1460 results about "Treatment plan" patented technology

The invention discloses an electronic medical record intelligent evaluation method based on complex quality control indexes, and relates to the field of medical information processing and artificial intelligence. The method comprises the steps that an original electronic medical record text is collected and preprocessed, and structured diagnosis and treatment information and an event sequence diagram are extracted; through prompt word chain construction and a semantic reasoning mechanism, a large language model is guided to intelligently evaluate complex quality control indexes in medical records. The complex quality control indexes comprise diagnosis basis sufficiency, treatment scheme rationality, key result and change record integrity, treatment measure integrity and causal relationship rationality. According to the method, technologies such as a medical knowledge graph, a graph neural network and semantic vector retrieval are utilized to realize external knowledge recall and causal reasoning support; a self-consistency reasoning mechanism, a self-reflection mechanism and a multi-model cross validation mechanism are introduced to improve the accuracy and credibility of an evaluation result; and finally, outputting a structured quality control report and a visual reasoning chain. According to the method, the intelligence and refinement level in a complex medical quality control task can be remarkably improved, high interpretability and practical value are achieved, and the method is suitable for application scenes such as hospital quality management, scientific research evaluation and medical document standardization.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention discloses an AI-based traditional Chinese medicine dialectical auxiliary diagnosis method and system and a medium thereof, and relates to the technical field of intelligent auxiliary diagnosis, and the method comprises the steps: collecting physiological monitoring data, tongue condition data and pulse condition signals of a patient, obtaining patient symptom information, mapping the patient symptom information to a preset traditional Chinese medicine pathogenesis classification model, and generating an initial symptom feature vector; calling a knowledge base containing traditional Chinese medicine prescription rules and compatibility medication taboo, performing data space-time alignment processing and dynamic combination calculation on the initial symptom feature vector, and generating a personalized candidate prescription set conforming to the traditional Chinese medicine compatibility taboo; according to dynamic combination calculation, a solution algorithm based on a constraint satisfaction problem is adopted, and multi-dimensional association rule matching is carried out in combination with the mapping relation between syndromes and modern medical symptoms; and outputting a prescription medication recommendation scheme containing traditional Chinese medicine compatibility, dosage and decoction methods based on the candidate prescription set. The method supports the formulation of an accurate personalized diagnosis and treatment scheme, and meets the requirements of high-quality traditional Chinese medicine personalized auxiliary diagnosis and treatment.

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

The invention relates to the technical field of river channel desilting treatment, in particular to a dynamic simulation and optimization decision-making-based river channel silt treatment method, which comprises the following steps of: acquiring and treating historical and real-time multi-dimensional environmental data of a to-be-treated river channel and an influence area of the to-be-treated river channel; a river basin process simulation system capable of simulating river water power, sediment transportation, pollutant migration and transformation and ecological response processes is constructed, decision variables forming a river regulation scheme are defined in a parameterized mode, a multi-target optimization problem is defined and set in a formalized mode, a multi-target optimization engine is adopted, and the river regulation scheme is optimized. The method comprises the following steps: embedding a drainage basin process simulation system as a core evaluation module into an optimization iterative loop, implementing an optimization scheme, and continuously monitoring the environmental state change in the implementation process and after the implementation; the method can accurately predict the long-term and short-term influence of different treatment schemes in multiple aspects of hydrodynamic force, silt, pollutant migration and transformation, ecological response and the like, and solves the problem of comprehensive treatment of sludge.

The invention discloses a doctor-patient negotiation method and device fusing LLM and a multi-agent architecture, equipment and a medium, and relates to the technical field of doctor-patient negotiation. The doctor-patient negotiation method comprises the following steps: acquiring doctor-patient background information, and constructing a doctor agent, a patient agent and a coordination agent based on a large language model. The doctor agent generates a preliminary treatment regimen. The patient agent generates feedback to the preliminary treatment regimen. The coordination agent parses the feedback, generating a structured acceptance score and a semantic vector. The dynamic belief is updated based on the semantic vector doctor agent. And performing fuzzy utility evaluation on the current treatment scheme, and judging whether strategy adjustment is triggered or not. And the coordination agent calculates the adjustment amount of each issue based on the acceptance difference value, the patient yielding willingness coefficient and the maximum yielding amplitude, and obtains a new quotation scheme. And triggering a belief calibration and strategy reconstruction process at a preset stage node of multiple rounds of negotiation to obtain a new strategy. And when the negotiation round reaches a preset maximum round number, outputting a final treatment scheme.

