306 results about "Personalized treatment" patented technology

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 medical data analysis, in particular to a brain tumor curative effect analysis system which comprises a tumor data sensing layer for collecting multi-department diagnosis and treatment data, tumor image data and patient pathology monitoring data; the tumor feature processing center extracts data features, correlates data and core features before and after treatment through an attention mechanism, and generates a tumor complete-cycle unified feature map; the therapeutic effect dynamic analysis unit evaluates the therapeutic effect in stages, and outputs a therapeutic effect index and a recurrence risk value through a self-supervised model; the dynamic adaptation decision module is used for generating personalized treatment adjustment suggestions based on the dynamic change of the blood brain barrier in combination with the curative effect index, the recurrence risk value and the multi-omics characteristics of the patient; and the AI multi-department consultation unit automatically matches similar cases with field expert suggestions, and formulates a target diagnosis and treatment scheme based on a visual platform and multi-department doctor collaborative consultation in combination with personalized treatment adjustment suggestions. Therefore, the problems of lagging effect evaluation, insufficient diagnosis and treatment suggestions and the like in the prior art are solved.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

The invention relates to the technical field of medical information processing, in particular to a type 2 diabetes precise typing and dynamic treatment system which comprises a clinical feature extraction module, a metabolism dynamic modeling module, a precise typing module, a treatment parameter generation module and a dynamic treatment output module. Wherein the clinical feature extraction module is used for collecting a multi-dimensional clinical data set of a patient for continuous 14 days; the metabolism dynamic modeling module is used for extracting blood glucose fluctuation characteristics and pancreas islet function compensation characteristics; the precise typing module is used for dividing metabolism-driven subtypes to which the patients belong through a clustering algorithm; the treatment parameter generation module is used for mapping a subtype-treatment rule base and outputting a matched personalized treatment parameter combination; and the dynamic treatment output module outputs a dynamic treatment scheme. According to the invention, through a dynamic treatment mechanism based on typing, linkage control of individual difference identification and real-time adjustment of the medication scheme of the type 2 diabetes patients is realized, and the accuracy and adaptability of treatment are improved.

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The invention relates to the technical field of medical treatment, and discloses a knee joint knee protection treatment personalized planning system based on multi-modal data fusion. A data acquisition and preprocessing module of the system acquires a multi-modal data stream of the knee joint of a patient in real time and generates a standardized data packet; the treatment event identification module is used for automatically identifying and verifying key treatment events from the standardized data packet and generating a treatment event sequence; the historical treatment path query module queries a distributed database and extracts treatment modes of similar patients; the graph matching and path generation module is used for calculating the similarity between the current treatment event sequence and a historical treatment mode, generating a fusion treatment path when the similarity exceeds a threshold value and storing the fusion treatment path to the block chain network; the abnormity monitoring and analysis module monitors abnormal event points in the fusion treatment path in real time, triggers a path decomposition mechanism and performs multi-dimensional performance analysis; and the personalized scheme generation module is used for dynamically adjusting treatment parameters and generating a personalized treatment planning scheme.

The invention provides a method for generating a personalized nerve regulation and control stimulation scheme, and aims to generate a precise nerve regulation and control stimulation scheme for an individual through an electroencephalogram evaluation result. The method comprises the following steps: firstly, constructing a scheme library containing a plurality of transcranial electrical stimulation basic schemes; secondly, collecting resting-state electroencephalogram signals of a user, extracting electroencephalogram characteristic indexes related to emotion, cognition and sleep, and comparing the electroencephalogram characteristic indexes with a norm database to judge cognition risks; generating a primary stimulation scheme according to an evaluation result, and if a cognitive risk exists, further detecting that a feature index is abnormal and generating a targeted correction scheme; all the schemes are subjected to priority ranking, and the sequence is determined according to the abnormal severity degree and the clinical weight; and finally, outputting a personalized treatment scheme sequence of one week. According to the method, the accuracy and effectiveness of treatment are improved, high individuation, systematicness, practicability and dynamic optimization potential are achieved, and powerful support is provided for nerve regulation and control treatment.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention discloses a three-section thighbone retrograde intramedullary nail, relates to the field of medical instruments, and provides a detachable and assembled three-section thighbone retrograde intramedullary nail aiming at the problems that an existing thighbone medial condyle retrograde intramedullary nail is poor in adaptability and insufficient in fixing strength. A main nail body of the intramedullary nail is composed of a head section, a middle section and a tail section, original spiral nested structures are adopted at the joints of the sections, and the three sections are tightly connected to form the complete and high-strength intramedullary nail by matching with the hollow interior design and penetrating a titanium cable into a medullary cavity for tightening and automatically rotating and matching the head section and the middle section of the main nail body. And medullary cavity structures and fracture types of different patients can be accurately matched. Compared with a traditional intramedullary nail, the intramedullary nail has the advantages that mounting convenience and structural stability are remarkably improved, intraoperative adjustment time can be shortened, the risk of injury to a medullary cavity of a patient is reduced, and an innovative solution is provided for personalized treatment of distal femur fractures.

