1166 results about "Personalized treatment" patented technology

The invention provides a cerebral hemorrhage personalized treatment scheme optimization method and system based on big data analysis, and relates to the technical field of data analysis, and the method comprises the steps: constructing a cerebral hemorrhage patient multi-source heterogeneous clinical database, extracting feature subsets, carrying out semantic alignment, carrying out time sequence modeling and fusion, and obtaining a patient panoramic feature representation; constructing a cerebral hemorrhage diagnosis and treatment knowledge graph, adding the cerebral hemorrhage diagnosis and treatment knowledge graph to a preset graph attention network, distributing weights, obtaining knowledge-enhanced patient feature representation and taking the knowledge-enhanced patient feature representation as a state, generating an action space based on an optional treatment scheme, constructing a reward function and determining a candidate treatment scheme; performing medication suitability analysis, performing semantic conflict identification in combination with the patient allergy history and complication, and performing screening to obtain a suitable candidate treatment scheme set; and for each scheme, predicting a multi-dimensional prognosis index, constructing a multi-criterion decision matrix, combining personal influence factors, adjusting the weight of each index, modeling an optimization problem and determining an optimal treatment scheme.

The invention discloses an intelligent decision-making method and device based on a knowledge retrieval multi-modal large model, and belongs to the technical field of artificial intelligence and medical data processing.The method comprises the steps that multi-modal diagnosis and treatment data of a patient are obtained and preprocessed, and corresponding feature vectors are generated; performing enhancement and cross-modal feature vector fusion on the feature vector of each modal based on an enhancement fusion model to obtain a patient personalized fusion feature vector; and performing knowledge retrieval in a tumor treatment whole-process electronic record database based on the personalized fusion feature vector, obtaining a plurality of treatment schemes under similar clinical manifestations, inputting the retrieved treatment schemes and the personalized fusion feature vector of the patient into a large language model for decision making, obtaining a personalized treatment scheme of the patient, and evaluating the risk of the personalized treatment scheme. A doctor is assisted to give an actual treatment scheme; and if the multi-mode diagnosis and treatment data of the patient is updated along with the actual treatment process, updating the personalized treatment scheme and the risk thereof so as to provide real-time dynamic assistance for a doctor.

The invention discloses a magnetic wave ring which comprises a base, supporting legs are arranged at the bottom of the base, a machine body is installed on the base, a displayer support is installed on the top of the machine body, a displayer is installed on the displayer support, an operation interface is integrated in the displayer, a handle connector is arranged at the end, close to the displayer support, of the machine body, and a magnetic therapy handle is installed in the handle connector. A handle support is arranged on one side of the machine body, a plurality of radiators are arranged on the back of the machine body, power sockets are arranged at the bottoms of the radiators, and power switches are arranged on one sides of Compared with the prior art, the device has the advantages that painless and noninvasive electromagnetic stimulation treatment is achieved through the magnetic induction principle, the curative effect is definite, the side effect is small, and addiction is not prone to occurring. The equipment is reasonable in design, simple and convenient to operate, easy to master and suitable for medical institutions and families. Personalized treatment parameter setting is provided, and the requirements of different patients and different treatment parts are met. The device has multiple safety protection functions, and safety and effectiveness of the treatment process are ensured.

The invention provides a rectal cancer neoadjuvant chemoradiotherapy curative effect prediction method and system based on magnetic resonance image analysis, and aims to improve accurate prediction of curative effects of patients with local advanced rectal cancer (LARC). According to the method, multi-modal magnetic resonance images (T1WI, T2WI, DWI and DCE-MRI) are obtained, qualitative and quantitative analysis is combined, tumor morphology, diffusion characteristics and microcirculation parameters are extracted, clinical and pathological data of a patient are integrated, and a multi-dimensional feature set is constructed. Curative effect prediction is performed through machine learning and a deep learning model, and the model can predict treatment results of complete remission, partial remission, ineffectiveness and the like in a classified manner. The system accurately predicts the reaction of a patient to neoadjuvant radiotherapy and chemotherapy through a non-invasive method, supports formulation of a personalized treatment scheme, reduces excessive treatment, optimizes the treatment effect, and has good clinical application value.