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention discloses a multi-angle particle therapy system without a rotating rack and a using method thereof, and relates to the technical field of particle therapy equipment, and the treatment system comprises an accelerator, an irradiation system, a control system, a patient supporting system and an imaging system. The patient supporting system comprises a containing and fixing device, a two-dimensional translation system, a rotating system and a multi-degree-of-freedom mechanical positioning system, the containing and fixing device is connected with the two-dimensional translation system, the two-dimensional translation system is connected with the rotating system, and the two-dimensional translation system controls the relative position of a patient and a rotating center; the rotating system rotates a patient to an appointed angle of a treatment plan or cooperates with the imaging system to obtain a three-dimensional image of the patient, and the irradiation system irradiates a tumor target area; the use method comprises the following steps: generating a treatment plan by reconstructing a three-dimensional CT image, and completing irradiation at different angles according to the treatment plan. According to the treatment system, multi-angle irradiation on a patient can be realized under the condition that a rotating rack is not used, and the manufacturing period and cost of the particle treatment system can be reduced.

The invention discloses an apoplexy patient management method and system based on a multi-mode RAG and LLM model, and the method comprises the steps: obtaining the standardized electronic medical record data of a patient based on an FHIR standard electronic medical record; performing feature extraction on the electronic medical record data to obtain multi-modal data; integrating and representing a time-space relationship among the multi-modal data, and obtaining a dynamically updated dynamic knowledge graph; analyzing and integrating the electronic medical record data to obtain integrated information; constructing a stroke risk causal network based on an LLM model in combination with a causal reasoning mechanism; obtaining a personalized health report based on the dynamic knowledge graph and a stroke risk causal network; and based on a personalized report result and feedback data of the patient, a treatment scheme and a management strategy of the patient are optimized in real time. According to the application, the causal reasoning mechanism is embedded into the Transform architecture of the LLM, so that the modeling and prediction capabilities of the stroke risk causal relationship are enhanced.

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

The invention relates to the technical field of burn wound treatment, and discloses an intelligent burn wound dynamic monitoring and evaluation system. The system comprises a data acquisition module for acquiring real-time physiological data of a burn wound by using a multi-source sensor; the multi-modal feature fusion module is used for generating wound comprehensive state features based on a deep belief network; the dynamic evaluation module outputs a wound healing level and a risk early warning signal through a pre-trained collaborative evaluation model; the optimization decision generation module is used for constructing a multi-target dynamic optimization model to adjust a treatment scheme according to risk early warning; and the hierarchical execution control module realizes distributed execution of treatment instructions through a hierarchical control architecture. The system can comprehensively monitor the state of the wound surface, accurately evaluate the healing risk, optimize the treatment scheme and accurately execute the treatment scheme, effectively improve the treatment effect of the burn wound surface, reduce the infection risk and shorten the healing period, and has important clinical application value.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

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 embodiment of the invention provides a water conservancy project intelligent processing system based on BIM and GIS. The water conservancy project intelligent processing system comprises the steps that material strength and construction timestamps of engineering components are extracted through a BIM parser to generate a topological relation graph; combining the remote sensing image and the geological data through a coordinate converter to determine a three-dimensional terrain field; establishing a spatial Hash grid correlation topological relation graph and a three-dimensional terrain field; constructing a digital twinborn body containing structural stress distribution and a hydrodynamic model on the basis of a correlation result; executing Monte Carlo simulation in the digital twin according to the real-time sensor data to generate a prediction scene set; when abnormal data is monitored, performing reverse tracing along the topological relation graph to generate a multi-factor association graph; and optimizing a disposal plan weight coefficient through a reinforcement learning algorithm and updating the knowledge base. Pixel-level matching of the rock-soil layer permeability coefficient and the concrete member strength is achieved, the system can recognize the coupling relation between a geological weak area and a structural high-risk point, meanwhile, a knowledge graph containing a construction time sequence is constructed, and a cause chain of quality defects can be traced.

