121 results about "Intervention effect" patented technology

The invention relates to the technical field of artificial intelligence and nutrition health management, and discloses an intelligent nutrition correction tracking early warning method and system.The intelligent nutrition correction tracking early warning method comprises the steps that a hierarchical heterogeneity causal effect model is constructed, and individual features are mapped to a potential type space; representing a causal relationship between nutrition intervention and result variables by using a structural causal model, and evaluating an intervention effect by using a dual robust estimator; personalized grouping is realized by identifying individual subgroups; a reinforcement learning algorithm based on context awareness is combined with an anti-fact reward function, and a personalized nutrition intervention strategy is optimized; key feature combinations are extracted through a differentiable decision tree and SHAP value analysis, and multi-level causal interpretation is generated. According to the method, anti-factual reasoning, reinforcement learning and causal relationship extraction technologies are fused, accurate prediction, evaluation and early warning of the individual nutrition intervention effect are realized, and the personalized precision of nutrition intervention and the user compliance are remarkably improved.

The invention discloses a motion intervention multi-dimensional decision support system and method for CIPN remission, and relates to the technical field of decision support, and the system comprises an intervention effect prediction unit which is used for collecting the multi-dimensional data of a patient, constructing a patient simulation model, and carrying out the simulation of different motion intervention schemes through the Markov decision process theory, establishing an intervention effect prediction model based on the simulation result; an exercise intervention recommendation unit; an encouraging and exciting unit; the optimal motion recommendation unit is used for updating a patient simulation model according to the constructed encouraging and incentive mechanism, analyzing a causal relationship between a motion effect and neural restoration through a hypergraph gradient causal discovery algorithm, and screening out an optimal motion recommendation scheme; and a visualization unit. According to the invention, a personalized exercise intervention scheme can be customized according to the individual characteristics and physiological status of the patient, and the effects of different intervention strategies are predicted, so that the pertinence and effectiveness of exercise treatment are improved.

The invention provides an intelligent virtuality and reality combined psychological health service device based on digital elements, and aims to improve the accuracy and individuation level of psychological health management. The device firstly obtains the physiological indexes, behavior data, environmental factor data and social economic data of an individual, and carries out multi-modal fusion to form comprehensive feature data. Based on this, a psychological health environment factor model is constructed to quantify the influence of the external environment on the individual psychological state, and the calculation weight and prediction logic of the negative emotion large model are optimized. And the optimized negative emotion large model is used for identifying an individual emotion state, analyzing factors such as social environment, economic pressure and life events in combination with the mental health environment factor model, and generating an individual mental health assessment result. And according to an evaluation result, the virtual digital doctor provides intelligent pre-inquiry and other services. Through data-driven intelligent analysis and virtual-real combined intervention means, the accessibility, accuracy and intervention effect of psychological health services are improved.

The invention discloses a child autism adaptive brain-computer fusion intervention system based on a large model, and the system comprises a multi-modal data collection module which is used for collecting the neural data, behavior data and clinical scale data of a user; the feature extraction and user portrait construction module is used for performing feature extraction and subtype recognition on the multi-modal data to generate a personalized portrait; the multi-defect intervention normal form generation module is used for dynamically generating a personalized intervention task on the basis of a large language model in combination with the personalized portrait and the historical state of the task; the self-adaptive regulation and control module is used for dynamically adjusting an intervention strategy and task difficulty according to the feedback of the real-time neural data and the dynamic change of the behavior data; the user interaction interface module is used for providing a multi-modal human-computer interaction interface; and the effect evaluation and long-term tracking module is used for generating an individual and group intervention effect report. By utilizing the system and the method, accurate, personalized, multi-dimensional and long-term intervention on the autism children can be realized.

