652 results about "Patient Base" patented technology

The invention provides a dynamic myopia management lens prescription customization method and system, and relates to the technical field of lens design and customization. According to the dynamic myopia management lens prescription customization technology provided by the invention, ophthalmic basic data of a patient is acquired; then periodically follow-up the patient to obtain ophthalmology measurement follow-up data of the patient; the joint evaluation model dynamically updates a myopia risk evaluation grade of the patient based on ophthalmology basic data and ophthalmology measurement follow-up visit data; and finally, dynamically updating the lens prescription based on the myopia risk assessment level of each time by the joint assessment model. According to the technical scheme, the combined evaluation model can perform dynamic lens prescription adjustment according to follow-up visit ocular axis progress and choroid change conditions, dynamic myopia management lens prescription customization, whole-course tracking of a myopia cycle and individualized prevention and control management are realized, and thus the effectiveness and continuity of myopia prevention and control are improved.

The invention provides a medication guidance generation system, and relates to the technical field of medical informatization. Comprising a knowledge base construction module used for constructing a medication guidance knowledge base; the graph term construction module is used for constructing a knowledge graph and a standard term set according to the medication guidance knowledge base; the index construction module is used for splitting the medication guidance knowledge base and constructing a vector index and a full-text index; the personal information processing module is used for acquiring personal information of a patient and preprocessing the personal information to obtain preprocessed personal information; the mixed detection module is used for performing retrieval by utilizing a knowledge graph, a vector index and a full-text index according to the preprocessed personal information to obtain a retrieval result set and obtain an intersection result according to the retrieval result set; and the generation module is used for generating medication guidance corresponding to the target patient according to the intersection result. The problems that traditional medication guidance mainly depends on experience and professional knowledge of medical staff, and is low in efficiency, prone to errors and difficult to personalize are solved.

The invention relates to the field of medical health and artificial intelligence, and discloses a cancer patient low-molecular heparin individualized dose dynamic optimization method based on reinforcement learning, and the method comprises the steps: S1, collecting multi-dimensional clinical data of a cancer patient, carrying out the preprocessing, carrying out the enhancement of the preprocessed data, and generating time sequence synthesis data; s2, based on the multi-dimensional clinical data and the time sequence synthesis data, constructing a reinforcement learning environment, including defining a state space, an action space and a reward function; s3, constructing an environment adaptive deep reinforcement learning EA-DRL model as a dose optimization model; performing model training and optimization in the reinforcement learning environment until convergence and obtaining an optimal dose decision strategy; and S4, inputting real-time dynamic clinical data of a patient to be decided into the trained dosage optimization model, and outputting individualized low molecular heparin LMWH administration dosage recommendation. Individualized precise administration is realized, and the treatment effect and the clinical medication safety are improved.

The invention relates to the technical field of psychosomatic health management, in particular to a psychological immune system evaluation system and intervention method for cancer patients. And the data acquisition module is used for acquiring and fusing the immune biochemical indexes of the patient, the continuous physiological time sequence signals generated by the wearable device, the digital psychological assessment data and the clinical treatment stage marks, and outputting a structured multi-dimensional time sequence data set through time alignment and feature extraction operation. Through multi-modal data fusion and integrated machine learning analysis, the psychosomatic health risk of the patient can be predicted based on the continuous physiological and psychological data change trend of the patient, and corresponding intervention measures can be triggered before the patient has obvious clinical symptoms by outputting the quantized risk coefficient and the multi-stage early warning signal, so that the accuracy of the patient is improved. Therefore, an intervention mode is promoted to be converted from traditional intervention after evaluation to predictive and preventive intervention.

The invention provides a digital twinborn medical education training method and system. The method comprises the following steps: generating case information and scoring rules of AI digital patients based on clinical real cases; constructing a physical training environment which comprises a training model, an environment construction object and an operation sensing device; creating a digital training environment mapped with the physical training environment in real time, wherein the digital training environment comprises an AI digital dummy and an interactive operation process; responding to operation input of the trainee in the physical training environment or the digital training environment; through a server synchronization engine, real-time bidirectional interaction between entity operation of the physical training environment and virtual operation of the digital training environment is realized; and dynamically collecting cross-environment operation data, and generating a clinical ability evaluation report based on case information and scoring rules. The method has the beneficial effects that a medical education evaluation-diagnosis-treatment full operation chain is deeply fused, the clinical practice ability is remarkably improved, and medical risks are effectively prevented and controlled.