The invention discloses a multi-modal diagnosis and treatment scheme recommendation system based on reinforcement learning, and relates to the technical field of artificial intelligence, and the system comprises a data processing module which is used for obtaining multi-modal medical data of a patient and carrying out the preprocessing of the multi-modal medical data to obtain a multi-modal data set; the feature extraction and fusion module is used for performing feature extraction and fusion based on the multi-modal data set to obtain multi-modal features as the current state of the patient; the training set acquisition module is used for constructing a decision network and inputting the current state of the patient to obtain a current diagnosis and treatment scheme, taking the current state of the patient and the current diagnosis and treatment scheme as data groups, and circulating the process to obtain a plurality of data groups to form a training set; the network optimization module is used for optimizing the decision network based on the training set and the core objective function to obtain an optimized decision network; and the optimal scheme output module is used for obtaining the current state of the patient to be recommended and inputting the current state into the optimized decision network to obtain an optimal diagnosis and treatment scheme. And the disease diagnosis accuracy and comprehensiveness are improved.

The invention is suitable for the technical field of intelligent medical treatment, and provides an intelligent decision-making system for clinical diagnosis decision analysis, which comprises a data acquisition module, a knowledge graph module, a multi-modal analysis engine, a clinical evaluation module, a clinical treatment scheme decision-making module and a feedback optimization module, the data acquisition module is used for integrating multi-source heterogeneous clinical data; the knowledge graph module is used for dynamically storing a medical entity relationship; the multi-modal analysis engine extracts features through deep learning; the clinical treatment scheme decision module is used for outputting diagnosis suggestions and providing explanations; the feedback optimization module is used for receiving a doctor correction result, automatically iterating a training model and forming closed-loop learning; according to the system, real-time association medical new discovery is supported through a dynamic knowledge graph, and hospital information system data are not effectively integrated; through cooperative work of the rule engine and the AI model, clinical medical quality and efficiency are improved.

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.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

The invention discloses a prognosis evaluation method and system for II-III stage colorectal cancer patients, and relates to the technical field of wisdom medicines.The prognosis evaluation method comprises the steps that variables related to survival prognosis of the patients are screened, potential variables influencing RFS and OS of the patients are obtained, and prognosis outcomes of the colorectal cancer patients are evaluated by evaluating relevance between various indexes and prognosis outcomes of the colorectal cancer patients and correcting confounding factors; screening out indexes having important value for the model; the screened indexes and machine learning algorithms are utilized to construct RFS and OS prognosis models of the CRC patients, the RFS and OS prognosis models are used for performing prognosis evaluation on the CRC patients in the II-III stages, and the machine learning algorithms comprise six types of LR, RF, XGB, SVC, MLP and GNB. By integrating clinical data and a machine learning technology, an efficient and accurate tool is provided for prognosis evaluation of the II-III stage colorectal cancer patient, and a scientific basis is provided for making clinical decision support and an individualized treatment scheme.

The invention discloses a myocardial infarction patient risk assessment model construction method and system, and particularly relates to the field of cardiovascular disease prediction, and the method comprises the following steps: I, collecting modal data of a hospital information system, an image filing system and wearable equipment, and integrating the modal data; according to the method, the accuracy of myocardial infarction risk prediction can be optimized, risk assessment not only depends on statistical characteristics, but also fuses pathophysiological mechanisms, the clinical interpretability of prediction is improved, the limitation that a traditional data-driven method is only based on correlation modeling is avoided, the optimal treatment scheme can be dynamically adjusted, data-driven personalized medical treatment is achieved, and the risk assessment efficiency is improved. The scientificity of intervention decision making is improved; the method can quickly adapt to new patient data, improve the individualized prediction ability, improve the data utilization rate, enhance the generalization ability of the model for different patient groups, avoid training the whole model from zero, greatly reduce the consumption of computing resources, make up for the problem of insufficient data of a single patient, and improve the prediction ability of rare cases.