The invention discloses injectable double-drug delivery hydrogel and a preparation method thereof, and belongs to the technical field of biomedical materials. According to the method, two nano microspheres with different charges are respectively prepared by utilizing the characteristics of A-type gelatin and B-type gelatin, a negatively charged medicine EGCG is specifically carried by the positively charged A-type gelatin, and a positively charged medicine SAG is carried by the negatively charged B-type gelatin microsphere; and finally, mixing the two gelatin drug-loaded microspheres into a material taking gelatin and zirconium ions as a substrate to form the composite hydrogel. The hydrogel realizes immune regulation and osteogenesis regulation, also realizes long-acting release of the two drugs, effectively solves the problems of bone resorption, difficulty in healing and the like of host tissues through synergistic regulation of inflammation and mobilization of osteogenesis genes, realizes personalized treatment, and improves the clinical treatment effect and efficiency.

The invention discloses a mental stress assessment and intelligent dredging method and system, and belongs to the technical field of digital medical treatment and health informatics. Multi-modal physiological signals are continuously collected through the wearable device, medical diagnosis level pressure state evaluation is carried out based on the personalized physiological baseline, and personalized pressure indexes and levels are generated; secondly, when it is diagnosed that the pressure level exceeds the standard, the system serves as an intelligent decision support system, the environment and schedule information after privacy protection processing is fused, and an optimal grooming action is dynamically selected from a predefined intervention action library and executed; finally, the system serves as a continuous learning system, the intervention efficiency is evaluated in real time according to feedback data of the pressure index after execution, the decision model is updated, and collaborative self-optimization of the diagnosis strategy and the intervention strategy is achieved. According to the method, medical diagnosis, personalized treatment decision and adaptive learning are integrated, and the systematicness, accuracy and intelligent level of mental stress related health problem management are improved.

The invention provides a postpartum physiotherapy instrument personalized treatment scheme recommendation system based on data driving, and belongs to the field of data processing. The method comprises the steps of obtaining postpartum complications contained in a to-be-recommended puerpera and a treated puerpera, index factors of each contained postpartum complication, a real-time data sequence in a physiological dimension, basic disease information in a plurality of dimensions and fixed values in a plurality of body dimensions, and obtaining a reference puerpera; and obtaining a physiotherapy instrument use scheme of the reference puerpera, and combining the distribution of the postpartum complication contained in each reference puerpera and the index factors of the same postpartum complication contained in the to-be-recommended puerpera and the reference puerpera to obtain the priority of each physiotherapy instrument used by the to-be-recommended puerpera. The objective of the invention is to solve the problem that when a physiotherapy instrument usage scheme is recommended to a to-be-recommended patient through a current FP-Growth algorithm, the body difference between different lying-in women is not considered, so that the obtained recommendation scheme is possibly not suitable for the to-be-recommended lying-in women.

The invention relates to the technical field of intelligent wearing monitoring, in particular to a multifunctional head-mounted device for improving anxiety and depression symptoms in the perioperative period, which comprises a main body base, an electric treatment assembly and a power supply assembly are arranged on the main body base, extending ear hooks are arranged at two ends of the main body base, binding straps are arranged on the extending ear hooks, and a nose pad block is arranged at the bottom of the main body base. The electric treatment assembly is in signal connection with an evaluation and control system, heart rate monitoring assemblies are arranged on the extension ear hooks, and an eye movement monitoring assembly is arranged at the bottom of the main body base; a displacement monitoring assembly is arranged on the main body base, and a respiration monitoring assembly is arranged on the nose pad block. According to the invention, physiological parameter monitoring, anxiety and depression assessment and percutaneous nerve stimulation treatment functions are integrated, and multi-node fixed design and real-time dynamic data processing are combined, so that redundant steps depending on preposed medical assessment in the traditional technology are omitted, doctors can remotely and accurately adjust personalized schemes based on dynamic data, and the accuracy of diagnosis and treatment is improved. The anxiety depression intervention efficiency and reliability in the perioperative period are improved.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