The invention relates to a hierarchical diagnosis and treatment resource allocation system based on an AI priority pre-auditing mechanism, and belongs to the technical field of medical resource allocation, and the system comprises a user input module which is used for receiving initial information of a patient based on an AI applet, and the initial information at least comprises a symptom description, a medical history record, examination data and a diagnosis and treatment stage; the AI intelligent pre-inquiry module is used for performing multiple rounds of questions and answers on the patient according to the initial information to extract an illness state index, inputting the illness state index into a preset risk layering model, and determining an illness state severity / urgency index of the patient; the resource distribution module comprises a special number source pool reserved for experts, and the resource distribution module is used for opening expert numbers for the patients whose illness condition severity / emergency index is greater than an illness condition threshold value; and the referral guidance module is used for recommending a replacement doctor seeing scheme for the patient who does not accord with the open expert number. According to the invention, the technical problem of insufficient fairness and efficiency of an illness state emergency degree registration distribution mechanism caused by structural mismatching of expert resources is solved.

The invention discloses a postoperative monitoring system and method for a metabolic weight loss surgery patient, and the system comprises a multi-source data collection module which is used for obtaining physiological parameters, behavior data and environment data of the patient in real time, the physiological parameters comprise body weight, blood sugar and body composition indexes, and the behavior data comprise exercise amount and diet records; the environment data comprises temperature, humidity and geographic position; the central processing platform is connected with the multi-source data acquisition module and comprises a data fusion unit for performing space-time alignment and dimension reduction processing on the multi-source heterogeneous data by adopting a feature level fusion algorithm; the dynamic risk assessment unit is used for constructing a patient personalized metabolism model based on the fused data, and dynamically generating nutrition loss and complication recurrence risk indexes through a machine learning algorithm; and the intelligent early warning module triggers a graded early warning signal according to the risk index, generates a personalized intervention scheme and pushes the personalized intervention scheme to the doctor-patient terminal.

The invention discloses a thoracic surgery patient postoperative remote monitoring method and system based on the Internet of Things, and relates to the technical field of medical rehabilitation monitoring. Feature index extraction is carried out by taking body position pressure and respiration collaborative response as a two-factor dynamic evaluation basis, so that high-sensitivity judgment on a thoracic cavity load state is formed; after the abnormal state is recognized, body position adjustment is carried out, acoustic feedback analysis is linked, secondary evaluation after body position adjustment is achieved, intervention effectiveness of body position adjustment is ensured, personalized adjustment suggestions are automatically generated, the whole process of'perception-recognition-execution 'of remote monitoring is opened, the intelligent level and response real-time performance of postoperative recovery management are improved, and the postoperative recovery management efficiency is improved. According to the overall scheme, health risks caused by position misjudgment, intervention lag and monitoring blind areas in a traditional system are effectively avoided.

The invention discloses a rehabilitation state evaluation system for a patient with language psychological disorder based on multi-agent collaboration, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize comprehensive evaluation of language and psychology and improve the evaluation accuracy and efficiency. The system overcomes the defects of poor dialect adaptation, multi-mode splitting, single evaluation dimension and weak interpretability in the prior art, and meets the clinical requirements of accurate and efficient evaluation on patients with language psychological disorders. The technical scheme is as follows: the system comprises a base model construction unit, a special evaluation agent construction unit and a collaborative scheduling unit, the base model construction unit is used for constructing a base model by taking Transform-XL and Wav2Vec2.0 as a basic framework and fusing a multi-modal attention mechanism so as to realize cross-modal information interaction; and the special evaluation agent construction unit is used for constructing a dialect special evaluation agent, a text special evaluation agent and a chart special evaluation agent based on the base model.

The invention relates to the technical field of digital health, in particular to a personalized rehabilitation exercise method for rheumatoid arthritis patients based on a BCW theory, which comprises the following steps: acquiring joint angle change and calculating a pain delay response coefficient, detecting fluctuation of supporting force and stabilizing force to obtain a joint load tolerance value, analyzing respiratory coordination to generate a respiratory coordination index, and performing rehabilitation training on the respiratory coordination index. The method comprises the steps that a joint angle sensor and pain intensity data are analyzed, a BCW capacity factor weight value is generated in a weighted mode, a fuzzy control algorithm is input to conduct hierarchical mapping adjustment on action amplitude frequency and rest intervals, and exercise adaptability feedback data are generated through monitoring completion degree and pain feedback. The exercise intensity is dynamically adjusted in real time through a data model, a personalized rehabilitation scheme is automatically optimized, the problems that subjective feedback is relied on, and physiological changes cannot be responded in time are solved, more accurate exercise amount adjustment is provided, the situation that the joint burden is increased due to improper exercise intensity is avoided, and the rehabilitation process is effectively promoted.