The invention relates to the technical field of cardiovascular disease treatment, and discloses a cardiovascular disease medical decision support method based on a large model, and the method comprises the steps: obtaining multi-modal cardiovascular disease medical data, constructing a multi-source heterogeneous data set, carrying out the preprocessing and feature fusion, and constructing a combined representation model based on the pre-training large model and the fusion of traditional and western medicine knowledge. And designing a hierarchical space-time diagram convolutional network to generate a dynamic physiological state code of the patient, and constructing a multi-target optimization decision tree to generate a treatment scheme containing western medicines, traditional Chinese medicine prescriptions and non-drug intervention. A federal learning framework is adopted to cooperatively train a local model, data security is guaranteed, a global model is optimized, and a curative effect attribution module is further arranged to correct decision deviation. According to the method, multi-modal data and traditional Chinese and western medicine knowledge are integrated, personalized treatment scheme generation is achieved, data safety is guaranteed, and the cardiovascular disease diagnosis and treatment level is improved.

The invention discloses an electroporation ablation voltage control method and system based on a pulse sequence, and relates to key technologies such as intelligent target area identification, electric field modeling, dose control, real-time monitoring and feedback. Tumors are accurately identified and treatment is planned through the AI technology, electric field distribution is optimized to ensure the accurate effect of electroporation pulses, pulse parameters are dynamically adjusted through closed-loop control, and treatment effect evaluation is provided through the sensor fusion technology. The system integrates data analysis and machine learning, intelligently adjusts treatment strategies, realizes personalized treatment and complication prediction, and improves the treatment effect and patient safety. The system is suitable for precise tumor treatment, and healthy tissues are protected while the curative effect is ensured.

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 discloses an effect evaluation method for a non-dialysis diabetic kidney disease, which comprises the following steps: monitoring the change trend of data by collecting renal function related data of a patient, and generating a basic data set; a dynamic nonlinear weighted regression algorithm is adopted to analyze the multi-dimensional renal function data, data weights are dynamically adjusted, and influence weights of different time periods are distributed according to the change mode of the illness state; the data influence is adjusted in real time through a time correlation mechanism, and a disease assessment curve is generated; according to the method, historical data and real-time data are combined, a patient condition trend line and a prediction result are generated, personalized treatment feedback is output, and intervention suggestions are provided for clinicians based on long-term prognosis prediction. The method is suitable for disease monitoring and treatment feedback of patients with non-dialysis diabetic kidney diseases.