The invention relates to a weight loss intervention method and device for chronic renal failure and obesity patients based on an individualized prescription. The method comprises the following steps: constructing a multi-modal dynamic time sequence feature vector of a patient; identifying and quantifying potential causal effects of different weight loss intervention measures on physiological outcomes of renal functions, electrolyte balance, anemia states, bone mineral metabolism and weight indexes of the CKD patient according to a dual robust estimator; dynamically constructing a weight loss intervention measure causal model according to the potential causal effect of the physiological outcome of each patient and the physiological and pathological background; and inputting the multi-modal dynamic time sequence feature vector of the patient into a weight loss intervention measure causal model, and generating a daily diet scheme, an exercise scheme and corresponding high-confidence risk early warning and causal explanation of the patient. The method overcomes the limitation that the traditional method only pays attention to correlation and cannot clearly determine the intervention effect, ensures that the causal atlas and the intervention scheme are optimized according to the real-time change state of the patient, and realizes a real dynamic prescription.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention discloses a big data analysis-based occupational health management system optimization method. The method comprises the following steps of 1, collecting and preprocessing multi-source health information data of employees; 2, mapping the multi-modal data set into a unified-dimension employee health feature tensor; 3, constructing a health risk isomerism map based on the employee health feature tensor; 4, inputting the health risk heterogeneous atlas into the improved GATv2 model to obtain a health risk embedded representation vector; 5, performing risk assessment on the health risk embedded representation vector, and identifying a high-risk employee node; step 6, constructing an intervention action candidate set for the high-risk employee nodes, and generating an optimal intervention strategy by adopting an MOPSO algorithm; and step 7, executing the optimal intervention strategy and generating a score of an intervention effect. According to the invention, the improved GATv2 model and the MOPSO algorithm are fused, and dynamic assessment and accurate intervention of occupational health are realized.

The invention relates to the technical field of psychological health intelligent evaluation and collaborative intervention, and discloses a family-school psychological health collaborative method and system.The method comprises the steps that a multi-dimensional data model is built based on the psychological theory, and family-school double-end data collection is achieved through standardized indexes; constructing a multi-dimensional psychological state diagram structure, and realizing home-school data deep fusion by adopting a time sequence dynamic coding method; a time sequence modeling and multi-level risk assessment method is adopted, and a dynamic mental health risk assessment result and an intelligent early warning signal are output; a cross-platform collaborative intervention management method is adopted, and a personalized intervention strategy and a collaborative task allocation scheme are output; and a multi-dimensional effect evaluation and reinforcement learning optimization method is adopted to realize continuous optimization of an intervention strategy. Through artificial intelligence technologies such as the dynamic graph neural network and deep reinforcement learning, intelligent fusion and cooperative intervention of family-school psychological health data are realized, and the early recognition accuracy and intervention effect of psychological health problems are improved.

The invention discloses a cognitive behavior training system, method and device and a program product, relates to the field of medical rehabilitation, and is used for solving at least one problem of single training target, single task difficulty and single task element. According to the method, main training tasks of five dimensions are carried out in stages, cognitive competence corresponding to task training of each dimension is progressively carried out step by step, and centralized, continuous, selective, alternate and dispersive attention cognitive competence intervention is respectively carried out. The main training task of each dimension sequentially comprises multiple levels of sub-training tasks with gradually increased difficulty, and each level of sub-training task comprises multiple groups of segmented tasks. And respectively evaluating the training effect under each dimension. According to the method, the difference and compliance of cognitive behavior training are improved, the intervention efficiency is improved, and the long-term intervention effect is ensured.

The invention belongs to the field of computational medicine, particularly relates to a method and a system for constructing full-parameter digital twinning of a patient and performing treatment simulation, and aims to solve the problems that an existing digital twinning model cannot fuse multi-scale data, lacks dynamic optimization capability and is disjointed with a clinical decision process. The method comprises the following steps: acquiring and integrating multi-source heterogeneous data of a patient; performing cross-scale fusion modeling based on the data, and constructing a personalized digital twinborn initial parameter set containing genetic background correction; driving a multi-physics field coupling engine to simulate an intervention effect, constructing a reverse optimization problem taking real-time monitoring data as a dynamic constraint, and solving an optimal intervention scheme parameter; and carrying out verification simulation on the optimization scheme and generating a treatment report containing quantitative evaluation and risk early warning. According to the invention, by constructing a full-parameter and mechanism model and introducing a dynamic closed-loop optimization verification process, the personalized precision, safety and clinical decision support value of treatment scheme simulation are significantly improved.