System and methods are described for developing a delivery plan for a pulsed field ablation device for treating an arrhythmia. A method runs a lesion development simulation based on cardiac characteristics and a treatment plan that specifies a delivery plan and a target location to generate a simulated lesion having lesion characteristics. The method initializes a three-dimensional (3D) mesh representing a heart based on cardiac characteristics. The vertices of the 3D mesh are associated with cardiac tissue characteristics with some of the associated with cardiac tissue characteristics representing an arrhythmia source. The method adjusts the cardiac tissue characteristics of vertices of the 3D mesh to reflect the effect of an ablation resulting in formation of the simulated lesion. The method runs a lesion evaluation simulation based on the 3D mesh with the adjusted cardiac tissue characteristics to determine whether the simulated lesion would be effective at treating the arrhythmia.

The invention discloses an intelligent multidisciplinary clinical consultation system and method based on a large language model, and relates to the technical field of medical artificial intelligence. According to the structured consultation suggestion set, the association degree value of the possible disease causes and the consultation departments is calculated in combination with the medical knowledge graph, the departments participating in the consultation are automatically matched, and the consultation sub-targets are determined, so that the problem of lack of interdisciplinary knowledge integration and dynamic coordination ability in the prior art is solved, and the situation that diagnosis and treatment only depend on a single subject is avoided; meanwhile, a multi-disciplinary consultation set is formed by counting the disease cause matching number, calculating the treatment emergency value and the priority index, determining the consultation sequence of consultation departments, obtaining consultation content according to matching of consultation sub-targets and clinical guidelines, and determining the application treatment scheme in combination with patient replies and historical treatment records; according to the process, multidisciplinary knowledge is integrated, comprehensive diagnosis and treatment suggestions are provided for the patient, and the defects of the prior art on a multidisciplinary cooperation overall solution are overcome.

The invention discloses a prostate cancer endocrine therapy drug resistance prediction system based on multi-model artificial intelligence. The prostate cancer endocrine therapy drug resistance prediction system comprises a data acquisition and preprocessing module, a single omics prediction module, a multi-modal feature integration module, a model training module and a prediction result output module. Multi-omics data of a patient is collected and preprocessed, data representations of omics data of different labels are generated by using priori knowledge, and an optimal single-omics prediction model is matched for the omics data of different labels based on the data representations after knowledge enhancement. And constructing a multi-omics prediction model of the prostatic cancer endocrine treatment drug resistance by using the optimal single-omics prediction model corresponding to the different label medical data by adopting Stacking, predicting the drug resistance risk of the patient within a preset time, outputting an early warning, and adjusting the treatment scheme of the patient according to the drug resistance risk. According to the method, the multi-omics data are integrated, the accurate multi-omics prediction model for the drug resistance of the endocrine therapy of the prostate cancer is constructed, and the accuracy and clinical applicability of drug resistance prediction are improved.

A machine learning-based method of generating a radiotherapy treatment plan for a patient, comprises dose prediction and dose mimicking, wherein the dose prediction step involves using a machine learning system that has been trained to consider at least one optimality criterion related to physical or technical restrictions that will affect the delivery of the treatment plan. Thus, at least one of the factors that are normally taken into account in the dose mimicking step is introduced in the dose prediction step. The invention also relates to a method of training such a machine learning system for use in radiotherapy treatment planning, a computer program product and a computer system.

The invention relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

The invention relates to the technical field of remote monitoring, in particular to a critically ill patient remote monitoring system and method, and the system comprises a life signal collection module, a physiological state analysis module, a remote monitoring response module and a remote intervention operation module. According to the method, the data analysis and real-time feedback process is optimized, the efficiency and accuracy of medical response are improved, the key physiological indexes deviating from the normal range can be quickly and accurately recognized and analyzed by comprehensively analyzing the instant vital sign data and the historical medical records of the patient, the prediction accuracy is improved, and the prediction efficiency is improved. Medical personnel are allowed to receive clear health risk warnings at critical moments, direct communication with a medical network is enhanced through a rapid grading and alarm system, a faster medical intervention response is achieved, meanwhile, a treatment plan can be remotely and dynamically adjusted according to the current physiological state of a patient, the treatment process is more accurate, and the treatment efficiency is improved. And the survival rate and the treatment quality of patients are greatly improved.