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

The invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

The invention discloses an intelligent processing method and system for multi-modal biological medicine data. The method comprises the following steps: acquiring a tumor tissue microscopic image and clinical characteristics of a diagnosed patient object; performing semantic coding on the clinical features to obtain clinical feature semantic understanding feature vectors; performing feature extraction on the tumor tissue microscopic image through a tumor tissue morphological feature extractor based on a deep neural network model to obtain a tumor tissue microscopic feature map; performing target significance analysis on the tumor tissue microscopic feature map to obtain a sequence of target manifestation tumor tissue microscopic local feature vectors; performing class attention interaction fusion processing on the sequence of the clinical feature semantic understanding feature vector and the target explicit tumor tissue microscopic local feature vector to obtain a multi-modal medical semantic fusion feature; and determining a tumor type label based on the multi-modal medical semantic fusion features. Therefore, the tumor type can be detected and judged, and powerful support is provided for the aspects of early diagnosis, personalized treatment and the like of tumors.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention relates to the technical field of prediction analysis, and discloses a lung cancer phenotype prediction auxiliary analysis method and system based on artificial intelligence. The method comprises the following steps: carrying out adaptive preprocessing on a digital image of a lung cancer pathological section to obtain a standardized preprocessed image; carrying out hierarchical progressive tissue segmentation on the preprocessed image to obtain a lung cancer region labeled image; carrying out multi-dimensional feature cascade extraction on the lung cancer region labeled image to obtain a phenotypic feature set; performing hierarchical prediction on the phenotype feature set to obtain an immune phenotype prediction result; performing subtype mapping analysis on the immune phenotype prediction result to obtain a patient diagnosis report; and carrying out multi-dimensional data integration on the diagnosis report of the patient to obtain a personalized treatment scheme. The accuracy and interpretability of lung cancer phenotype prediction are improved through cascade extraction and hierarchical prediction modeling of multi-dimensional features.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention discloses an artificial intelligence multi-modal medical image processing diagnosis and treatment system, and relates to the technical field of medical image processing and diagnosis and treatment, and the system collects multi-angle multi-modal brain image data, extracts brain region features based on multi-modal brain images, formulates a brain normal model, carries out the set fusion of the obtained brain region features, and carries out the diagnosis and treatment of the brain normal model. Generating a three-dimensional structure model of the brain area, compensating the three-dimensional structure model by using the treatment data of the patient to obtain patient features, and analyzing the patient features to obtain a diagnosis report; according to the method, the brain three-dimensional structure model is constructed by fusing the multi-modal image features and the patient treatment data, and the pathophysiological process of the brain diseases is simulated, so that the diagnosis accuracy and reliability are improved, and technical support is provided for early diagnosis and personalized treatment of the brain diseases.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention provides a radiotherapy patient skin injury real-time detection record management system based on image processing, and belongs to the technical field of radiotherapy patient skin injury real-time detection. Comprising a multispectral image acquisition module for imaging the skin of a patient at the same visual angle by adopting different spectrums to obtain three spectral images including near-infrared imaging, infrared imaging and ultraviolet imaging; a first identification module is used for fusing three spectral images to obtain an image data set, and a second identification module is used for constructing an epidermis injury coefficient # imgabs0 #, a dermis injury coefficient # imgabs1 # and a subcutaneous injury coefficient # imgabs2 # of the ith patient and evaluating the epidermis injury coefficient # imgabs0 #, the dermis injury coefficient # imgabs1 # and the subcutaneous injury coefficient # imgabs2 # respectively. A skin injury evaluation model is constructed, the comprehensive injury area ratio is calculated, the classification of injury levels is realized, data support is provided for a personalized treatment scheme, and a powerful auxiliary diagnosis tool is provided for a medical institution.

The invention relates to the technical field of medical emergency monitoring, in particular to an ICU emergency response system which comprises a preliminary risk assessment module, an emergency recognition module, a treatment scheme optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation management module and a medical decision support module. According to the method, feature engineering and logistic regression analysis are combined with medical history and real-time vital signs of a patient, emergency medical events are recognized by applying pattern recognition and natural language processing, a treatment scheme is optimized by using a support vector machine and a decision tree, personalized treatment suggestions are provided, and the time sequence and a biometric method are used for finely monitoring the vital signs; health risks are early warned, resources are reasonably allocated in emergency response through a resource scheduling algorithm and a priority queue, illness state prediction is assisted through machine learning and probability statistics, resource utilization is improved through linear programming and a resource optimization strategy, and waste is reduced.

The invention provides a traditional Chinese medicine diagnosis and treatment decision method and system based on evidence, and relates to the technical field of diagnosis and treatment decision, and the method comprises the steps: obtaining patient information, screening evidence, classifying syndromes, generating a personalized treatment scheme, collecting feedback and monitoring reactions, quantifying the curative effect, and dynamically adjusting the scheme. The method is systematized and scientific, and has a good application prospect and popularization value. Through a feedback mechanism and multi-round iterative learning, the diagnosis and treatment scheme is continuously optimized, and it is ensured that the diagnosis and treatment scheme adapts to the change requirements of patients; and by utilizing statistical analysis, the interpretability of the model is enhanced, a doctor is helped to understand a decision process, and clinical confidence is improved.