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 AI-based atrial fibrillation patient health risk prediction method, and belongs to the technical field of medical information, and the method specifically comprises the steps: receiving a physiological parameter record and an intervention treatment record of an atrial fibrillation patient, carrying out the time sequence alignment operation, and forming structured patient data; extracting risk-related features from the structured patient data, wherein the risk-related features are divided into basic risk features and intervention response features; inputting the basic risk features into a basic risk prediction network, and outputting a basic risk score; inputting the intervention response characteristics and the basic risk score into an intervention effect separation network together to generate a post-intervention risk score; calculating a net effect value of treatment intervention according to the difference between the basic risk score and the post-intervention risk score; combining the basic risk score, the post-intervention risk score and the net effect value to generate a patient individualized long-term risk prediction trajectory; and accurate prediction of the long-term health risk of the atrial fibrillation patient is realized.

The invention provides a tumor patient group psychotherapy method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting demographic and disease course information, psychological scale scores, physiological signals and voice-text-face multi-modal data of a patient; emotional features are jointly recognized through a deep model, psychological needs are evaluated, clustering is carried out in combination with disease course stages, and patients are automatically distributed to homogeneous treatment groups. In the implementation process, the speaking balance degree, the topic dominant rate and the intra-group cohesion are calculated in real time, and if indexes cross the boundary, a guide instruction is generated to adjust discussion. And the system adaptively selects and dynamically adjusts a cognitive behavior therapy according to the emotion and interaction state, accepts a commitment therapy or a positive pressure reduction script until the emotion returns to a safety interval, carries out closed-loop summarization on intervention effect data, and outputs a report containing an emotion trend, interaction quality and an intervention effect. According to the method, real-time accurate evaluation, immediate intervention adjustment and continuous optimization can be realized.

The invention discloses a healing landscape intervention method and device based on user feedback, and the method comprises the steps: carrying out the intervention of a to-be-intervened user according to a preset intervention scheme, and obtaining the feedback data of the to-be-intervened user after the intervention; wherein the preset intervention scheme comprises a preset healing landscape video; collecting user multi-dimensional data of the to-be-intervened user, and preprocessing the feedback data and the user multi-dimensional data to obtain a plurality of personal emotion indexes corresponding to the to-be-intervened user; and according to the plurality of personal emotion indexes, constructing a healing isomerism map, and according to the healing isomerism map, adjusting the preset intervention scheme. According to the invention, the intervention effect of psychological intervention based on the healing landscape can be improved.

The invention relates to the technical field of limb rehabilitation training, in particular to an artificial intelligence-based cognitive impairment assessment and intervention training system, which comprises a path construction module, an action acquisition module, a matching recognition module, a rhythm regulation and control module and a cognitive intervention module. According to the method, the standard path is constructed in the virtual task and combined with the three-dimensional action data of the user to form the path section response result, so that the spatial integrating degree of the action of the user and the target path can be identified, and quantitative description of the action execution stability and continuity is realized through coordinate difference analysis; a cooperative path record of task execution is obtained in combination with a trajectory coverage rate and a matching relationship, and a trend analysis and parameter regulation mechanism is introduced, so that a task pushing rhythm and a user response rhythm are dynamically matched, and tracking correction of an intervention effect in a training stage and fine evaluation of cognitive limb disorder performance are realized. The analysis granularity of cognitive state and action collaboration is improved, and the mapping precision between the intervention content and the user execution characteristics is enhanced.