The invention provides a rehabilitation training feedback system and method. The method comprises the following steps: acquiring a surface electromyogram signal; a plurality of activation trend indexes are determined through the surface electromyogram signals, and then muscle force activation points of different muscle areas are extracted from the surface electromyogram signals according to all the activation trend indexes; performing collaborative analysis on the basis of a function collaborative relationship in combination with all muscle force activation points to obtain time sequence response tensors between all motion joints and corresponding muscle areas when motion responses are inconsistent; according to the joint angle information and the time sequence response tensor, constructing a functional coupling graph, and further performing kinematic chain simulation on a rehabilitation training process through the functional coupling graph to obtain a response consistency index; and performing feedback control on the rehabilitation training process of the patient according to the response consistency index in combination with a preset consistency threshold. By means of the scheme, feedback control can be conducted on rehabilitation training of the patient based on the space-time coupling relation between muscle group microstructure changes and mechanical conduction characteristics.

The invention relates to the technical field of intracranial pressure monitoring, in particular to a craniocerebral injury patient secondary brain injury early warning method based on multi-modal fusion, which comprises the following steps: acquiring intracranial pressure, cerebral blood flow, electroencephalogram signals and brain tissue oxygen partial pressure signals, carrying out denoising and normalization processing, constructing a multi-modal normalized time sequence data set, and carrying out multi-modal fusion on the basis of the multi-modal normalized time sequence data set; and inputting a linear discriminant analysis model to generate a time sequence difference vector, constructing a difference driving map based on a Bayesian dynamic network model, identifying abrupt change nodes, outputting a risk early warning section set, and carrying out trend stability analysis and reconstruction optimization. According to the method, the abnormal state recognition sensitivity is improved through normalized processing of multi-modal signals, enhancement of signal fusion capability, quantitative capture of direction change, reconstruction of spatial relationship between signals, modeling of dynamic transition probability, recognition of abrupt change nodes, combination of time difference vector change, evaluation of signal linkage trend, positioning of risk sections and extraction of parameter features; and the early warning and intervention precision is improved.

Techniques for early prediction of medical billing classification codes and associated medical billing costs using routine clinical text are disclosed. The system predicts the medical billing codes within defined hours of admission by generating vector embeddings from a set of medical notation data, bypassing the need for post-discharge medical codes. Using a novel segmentation technique, the system processes lengthy medical notation data by dividing them into smaller subsequences. These subsequences are input to a large language model (LLM) to generate a plurality of sets of probability values for a set of medical billing classifications. The system selects a particular predicted medical billing classification for the patient based on the sets of probability values. Additionally, the system estimates medical billing costs early in the admission process. The system ensures comprehensive context utilization from clinical notes, enabling hospitals to manage treatment expenses proactively and improve operational efficiency.

A method of operating an orthopaedic surgical system may include displaying a unique identifier on a visual display of a first device positioned in an operating room, the unique identifier being associated with a digital medical record of a patient positioned in the operating room, capturing the unique identifier with a camera of a second device positioned in the operating room, establishing a unique electronic connection between the second device and the digital medical record of the patient based on the unique identifier, capturing one or more images of the patient with the camera of the second device, transferring the one or more images from the second device to the digital medical record of the patient using the unique electronic connection, and accessing the one or more images with the first device to guide performance of an orthopaedic surgical procedure on the patient.

The invention relates to the technical field of data processing, in particular to a rehabilitation training control method and system based on a flexible hand joint rehabilitation glove, and the method comprises the steps: obtaining an individualized physiological parameter baseline of a patient; real-time data of a patient in the rehabilitation training process are collected through a multi-modal sensor array on the flexible glove, the active participation degree, the joint movement smoothness and the muscle fatigue degree of the patient are calculated based on the real-time data, the active movement intention of the patient is recognized, and a real-time rehabilitation state evaluation result of the patient is obtained; generating a control instruction through an adaptive control engine according to a preset personalized rehabilitation scheme and the real-time rehabilitation state evaluation result; the generated control instruction is sent to a driving control circuit so as to control a driving unit to execute corresponding bending or stretching actions and assist the patient in completing target training actions; according to the invention, personalized, self-adaptive and quantitative evaluation of rehabilitation training can be realized, and the efficiency and effect of hand rehabilitation training are improved.

