation module, a medicine management module, a behavior analysis module, a therapy customization module, a dose adjustment module and a system comprehensive management module. According to the invention, accurate physiological monitoring is realized through a data fusion algorithm and a noise filtering technology, the accuracy and reliability of data are ensured, the recurrent neural network model can provide more accurate disease development trend analysis, early diagnosis and timely intervention are facilitated, and the accuracy and reliability of the system are improved. A self-adaptive control algorithm and a biological feedback technology enable a patient to more effectively adjust the physiological state by themselves, the treatment initiative is improved, and the safety and effectiveness of drug treatment are improved through a mode recognition algorithm and drug interaction analysis of drug management. Through deep data mining and personalized treatment strategies of the behavior analysis module and the therapy customization module, a more comprehensive and customized treatment scheme is provided for the patient.

The invention discloses a whole course rehabilitation management system and management method based on an AI technology, and relates to the technical field of medical health management. The system mainly comprises a medical record file module, a rehabilitation management module and a doctor-patient communication module. Personal medical records of a patient are stored and managed through the structured information storage technology, free interaction between the system and a doctor-patient terminal is achieved through the doctor-patient communication module, a channel for timely consultation and feedback is provided for the patient, and doctor-patient communication is promoted. Rehabilitation schemes of different disease course stages are provided for each patient through the rehabilitation management module, a rehabilitation file of the next stage is adjusted according to execution data of the patient in each stage, and the whole disease course of the patient is throughout until complete rehabilitation is achieved. Personalized whole course rehabilitation management for different patients is realized, and the overall efficiency and the service quality of medical services are improved.

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 discloses a tumor whole course intelligent management platform and method, and particularly relates to the technical field of medical model management. Carrying out semantic analysis and uniform format processing on the multi-modal data from different sources by utilizing a natural language processing technology and a standardized tool; using a feature selection algorithm to screen key features related to tumor types and treatment schemes; through analyzing mutual information of the key features and the target variables and mutual information among the key features, evaluating the degree of redundant hidden dangers existing in the screened key features; the semantic consistency of the multi-modal data from different sources is evaluated by comparing and analyzing the prediction label and the actual label of the multi-modal data; and comprehensively analyzing the redundant hidden danger degree of the screened key features and the semantic consistency of the multi-modal data from different sources, and judging whether the personalized treatment effect prediction model can be constructed or not.

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 a large model hospital infection judgment method and system based on an intelligent agent, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining a target physiological feature of a target patient; the target physiological feature at least comprises one of a disease course description, an examination result and an inspection result of the target patient; retrieving a hospital infection knowledge map according to the target physiological features to obtain a corresponding target sub-map; wherein the hospital infection knowledge graph is constructed according to a hospital infection diagnosis standard document; generating a relation vector set according to the target sub-atlas to obtain a target relation vector set; and inputting the target physiological features and the target relationship vector set into a pre-trained hospital infection prediction model to obtain a target infection outcome, thereby reducing the data set screening difficulty of the hospital infection prediction model, and improving the prediction accuracy of hospital infection prediction.

The lung analysis system based on the parameter response diagram and the image generation system is realized. The system is composed of a data collection and management module, an image registration module, a controllable image generation module, a parameter response diagram analysis module and a disease course development evaluation module. The data collection and management module is in butt joint with a hospital information system and CT image collection equipment. The image registration module receives a double-phase CT scanning image of a patient to obtain a registered double-phase CT image; the controllable image generation module receives a single inspiration phase CT scanning image of a patient as input to generate a registered expiration phase CT scanning image; the parameter response diagram analysis module obtains multiple parameter response diagram analysis and evaluation results of organic lesion evaluation of the lung of the patient; the disease course development evaluation module predicts the lung function condition of the patient. Therefore, the problems that when a parameter response diagram method is used, biphasic CT collection is difficult, and the ability to evaluate the disease course development of a patient is lacked are solved.

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.