The invention discloses an optimization method for senile cognitive impairment intervention. The optimization method comprises the following steps: S1, a multi-modal evaluation stage: acquiring neurophysiology, behavior and language data of a patient through a multi-source sensing module; s2, personalized prescription generation: calculating a comprehensive cognitive impairment index (CCI), and generating a personalized cognitive training scheme; s3, the intervention module executes the steps that a virtual training scene is generated in the AR environment; and S4, effect verification and iteration: generating a neuroplasticity evaluation report after training, and quantifying the parahippocampal gyrus-prefrontal cortex function connection enhancement degree. Neurophysiology, behaviors and language data of a patient are synchronously collected by adopting a multi-source sensing module, and a personalized cognitive training scheme is generated based on a comprehensive cognitive disorder index. Meanwhile, a virtual scene is constructed to execute an intervention module, and the parahippocampal gyrus-prefrontal lobe function connection enhancement degree is quantitatively analyzed after intervention, so that an evaluation-decision-intervention-verification closed-loop system is constructed, and the intervention effect of the senile cognitive impairment is remarkably improved.

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 discloses a method and a system for training a dialogue intervention large model for children with autism, and the system comprises a data processing module which is used for converting original dialogue data from theme dialogue intervention records of children with autism and doctors into a high-quality multi-round dialogue data set; the model training module is used for finely adjusting an existing large language model on the basis of the high-quality multi-round dialogue data set so as to learn dialogue styles and intervention strategies in the high-quality multi-round dialogue data set, and an intervention large model which can follow an ABA application behavior analysis principle to perform theme dialogues with autistic children is obtained; and the model evaluation module is used for comprehensively evaluating the intervention effect of the intervention large model from the sentence dimension and the dialogue dimension so as to feed back the model fine adjustment process. According to the method, the large model following the ABA application behavior analysis principle can be trained to perform dialogue intervention with the autism children, the intervention effect is effectively improved, and accessible, efficient and low-cost social communication rehabilitation support is provided for the autism children.

The invention discloses a sleep apnea monitoring system and method, and the method comprises the following steps: S1, collecting a multi-modal physiological signal, executing the preprocessing, and generating a standardized sequence; s2, recognizing a sleep stage label, and extracting breathing phase and thoracico-abdominal phase difference information; s3, detecting a suspected apnea event, calculating a consistency index and a confidence coefficient, and outputting a candidate event; s4, when a safety threshold is met, disturbance verification is carried out, responses before and after intervention are collected, and event types are judged; s5, constructing an upper airway digital twinborn model, simulating and predicting an intervention effect, and outputting an abnormal severity score and an early warning; and S6, fusing the event and the risk information, generating a self-evolution risk curve, and outputting a monitoring report. According to the method, through multi-modal physiological signal fusion and upper airway digital twin modeling, accurate recognition, typing judgment and individualized risk dynamic monitoring of sleep apnea are achieved.

The invention discloses an emotion intervention system and device based on a reminiscence therapy, and belongs to the technical field of mental health intervention and intelligent medical equipment. The device comprises a stimulation presentation subsystem, a physiological signal collection subsystem, a behavior response recognition subsystem, a neural response modeling subsystem, an intervention effect evaluation subsystem and a central control and feedback regulation subsystem, a closed-loop intervention framework is formed through real-time communication bus interconnection, and by presenting an individualized life event image set, multi-modal physiological signals and behavior responses are synchronously collected, so that the intervention effect is evaluated. A neural response model is constructed based on a brain region abnormal activation mode disclosed by functional magnetic resonance imaging, the activation level of a target brain region is predicted, the intervention effect is evaluated by integrating multiple indexes, and a central control system dynamically adjusts a stimulation sequence according to an evaluation result and a neural response predicted value by applying a hierarchical reinforcement learning algorithm. Personalized and precise emotion intervention aiming at the subclinical depression state is realized, and the intervention effect and the nerve regulation pertinence are effectively improved.