The invention discloses a data mining technology-based construction method and application of an elderly diabetic hypoglycemia risk prediction model, and belongs to the technical field of medicines and computers. The method comprises the following steps: selecting elderly diabetic patients meeting standards, collecting demographic, disease and lifestyle factor data, dividing a training set and a verification set, comparing the performance of five data mining models, screening an optimal model, confirming the effectiveness of the model through external verification, and visualizing the result. The model can output a quantitative risk probability, is suitable for multi-scene real-time evaluation, can accurately identify high-risk groups, guides hierarchical intervention, reduces the incidence rate of hypoglycemia, and improves the management efficiency and safety of elderly diabetic patients.

The invention provides a coronary heart disease recurrence risk assessment method and device, equipment and a storage medium, and relates to the technical field of medical data processing. The method comprises the following steps: acquiring dynamic behavior data, physiological data and static risk indexes of a target patient; calculating a treatment compliance index and a rehabilitation health index of the target patient based on the dynamic behavior data and the physiological data of the target patient; and inputting the treatment compliance index, the rehabilitation health index and the static risk index into a trained coronary heart disease recurrence risk scoring model to obtain a coronary heart disease recurrence risk score of the target patient. According to the method, objective and quantitative recurrence risk scores can be obtained, more accurate decision support is provided for clinicians, and early warning and personalized intervention can be realized, so that the prognosis of patients is improved, and the medical cost is reduced.

The invention relates to the technical field of intelligent medicine recommendation, and provides a medicine recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the time sequence coding processing of the current treatment information of a patient, and generating a semantic vector of the current treatment information; performing multi-message propagation processing on the heterogeneous medical graph based on a graph convolutional neural network to determine knowledge memory vectors of various medical entities, and determining knowledge vectors of the current doctor seeing information based on the knowledge memory vectors; determining fusion information according to the semantic vector and the knowledge vector; and inputting the fusion information into a drug recommendation model, carrying out probability calculation processing that new drugs are used and probability calculation processing that historical drugs are reserved and used on the fusion information, and based on the determined newly-added use probability of each drug, the reserved use probability of each historical drug and an adjacent matrix of drug interaction, carrying out drug recommendation. And determining the drug combination recommended to the patient. The safety of medicine combination is improved, and the accuracy of medicine recommendation is effectively improved.

The invention discloses a central vein vascular access complication risk management method and related equipment. The method comprises the steps that based on basic physiological information and edema related information of a patient, ideal pressure values of different edema parts of the patient are determined through a treatment pressure decision model, and the treatment pressure decision model is obtained through training according to historical clinical treatment data; displaying the ideal pressure values of the different edema parts of the patient; in the binding process, the actual pressure value between the bandage and the skin of the patient is obtained, and the actual pressure value is displayed. The problems that in the prior art, complication supervision of a patient after a central venous catheter is implanted through a peripheral vein and infusion port implantation stays in static inquiry, the response speed is low, emergency situations cannot be recognized in time, the data collection mode is single, and the evaluation accuracy is insufficient can be solved.

The invention relates to the technical field of data processing, and provides an intelligent community health management system for senile chronic disease patients based on a radar map, and the system comprises the steps: regularly obtaining the monitoring data of a plurality of health indexes of the senile chronic disease patients; obtaining standardized data of each health index of the patient; determining the illness state reflecting degree of each health index of the patient; generating a radar map of each time of monitoring of the patient; determining the intervention degree of each monitoring of the patient according to the difference of the adjacent monitoring radar maps of the same patient and the change trend of the continuous monitoring radar maps; the medication adjustment degree of the patient is obtained, and then the illness state change degree of the patient is obtained; based on the disease change degree and the medication adjustment degree of the patient, obtaining health management suggestions for the patient; and the hospital timely shares the health management suggestions to the community after obtaining the health management suggestions of the patient. The objective of the invention is to solve the problem that health state monitoring management is affected by difference of radar map expressions caused by different illness states of patients.