The invention belongs to the technical field of medical rehabilitation, and discloses a rehabilitation treatment monitoring system based on multi-modal sensing and AI evaluation. A multi-dimensional skeleton key point confidence evaluation mechanism is established by integrating three types of sensing data of visual skeleton tracking, an inertial measurement unit and pressure distribution, and potential tracking interruption risk frames and error recognition candidate frames are recognized in real time. And a multi-modal fusion correction model is adopted to carry out accurate compensation on error recognition, missing data are reconstructed in combination with a skeleton motion mode memory bank and a bidirectional time sequence prediction algorithm, and a continuous and complete all-day skeleton motion sequence is generated. On the basis of the calculation, the time-phased activity level index and the rehabilitation action completion degree score of the patient are calculated, and an objective and quantitative rehabilitation evaluation basis is provided. According to the invention, the stability and continuity of skeleton tracking in a complex rehabilitation environment are improved, so that a medical team can obtain a real and complete all-day activity portrait of a patient, accurately position a rehabilitation bottleneck, formulate a personalized treatment scheme and promote functional recovery of the patient.

The invention discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and the method specifically comprises the steps: obtaining the brain multi-modal image data of a patient, and extracting a three-dimensional tumor region and a corresponding perfusion parameter; constructing a biological target region hierarchical structure containing a tumor overall region and an anoxic subregion; a relative hypoxia degree parameter is obtained by calculating the cerebral blood flow ratio of the hypoxia subregion to other regions of the tumor; historical treatment case data are deconstructed, and a dose-curative effect associated parameter set is established; training and outputting a dose adjustment strategy model for the hypoxia subregion; and generating a final radiotherapy dose planning scheme according to a dose adjustment value output by the model in combination with a clinical guide basic dose. Through a data-driven dose decision-making mechanism, the limitation of traditional uniform dose irradiation is overcome, so that an objective and reliable evaluation basis is provided for clinical selection and optimization of personalized treatment schemes.

The invention provides a postoperative bleeding risk monitoring method and system for a hepatobiliary patient. The method comprises the following steps: collecting real-time multi-modal data; constructing an LSTM-CNN hybrid model by using the time-frequency decomposition features, and obtaining a local tissue hypoxia index and a vasomotor function anomaly probability; the low-frequency impedance change rate and the albumin level are fused through a random forest algorithm, and the ascites occurrence probability and the effusion amount predicted value are obtained; and generating a bleeding point positioning coordinate and a thermodynamic diagram risk grade. And calculating a comprehensive bleeding risk probability and positioning a bleeding area. And generating graded early warning signals and recommending personalized treatment schemes or nursing suggestions. According to the invention, the LSTM-CNN hybrid model, the random forest algorithm and the three-dimensional convolutional neural network are adopted to deeply extract different data features, so that the limitation of single index evaluation is avoided. A causal relationship model is established through the Bayesian network, and the comprehensive risk probability calculation preciseness is improved; the early warning threshold is dynamically adjusted by combining the individual characteristics of the patient, and the traditional problem of easy misjudgment is solved.

The invention discloses an in-vitro fertilization-based method and system for predicting the cumulative live productivity of multiple complete cycles, and the method comprises the steps: obtaining clinical data, embryo information and pregnancy follow-up visit data of a patient, and screening and preprocessing a sample in combination with a collection and discharge standard; feature variables are extracted before treatment, after treatment and at the early stage of pregnancy, an optimal set is determined, and key factors influencing live birth at all stages are accurately captured; after the nonlinear variables are processed by the RCS, a pre-treatment / post-treatment COX regression model and a Logistic regression model are adaptively constructed, and the prediction precision is improved by fitting data characteristics; and meanwhile, by evaluating the iterative optimization model, the variable weight is visually displayed by using a Nomogram graph after the model is qualified. The method can accurately predict the multi-cycle cumulative live productivity, assists a doctor in formulating a personalized scheme, helps a patient to reasonably plan treatment, reduces the time and economic burden of blind treatment, and has clinical practical value and benefits of the patient.