The invention relates to the technical field of natural language processing and image processing, and discloses an emotional music adjusting method and terminal based on dynamic perception and multi-modal fusion. The method comprises the following steps: acquiring multi-dimensional feature data of a user, processing an unstructured text through a dynamic sensing field based on a large language model, and carrying out dynamic weight distribution on the multi-dimensional feature data to generate fusion features; collecting facial image data of a user, extracting facial emotion features through a visual converter model, and mapping the facial emotion features to a music feature space; the features of the two modes of text and image, namely fusion features and facial emotion features, are input into a music generation module, and personalized treatment music is generated through feature fusion coding, music structure planning and a layered decoding architecture. And music generation parameters are adjusted through a dynamic feedback mechanism to form closed-loop control. According to the method and the computer terminal, full-process intelligent processing from user feature capturing to emotional music generating is achieved, and the treatment effect and the user experience are improved.

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

The invention discloses a deep learning-based lung cancer early screening system, and particularly relates to the technical field of medical assistance, which comprises an intelligent data ecological module, an intelligent analysis and diagnosis module, an intelligent decision support module and a user interaction management module. According to the invention, cross-mechanism data sharing and cooperative training are realized by using data semantic understanding and federated sharing technologies, transparency and traceability of data are ensured through a block chain technology, and in an intelligent analysis and diagnosis module, a deep learning model comprehensively analyzes a multi-modal image and identifies molecular features of cancer cells, so that the accuracy of cancer diagnosis is improved. Potential early-stage lung cancer signs are found through an unsupervised learning algorithm, individual risks are dynamically evaluated, focus development is tracked, and an intelligent decision support module combines personalized treatment suggestions, optimizes clinical pathways and multidisciplinary collaboration, provides accurate decision suggestions and assists doctors in making scientific and effective treatment schemes.

The invention relates to the technical field of artificial intelligence, in particular to an old-age care chronic disease management method, system and device based on artificial intelligence and a medium. The method comprises the following steps: firstly, acquiring historical medical data of a patient, extracting features by using a pre-trained deep neural network, and performing feature analysis through an old-age disease knowledge graph; then, based on the health feature data, a time sequence prediction model is adopted to evaluate the chronic disease risk; combining a risk assessment result with physiological index data collected in real time to predict a disease development trend; a personalized treatment scheme is formulated according to the illness state evaluation and prediction result; the scheme is dynamically adjusted by analyzing treatment compliance data of the patient; and finally generating a scheme including medication reminding, complication prevention and lifestyle guidance. Through mining of historical data, accuracy of risk assessment is ensured by using time sequence prediction, individuation of a treatment scheme is ensured by using reinforcement learning, and adaptability of a management scheme is improved through dynamic adjustment.

The invention discloses a depression personalized treatment decision-making system based on a knowledge graph, and belongs to the technical field of medical aid decision-making. A collection module is designed to collect patient related data, and then a construction module is used to construct the knowledge graph; the knowledge graph is optimized through the optimization module, a personalized auxiliary treatment scheme is output through the personalized output module, auxiliary decision making is provided for treatment of a patient, and the method has the advantages that by integrating multi-source data and constructing the high-quality knowledge graph, a new thought and method are provided for diagnosis and treatment in the medical field. The application also provides a depression personalized treatment decision-making system based on a knowledge graph by combining an artificial intelligence technology, then realizes optimization of the knowledge graph through operations such as entity extraction, relation extraction, attribute extraction, entity alignment, entity linking, relation deduction and the like, and finally realizes personalized treatment decision-making of depression through multi-modal data analysis and knowledge graph reasoning. And intelligent support is provided for depression diagnosis and treatment.