The application belongs to the technical field of disease risk prediction, and discloses a computer network-based risk prediction system for senile dementia, which comprises the following steps: collecting multi-modal data of a target user to obtain social interaction data, physiological parameter data and cognitive behavior data; extracting cognitive fluctuation nodes, social degradation nodes and physiological disorder nodes through dynamic fluctuation feature analysis to form a potential key node feature set; constructing an individualized dementia conversion risk map based on time sequence correlation and cross-modal coupling characteristics of the potential key node feature set to identify key risk nodes; determining a risk inflection point and predicting a senile dementia risk grade by analyzing the topological structure change and time evolution mode of the dementia conversion risk map; and providing a personalized risk intervention scheme and monitoring intervention effects in real time; the application realizes early and accurate prediction and active intervention of dementia risk, and significantly improves prevention efficiency and clinical decision value.

The present application relates to the technical field of postoperative rehabilitation, in particular to a postoperative rehabilitation intervention system for patients with neurological diseases based on evidence-based medical evidence. The steps implemented by the system include: obtaining characteristic data of a patient with a neurological disease to be analyzed and reference patients, a rehabilitation scheme of the reference patients and a postoperative Barthel index, calculating a reference factor of the reference patients corresponding to each rehabilitation scheme under each preoperative state; obtaining a comprehensive Barthel index of each rehabilitation scheme under each preoperative state by using the reference factor and the postoperative Barthel index, determining a correction factor under each preoperative state according to the comprehensive Barthel index, and correcting the comprehensive Barthel index to obtain a target Barthel index; determining a reference rehabilitation scheme by combining the similarity of the characteristic data of the patient with a neurological disease to be analyzed and different reference patients and the target Barthel index. The present application can improve the postoperative rehabilitation intervention effect for patients with neurological diseases.

The invention discloses a latent variable guided unsafe behavior intervention system and a latent variable guided unsafe behavior intervention method for building workers. A decision laboratory-interpretation structure model is used for analyzing the mutual relation between unsafe behavior factors such as unsafe psychology, unsafe physiology, safety ability and unsafe motivation of the building workers and safety culture, safety atmosphere and safety education. And determining an action path of the intervention measures on the influence factors. And constructing an unsafe behavior occurrence mechanism and intervention mechanism model in combination with the structural equation model. And then simulation research and analysis of the action effect of the intervention strategy are carried out by using system dynamics, and the internal occurrence mechanism and intervention effect of the unsafe behaviors of the building workers are deeply analyzed. The method has the advantages that the relation between unsafe behavior occurrence and intervention paths of workers is studied statically, the unsafe behavior occurrence and intervention process is studied dynamically, the safety management level in the building construction process is improved, and safety accidents are reduced.

The application discloses a mindfulness prediction-intervention-feedback system and a control method, relates to the technical field of mindfulness training systems, and comprises a wearable user interaction device and sequentially connected user management module, mindfulness intervention training module, dynamic prediction module, intervention effect evaluation module and feedback interaction module; the initial mindfulness level, depression level and anxiety level of a user are determined; based on the depression level and anxiety level of the user, a target training scheme of the user is determined, and the user is prompted to perform mindfulness training according to the target training scheme; the target training scheme is adjusted based on an emotional state factor; the real-time mindfulness level of the user and the completion progress of the target training scheme by the user are determined according to a preset time interval, a mindfulness training report is generated in combination with the initial mindfulness level, and visual processing is performed; the mindfulness training method is selected by evaluating the mindfulness level of the user, and the rationality of the mindfulness training scheme can be improved.

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

This invention provides a method, device, electronic device, and storage medium for mental health intervention. The method includes: acquiring user assessment behavior characteristic data, including consistency scores for answers, reasonableness scores for completion time, and regularity scores for response patterns; determining a credibility index based on the assessment behavior characteristic data, determining a warning level based on the credibility index, and determining a preset first intervention strategy based on the warning level; after the preset first intervention strategy is applied to the user, acquiring user characteristic data and forming a user characteristic vector based on the user characteristic data; assigning a corresponding preset second intervention strategy to the user through a trained classification model and the user's clustering categories for psychological intervention, acquiring intervention effect labels, and stopping the push of the preset second intervention strategy to the user if the levels of several pre-effect labels are lower than the preset label levels. This method can improve the effectiveness of mental health intervention.