The invention relates to an intervertebral disc degenerative change analysis system based on multi-dimensional information. The system comprises a data processing module, a modifiable factor processing module, a circular RNA data processing module, a biomarker processing module, an image processing module, a treatment information processing module, a diagnostic feature anchoring module, a model generation module and a change analysis module. Anchoring is carried out on circular RNA, biomarkers, treatment information and modifiable factors through image information to form an anchoring network, and a reliable and effective basis can be provided for disease course analysis through big data and a large number of research results, so that each doctor has a targeted expectation and understanding on the disease course of a patient in the diagnosis stage; the method has the advantages that an interventional therapy scheme can be arranged in advance or the condition of a patient can be observed in real time, research results can be collected and fed back in time through big data, the information utilization rate is increased, and an information dimension integration scheme for degenerative disease recognition is created, and the accuracy of recognition results is 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 discloses an intelligent old-age care service supervision system and method for old-age care, and belongs to the technical field of intelligent supervision. Constructing a community old-age care database, wherein the community old-age care database comprises community data and community old-age care data; constructing an activity area set and a community elderly set; marking the activity area, and calculating the weight of the activity area in the same time period; extracting community elder rescue data of the activity area, calculating a safety coefficient of the activity area, and pre-distributing the number of nursing personnel in the activity area; through user authorization, disease course logs of the community elderly are extracted, a disease condition assessment model is constructed, and the disease conditions of the community elderly are assessed; calculating the to-be-rescued risk probability of the community old people; calculating predicted values of to-be-rescued risk probabilities of all community old people in the activity area, generating a risk monitoring sequence in the activity area, and sending the risk monitoring sequence in the activity area to the caregiver; therefore, the quality of the pension service is improved.

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 discloses a disease process twinborn construction method and system based on a standard electronic medical record, and the method comprises the steps: obtaining electronic medical record data based on a standard FHIR electronic medical record of a patient; encoding and processing the electronic medical record data to obtain multivariable time sequence data; constructing a twinborn model in the disease process; acquiring a disease trajectory prediction report; a GPT model is introduced, numerical information in the disease trajectory prediction report and semantic information of the knowledge graph are converted into natural language description, and a report template is obtained; adjusting the report template; and generating a personalized medical report including the risk early warning information and the intervention suggestions. According to the method, the disease course of the patient with the diabetes complicated with the coronary heart disease can be subjected to individualized prediction by utilizing the constructed twinborn model in the disease process. By inputting personal information, medical history, laboratory examination and other data of a patient, the model can predict the disease development trend, the treatment effect and the occurrence risk of complications of the patient.

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 a chronic disease patient nursing management system based on big data, which relates to the technical field of medical nursing and comprises a disease course development analysis unit, a patient health analysis unit, an online diagnosis and treatment unit, a nursing management unit, a nursing recommendation unit and a nursing correction unit. According to the invention, the disease course development model of the chronic disease is constructed according to the disease course development data, the disease development evaluation trend is graded and judged according to the physiological data of the patient, the judgment result is corrected through the online diagnosis and treatment unit, and the matched nursing list is obtained according to the judgment result. The nursing list is sent to a remote medical terminal of a nurse and an intelligent mobile terminal of a family member of the chronic disease patient, physiological data of the patient is obtained after each nursing judgment period is completed, the correction trend of the nursing influence coefficient is judged, and the effect of the nursing scheme is evaluated. The nursing list is timely corrected according to the chronic disease development process of the patient, the disease development condition of the patient is better matched, and chronic disease deterioration is inhibited.

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 discloses an application of a benzotriazolyl piperazine compound or a salt thereof in preparation of a medicine for treating pulmonary arterial hypertension, and belongs to a new application of the benzotriazolyl piperazine compound. 3-[4-[4-(1H-benzotriazole-1-yl) butyl] piperazine-1-yl] benzisothiazole hydrochloride (PH-001) is subjected to an in-vivo animal experiment; mainly based on pharmacological experiments in a rat pulmonary arterial hypertension model induced by monocrotaline, it is found that the right ventricular systolic pressure (RVSP), the right ventricular hypertrophy index (RVHI) and the pulmonary arterial wall thickness (PAWT) of a rat are all remarkably reduced after intervention of a test sample PH-001, and the technical scheme opens up a new way for clinical application in the field of pulmonary arterial hypertension treatment. Therefore, PH symptoms and disease course development are effectively relieved, a theoretical basis is provided for clinical development, and the traditional Chinese medicine composition is expected to become a new effective treatment strategy.