The invention relates to the technical field of health management, in particular to an obese patient remote dynamic intervention system based on AI early warning, which comprises a multi-dimensional data acquisition module, an AI risk layering and early warning module, a personalized intervention scheme generation module, a remote collaborative management module and an effect evaluation and optimization module, the multi-dimensional data acquisition module is used for acquiring patient related data; the AI risk layering and early warning module constructs a multi-dimensional dynamic score card model to carry out risk scoring and layering, and obesity health risk early warning is generated; a personalized intervention scheme generation module constructs a multidisciplinary intervention strategy matrix, and dynamically generates an intervention scheme according to an early warning result and individual features of a patient; the remote collaborative management module realizes information interaction and collaborative intervention of multi-party resources; and the effect evaluation and optimization module tracks the intervention effect and optimizes the model and the intervention scheme. Therefore, the problems of data lag, intervention scheme solidification, untimely early warning and the like in obese patient management are solved.

The invention discloses an acupuncture robot meridian point positioning system based on digital twinning, and belongs to the technical field of acupuncture, and the system comprises a data acquisition digital modeling module which is used for obtaining body surface visual data of a patient, reconstructing a 3D digital model of the patient, and creating an initial digital twinning model of the patient; the treatment planning simulation rehearsal module is used for performing diagnosis and acupuncture treatment scheme design by a doctor and simulating and rehearsal the whole acupuncture process; and the virtual-real synchronous accurate positioning module is used for controlling the acupuncture robot to move above the target acupuncture point, tracking the tiny movement of the patient in real time, synchronously updating the patient digital twin model and the target position of the acupuncture robot, and realizing the dynamic tracking and accurate positioning of the meridian acupuncture point. The problem that the acupuncture treatment efficiency of a patient is low due to the fact that accurate positioning of meridian points of the patient cannot be achieved based on digital twinning in the prior art is solved. Precise positioning of the meridian points of the patient can be achieved based on digital twinning, and the acupuncture treatment efficiency of the patient can be improved.

The invention discloses a doctor-patient intelligent matching method and system fusing real-time doctor-seeing information, and relates to the field of intelligent medical treatment, and the method comprises the steps: obtaining pre-stored personal information, and recognizing the doctor-seeing intention of a patient based on symptom description information and the personal information; multiple rounds of dialogue questions are sent to the user terminal, and detailed disease information is obtained; generating question options based on the detailed disease information, and sending the question options to the user terminal to receive click feedback performed by the patient through the user terminal; analyzing the disease information of the patient based on the detailed disease information and the click feedback, and determining a preliminary diagnosis and treatment result; recommending a department list for the patient based on the preliminary diagnosis and treatment result, and determining the matching degree between each department in the department list and the disease; and according to the matching degree and the preliminary diagnosis and treatment result, determining a target doctor, obtaining a real-time residual number source number of the target doctor, and sending the target doctor and the corresponding real-time residual number source number to the user terminal. According to the invention, the matching accuracy can be effectively improved.

The invention relates to the technical field of remote monitoring, in particular to a chronic respiratory disease patient remote monitoring system based on the Internet of Things, which comprises a physiological baseline initialization module, an event response delay measurement and calculation module, a cardiopulmonary coupling phase quantization module and a decompensation risk early warning module. According to the method, an individual blood oxygen and cardiopulmonary coupling health baseline is established to provide accurate reference for subsequent analysis, then the blood oxygen recovery time after apnea is measured and calculated, the physiological response delay characteristic of an individual is quantified, and the characteristic is used for setting a dynamic analysis window to continuously calculate the phase difference between a breathing mode and a blood oxygen signal, so that the phase difference between the breathing mode and the blood oxygen signal is calculated. By continuously comparing the current phase difference with the individual health baseline and triggering early warning when the difference value continuously exceeds a specific range, the early sign of the instability of the cardiopulmonary system can be effectively captured, the conversion from passive response type alarm to active predictive early warning is realized, and a precious time window is provided for preventing decompensation.

The invention relates to the technical field of treatment and healing of depression patients, and discloses a depression patient AI treatment and healing interaction and note generation method based on natural language processing, which comprises the following steps: step 1, constructing a natural language processing model; step 2, psychological assessment and emotion analysis; 3, a personalized chemotherapy healing interaction strategy; step 4, a note generation function; and 5, evaluating and feeding back the treatment effect. According to the depression patient AI healing interaction and note generation method based on natural language processing, depression healing is converted into an online reinforcement learning problem, the rehabilitation effect of the patient is taken as an instant reward, an interaction strategy is taken as an action space, the quality of a strategy library is automatically improved through continuous iteration, and compared with static script type dialogue, the method has the advantages that the efficiency is greatly improved; the system can dynamically adjust parameters according to three factors of emotion level improvement, interaction duration and satisfaction, achieves the purpose of'understanding patients more than using ', enables a strategy library to be periodically and globally retrained after a large amount of crowd data is converged, remarkably shortens the single-time healing effectiveness time, and improves the overall rehabilitation efficiency.