The invention discloses a self-driven diagnosis and treatment integrated microneedle array paper-based chip as well as a preparation method and application thereof, and belongs to the technical field of biomedicine. The microneedle array chip system comprises a function integration module which at least comprises a self-powered extraction unit, a sensing detection unit and a controlled drug delivery unit. According to the microneedle array paper-based chip, biomarkers in skin interstitial fluid are efficiently collected based on a self-powered thermoelectric chemical battery microneedle, PtPB (at) PEG (at) HA nanoparticles loaded on an immunochromatography test strip combination pad are triggered to be subjected to ring opening, a hybridization chain reaction is induced to generate DNA nano-enzyme, and TMB is catalyzed to develop color; the open-loop nanoparticles can be used for SERS cross detection of biomarkers, and can be used as a thermal initiator to activate the phase change microneedle under the irradiation of near-infrared light, so that drugs are released as required, and precise treatment is realized. Besides, objective quantitative analysis of color signals is realized by constructing a convolutional neural network model, and the system has the functions of naked eye screening, intelligent quantification and personalized treatment, and can improve the home management ability of early skin cancer.

The invention relates to the technical field of nerve regulation and control, in particular to a depressive state adjusting system and method based on transcranial electrical stimulation, and the method comprises the steps: collecting electroencephalogram signals of left and right brain frontal lobe areas of even-level leads; the method comprises the following steps: performing noise filtering on collected electroencephalogram signals of a left brain and a right brain, extracting 8-13Hz power spectrum data of Alpha waves of the left brain and the right brain, calculating a numerical difference value of power spectrums of the Alpha waves of the left brain and the right brain and an average proportion of the difference value in a test time period, and taking the numerical difference value and the average proportion as quantitative indexes of the depression-associated lateral deviation level; the adaptive transcranial electrical stimulation module is matched through the acquisition module according to basic information such as the age and the medical history of a patient, initial parameters are set, the parameters are dynamically corrected through the control module in combination with real-time physiological signals, accurate personalized treatment is achieved, and treatment safety can be guaranteed.

The invention provides an infrared physiotherapy cabin system and method based on animal and environment cooperative regulation and control, and relates to the field of animal physiotherapy and intelligent monitoring control. Comprising an animal state and cabin environment monitoring module, an infrared physiotherapy and cabin environment regulation and control module, an animal health assessment and curative effect assessment module, a personalized treatment and intelligent decision module and a man-machine interaction and safety supervision module. A credibility result is formed through multi-modal data synchronous alignment and data quality evaluation, health state characterization is constructed under credibility gating, a risk level is output, and infrared physiotherapy control parameters and cabin environment regulation and control parameters are generated through cooperative control under the constraint of a stage control boundary and a treatment target and are issued and executed. Continuously adjustable parameters are subjected to online fine adjustment in the physiotherapy process, and rollback and interlocking protection is executed when the risk is increased or the credibility is reduced; after the period is finished, the curative effect is evaluated, data recording and tracing archiving are completed, and collaborative closed-loop infrared physiotherapy regulation and control of the animal state and the in-cabin environment are achieved.

The invention discloses a daily life activity ability assessment method and system based on a multi-branch space-time fusion network. The method comprises the steps of collecting motion data of a to-be-evaluated object, performing segmentation, noise reduction and standardization preprocessing, and extracting time-frequency domain features and motion deviation degree, motion similarity index and bilateral symmetry index provided by the invention; obtaining a uniform Bhattacharyya index scale score; and taking the motion feature data set and the clinical score as input, performing training based on a multi-branch space-time fusion algorithm, constructing a multi-task prediction model, and realizing automatic evaluation of the ADL. According to the method, the multi-branch space-time fusion network is innovatively introduced, the relevance between tasks is fully utilized, the spatial feature extraction capability is enhanced in combination with the graph neural network, adaptive fusion of the features is realized through a cross-layer interactive attention mechanism, the model prediction precision and evaluation reliability are remarkably improved, and the method is suitable for large-scale popularization and application. And an effective technical support is provided for rehabilitation evaluation and personalized treatment of patients with dyskinesia and disability and dementia.

The invention discloses a sepsis multi-complication prediction method and system, a storage medium and equipment, and belongs to the technical field of intelligent medical treatment, and the method comprises the following steps: S1, collecting multi-dimensional time sequence physiological data of a sepsis patient; s2, constructing a medical perception feature vector for the time sequence physiological data; s3, constructing a sepsis multi-complication prediction model and introducing a physiological logic space-time composite mask matrix; s4, outputting a multi-complication prediction probability by adopting a hierarchical expert hybrid network H-MoE; s5, training the model by adopting a dynamic weighting loss function based on task uncertainty; s6, risk output and feature contribution degree explanation; according to the model, by introducing physiological logic masks and an H-MoE framework, precise and synchronous prediction of complex complications of sepsis is achieved, redundant features can be effectively filtered, complex association between the redundant features can be revealed, quantitative decision support is provided for clinicians, and therefore early intervention and personalized treatment are achieved.