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 discloses percutaneous nerve field vagus nerve stimulation diagnosis and treatment equipment and a diagnosis and treatment method thereof, and particularly relates to the technical field of neuromedicine, intelligent medical equipment and multi-mode physical therapy. The equipment comprises a neural signal acquisition module, a nerve positioning module, a data processing and analysis module, a treatment execution module and a diagnosis result output module; the system is further provided with a battery power management module, a self-adaptive closed-loop cloud platform and a wearable fixing device, by collecting bioelectricity signals, light absorptivity and temperature data of an ear nerve area, ear target cranial nerve branches are positioned, a dynamic diagnosis map is constructed, and a diagnosis result and personalized treatment parameters are generated. Electric pulse, near-infrared light and local thermal therapy multi-mode collaborative stimulation is adopted for treatment, and parameters are monitored and adjusted in real time through a cloud platform. According to the invention, accurate diagnosis and personalized treatment can be realized, the medical efficiency and the treatment effect are improved, and an effective means is provided for nerve disease diagnosis and treatment and pain relief.

The invention relates to the technical field of medical equipment, in particular to a preset tooth cutting method and system based on image data, and the method comprises the following steps: S1, obtaining multi-modal image data by using CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and optical scanning technologies, and carrying out consistent analysis after preprocessing; and S2, performing automatic image segmentation by using a deep convolutional neural network (CNN). S3, optimizing the implant design through a generative adversarial network (GANs); s4, simulating and optimizing a cutting path through an augmented reality (AR) environment; and S5, laser micromachining is used for surface treatment of the implant. According to the method, the dentin cutting scheme matched with the specific shape and size of the bone defect area is designed through the data generated by scanning the three-dimensional shape of the complete tooth in advance, so that the image definition and the diagnosis accuracy are improved, the preoperative analysis is more accurate, and the subsequent personalized treatment design is effectively supported.

The invention provides a medical agent middleware-based full-specialized collaborative diagnosis and treatment method and system, and relates to the technical field of medical agents, and the method comprises the steps: building a basic medical knowledge graph through reinforcement learning, converting expert diagnosis and treatment experience into diagnosis and treatment probability distribution through a personalized treatment decision model, and forming an enhanced medical knowledge graph; and calculating a matching probability with a disease based on the symptom feature vector of the patient to generate a preliminary diagnosis decision, and selecting and adjusting a treatment scheme through reinforcement learning. According to the invention, general and specialized collaborative diagnosis and treatment can be realized, and medical decision accuracy and diagnosis and treatment efficiency are improved.

The invention aims to provide an acne precise treatment and drug delivery system based on multi-modal data and feedback control. The system comprises an image acquisition and identification module, which carries out acne focus identification on an obtained skin image based on a deep learning algorithm, and extracts focus feature data; the sensor acquisition module is used for acquiring skin physiological parameters and environmental parameters related to acnes; the multi-modal fusion module carries out fusion processing on the focus characteristic data, the skin physiological parameters and the environmental parameters to generate current acne state parameters; the digital twinborn prediction module constructs a digital twinborn model of the patient according to the current acne state parameters and predicts the development trend of acnes; the individualized treatment decision module formulates an individualized treatment scheme and a corresponding administration dosage according to a prediction result of the digital twinning prediction module; and a set of system capable of realizing multi-source data acquisition, automatic evaluation, prediction analysis and personalized treatment scheme recommendation of the acne of the patient is constructed.

The invention provides a prostate cancer biochemical recurrence prediction system based on a deep learning image and clinical information multi-mode model. The prostate cancer biochemical recurrence prediction system comprises an input module, a preprocessing module, a feature selection module, a fusion module, a prediction module, a data management and storage module and a result output and decision support module. According to the method, bpMRI image data and clinical information of a patient are fully utilized, multi-mode information is fully fused through a deep learning technology, and accurate prediction of biochemical recurrence is achieved. And the robustness of the model is further enhanced by adopting 5-fold cross validation and a diversified data enhancement strategy. Experimental results show that the method can assist in making more accurate patient layering and personalized follow-up visit schemes, and potentially reduces the over-treatment and missed diagnosis risks. The invention provides an efficient and reliable AI solution for prostatic cancer prognosis evaluation, and provides powerful support for promoting precise medical treatment and making personalized treatment strategies.