An ultrasound-imaging system is disclosed that includes an ultrasound probe and a console. The ultrasound probe includes an array of ultrasonic transducers configured to emit generated ultrasound signals into a patient, receive reflected ultrasound signals, and convert the reflected ultrasound signals into corresponding electrical signals of the ultrasound signals for processing into an ultrasound image, and a component configured to obtain positional-tracking data as the ultrasound probe moves along the patient from a starting position to an end position. The console includes one or more processors and a computer-readable medium storing logic that, when executed, causes operations including: receiving and processing the electrical signals to generate the ultrasound image, receiving the positional-tracking data, determining metrics of a path traveled by the ultrasound probe, and providing insertion recommendations corresponding to a medical device to be inserted into the patient based on the metrics of the path traveled by the ultrasound probe.

The invention provides a breast cancer patient pCR prediction classification method based on image-gene interpretability deep learning. The method comprises the following steps: dividing an obtained data set into a training set and a test set; splitting all the three-dimensional MRI images into two-dimensional slice images; the gene text features of each breast cancer patient are classified into N different sets; segmenting a two-dimensional slice image corresponding to the training set into a plurality of patches; each patch and the gene text feature corresponding to each patch are converted into a corresponding high-dimensional Embedding (high-dimensional Embedding); respectively multiplying H by Wk and Wv to obtain K and V; multiplying G by WQ to obtain Q; obtaining a gene guidance image feature CoAttn; the CoAttn and the Q are fused to obtain a feature set F; inputting the F into a network model, and training the network model based on a loss function; inputting the two-dimensional slice image corresponding to the test set into the trained network model, and outputting a plurality of classification results; and adopting a maximum voting strategy to make decisions on the plurality of classification results. According to the method, pCR and npCR classification is carried out on the patient based on a co-attention mechanism of gene text features and MRI images, the interpretability of model decision is enhanced, and a more visual and reliable auxiliary diagnosis tool is provided for clinicians.

The invention discloses a correlation analysis method for sleep disorder and cardiovascular adverse events of an MINOCA patient, relates to the field of clinical decision of cardiovascular diseases, and aims to perform anti-factual reasoning on data of a specific patient based on a structural causal model and simulate the correlation between sleep disorder and cardiovascular adverse events of the patient under the condition of changing one or more sleep characteristic variables. The theoretical change value of the cardiovascular adverse event risk of the patient is calculated, and the expected risk reduction benefit of intervention is calculated; and generating a decision support report, and performing visual display. According to the method, by means of time sequence causal discovery and a structural causal model, the causal relationship and effect quantification of sleep features, intermediary indexes and adverse events are defined, and a core action mechanism is revealed; a personalized intervention effect is simulated through anti-factual reasoning, and a precise risk prediction and intervention basis is provided for clinic; the visual decision support report reduces the clinical application threshold, assists in optimizing the personalized treatment scheme of the MINOCA patient, and effectively reduces the risk of cardiovascular adverse events.

The invention relates to a multi-mode intelligent internal arteriovenous fistula monitoring and early warning method and device and computer equipment. The method comprises the following steps: acquiring acousto-optic pressure and temperature information of internal arteriovenous fistula of a patient, and identifying data change distribution information corresponding to each monitoring type of the internal arteriovenous fistula based on the acousto-optic pressure and temperature information; based on the data change distribution information corresponding to each monitoring type, identifying an internal fistula state analysis result of the internal arteriovenous fistula through a data analysis strategy of each monitoring type; and on the basis of the internal fistula state analysis result of the internal arteriovenous fistula, predicting internal fistula state trend information of the patient, and on the basis of the internal fistula state trend information of the patient, generating an internal fistula early warning result of the patient. By adopting the method, the monitoring comprehensiveness of medical staff on internal arteriovenous fistula can be improved.

A prediction system includes: a terminal; and a processor, communicatively coupled to the processor. The processor is configured to: acquire medical treatment information including images captured at at least one of before, during, immediately after, and after a predetermined period from treatment of a patient with a symptom of a vascular disease; classify a condition of the patient based on a classification image; learn the medical treatment information in association with the condition of the patient; and predict at least one of a treatment effect and a prognosis when treatment intervention is performed for the patient based on a learning result.