The invention relates to the technical field of virtual reality medical treatment, and discloses a head and neck cancer chemoradiotherapy related symptom remote monitoring and multi-sensory intervention method based on VR, and the method comprises the following steps: S1, a patient carries out preliminary evaluation through a first input module of a VR terminal, and transmits monitoring data to a data processing module through the VR terminal; s2, performing secondary evaluation on the average score or the symptom of which any score is in a second interval in a targeted manner through a first input module of the VR end, and sending a multi-sensory first-level intervention request or a multi-sensory second-level intervention request or a multi-sensory third-level intervention request to the VR end by a data processing module according to the score condition of the symptom; and S3, after the patient completes corresponding intervention, performing post-intervention evaluation through the first input module of the VR end, and comparing the evaluation conditions before and after intervention by the data processing module to generate an intervention effect evaluation report. Related symptoms of a patient can be dynamically monitored in real time, symptom changes can be found in time, corresponding intervention is given, treatment interruption is prevented, and the lifetime is prolonged.

This invention discloses a verifiable application attack detection method based on artificial intelligence causal inference, including data collection and structuring, causal graph construction, intervention effect calculation, abnormal causal determination, verifiable evidence chain generation, detection result output, and false positive adaptive correction steps. By constructing a structured causal graph model between attack behavior and abnormal system states, this invention can effectively distinguish between "spurious correlations" and "true causality," significantly reducing the false positive rate caused by business fluctuations or system changes. The verifiable mechanism designed in this invention can generate a complete and tamper-proof causal evidence chain from the original attack payload to the final abnormal state for each detection conclusion, making the detection results not only accurate but also transparent, auditable, and reproducible. This greatly improves the judgment efficiency of security operations teams and provides a reliable technical foundation for security forensics scenarios based on blockchain or third-party arbitration.

The invention discloses an early diagnosis and intervention curative effect evaluation method for children with autism, and belongs to the technical field of medical diagnosis. The method specifically comprises the following steps: S1, obtaining historical case big data: collecting the historical case big data of an autistic truly diagnosed child and a normally developed child and corresponding diagnosis results and intervention diagnosis and treatment records, and associating to form a historical database; s2, historical data preprocessing: performing cleaning, standardization and missing value processing on historical case big data, and associating and integrating the historical case big data into a structured sample set; by collecting behavior, physiology, environment and family multi-dimensional data, mining an internal mode of the data in combination with depth features, and then utilizing a multi-modal fusion diagnosis model for analysis, dependence on subjective experience of doctors and a single scale is reduced; the classification accuracy of the model is optimized through cross validation, a diagnosis report contains core abnormal features and a judgment basis, manual recheck is supported, and the risks of missed diagnosis and misdiagnosis are further reduced.

The invention discloses a dynamic trajectory construction and visual mapping method for association of mental disorder brain network damage and systemic system diseases, and belongs to the field of artificial intelligence medical application. According to the method, high-resolution MRI images, biomarkers and clinical information of major mental disorder patients are collected, and the influence of factors such as age, gender, medication and diagnosis on the braingut axis and the cardio-cerebral axis is evaluated through multi-modal data fusion. By constructing a disease dynamic trajectory model, brain structures and function change modes corresponding to different mental disorders are identified. Large-scale samples are analyzed through machine learning, potential risks and protection factors are extracted, and a visual tool is developed to visually display changes of the brain under different disease systems. A closed-loop feedback mechanism is established through follow-up visit, the disease progress and the intervention effect are dynamically tracked, and key evaluation indexes are identified. According to the invention, theoretical basis and practical guidance are provided for early screening, precise intervention and personalized treatment of mental disorders, and the diagnosis and treatment accuracy and efficiency are remarkably improved.