The invention discloses a breathing equipment data processing method which comprises the following steps: synchronously collecting multichannel physiological signals through a built-in sensor of breathing equipment, carrying out noise processing and data standardization, and segmenting according to a time window to form data fragments; extracting breathing amplitude, frequency and morphological variability features of each data fragment, and combining to generate a multi-dimensional dynamic feature vector sequence; constructing a breathing state modeling system based on a hidden Markov model, training model parameters through a Baum-Welch algorithm, and calculating a prospective risk transition score by using a forward algorithm; and establishing an adaptive threshold strategy based on the risk transition score, carrying out structured labeling on the original respiration data, and generating processing data containing a prospective risk early warning label. Technical support can be provided for intelligent upgrading and personalized treatment of respiratory treatment equipment, and the accuracy, predictability and clinical practicability of respiratory anomaly detection are improved.

The present disclosure introduces an AI-based system (100) and method for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) over three months. The system interfaces with consumer devices through REST APIs, ensuring secure communication via HTTPS. Client modules collect and pre-process raw data for integrity and compatibility. This data is then sent to the server (104) which is then transmitted to Input module, the input module (112) authenticates and validates it. The module processes personal details, respiratory history, allergies, vaccination and medication history, and demographic data for air quality analysis. The prediction module (116) uses a CART Model with 90% accuracy to evaluate COPD risk. Based on predefined thresholds, the clinical pathway module (118) and risk prediction response module (120) offer personalized treatment protocols and clinical decision support. The system complies with ISO 13485 standards for safety and reliability.

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 provides a pancreatic cancer multi-modal hierarchical fusion prognosis prediction system and method.The system comprises a data input module, a preprocessing module, a multi-modal feature fusion module and a risk prediction module, and the data input module is used for receiving and managing medical image data, clinical table data and text report data of a patient; the preprocessing module is used for performing standardization processing and feature extraction on input medical image data, clinical table data and text report data; the multi-modal feature fusion module is used for fusing the image features and carrying out hierarchical fusion on the multi-modal features; and the risk prediction module is used for calculating a prognosis prediction result based on the multi-modal characteristics and providing support for clinical decision-making of pancreatic cancer. According to the method, the risk stratification of the patient can be accurately carried out, and a powerful basis is provided for clinically formulating a personalized treatment scheme.

The invention provides an intelligent laser acne treatment system, which comprises a laser module 10, a main control module 40, a laser output device 30, a treatment cooling device 20, an information feedback unit 50 and a temperature control unit 60, and is characterized in that the main control module 40 controls the laser module 10 to generate personalized treatment light beams based on an acne treatment neural network model; personalized treatment light beams generated by the laser module 10 are output through a laser window of the laser output device 30; the information feedback unit 50 collects the real-time temperature of the treatment cooling device 20 in real time, and the main control module 40 generates a cooling signal based on an intelligent temperature adjusting model according to the real-time temperature and sends the cooling signal to the temperature control unit 60. The acne treatment neural network model and the intelligent temperature regulation model are creatively constructed, the treatment effect is better, and the patient can be prevented from being burnt.

The invention provides an ophthalmic atomizer self-adaptive control method and system based on multi-mode perception, and relates to the technical field of intelligent medical instruments. The method comprises the following steps: collecting an eye visual image through a camera, and obtaining a voice signal by using double microphones; analyzing physiological indexes such as blinking frequency and eye closing duration for the visual image, and performing recognition and emotion analysis on the voice signal; fusing the multi-modal information to generate state parameters, and adaptively generating an atomization control instruction through a feedback control algorithm; the instruction is sent to an atomizer main controller through UART communication, and an execution mechanism is driven to adjust atomization parameters. According to the invention, through multi-modal perception and intelligent decision making, self-adaptive treatment adjustment according to the real-time state of the patient is realized, and the treatment accuracy and comfort are improved; the voice interaction function reduces the use threshold and improves the user experience; quantitative evaluation is generated based on objective physiological data, support is provided for personalized treatment, and intelligent development of ophthalmology rehabilitation management is promoted.