253 results about "Treatment protocol" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

A system for administering treatment with a medical device includes a medical device capable of delivering a medical treatment to a patient. An electronic computing device has a processor, computer-readable memory, and a power supply. The electronic computing device is in communication with the medical device. A patient interface is in communication with the electronic computing device and the medical device. The electronic computing device is configured to: receive an initial electronic treatment protocol; communicate the initial electronic treatment protocol to the medical device to enable an initial treatment of the patient; receive patient input through the patient interface; receive an updated treatment protocol based on the patient input; and communicate the updated treatment protocol to the medical device to enable an updated treatment of the patient.

A system for administering transcranial neuromodulation (TNM) treatment with a medical device capable of delivering a TNM treatment to a patient including an electronic computing device has a processor, computer-readable memory, and a power supply, in communication with the TNM device. A patient interface is in communication with the electronic computing device and the TNM device. The electronic computing device is configured to: receive an initial TNM treatment protocol; communicate same to the TNM device to enable initial TNM treatment; receive patient input through the patient interface; receive an updated TNM treatment protocol based on patient input; and communicate updated TNM treatment protocol to the TNM device to enable updated TNM treatment of the patient.

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

Systems, devices, and methods for electrically stimulating peripheral nerve(s) to treat various disorders are disclosed, as well as signal processing systems and methods for enhancing diagnostic and therapeutic protocols relating to the same. Personalized therapy based on algorithms and aggregated data are also provided.

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 belongs to the technical field of ophthalmology medical intelligence, and provides an ophthalmology remote intelligent consultation system and method based on multi-modal data fusion, and the method comprises the steps: collecting and integrating ophthalmology multi-modal data, generating a multi-modal feature set, and inputting an improved cross-modal attention model, generating a final fusion feature through dynamic weight fusion and a two-way interaction mechanism, outputting the ophthalmic disease probability distribution of the patient in combination with a local ophthalmic knowledge graph, quantifying detailed information of ophthalmic experts as expert feature vectors, calculating the matching degree between the ophthalmic disease probability distribution of the patient and the expert feature vectors, and determining the ophthalmic disease probability distribution of the patient. The method comprises the following steps of: screening high-matching-degree ophthalmology experts for consultation, calculating recommended values of treatment schemes provided by the ophthalmology experts and generating an electronic consultation sheet, supplementing local case data to train a local knowledge graph, and generating a global knowledge graph and performing feedback updating on the local knowledge graph by a central server through weighting and aggregating the local knowledge graphs of multiple mechanisms.

The invention relates to the technical field of treatment scheme optimization, and provides an emergency treatment critical patient first-aid path optimization method and system based on big data, and the method comprises the steps: building a standardized event sequence through extracting historical diagnosis and treatment data of a hospital information system, and analyzing the time sequence correlation between events through a graph structure mining technology, key diagnosis and treatment nodes and high and low frequency paths are identified, and a layered first-aid path state transition network is constructed based on the key diagnosis and treatment nodes and high and low frequency paths. When a real-time patient is processed, the system matches an electronic health record of the patient with a network core state, dynamically plans a subsequent standard transfer path, and avoids a low-frequency risk path marked as a taboo, so that an emergency path optimization scheme of risk avoidance and path dynamic optimization is realized, and the treatment efficiency of emergency critical patients is improved. The treatment effect is improved, and the bad prognosis risk is reduced.

The embodiment of the invention discloses a spinal metastatic tumor treatment scheme generation system. According to one specific embodiment, the system comprises a data processing server, an information fusion server and a scheme generation server which are in communication connection with one another, and the data processing server is used for preprocessing multi-source patient data to obtain standard multi-source patient data; the information fusion server is used for executing the following steps: performing feature code fusion on standard multi-source patient data to obtain a multi-source patient feature vector; performing feature enhancement on the multi-source patient feature vector to obtain a joint patient characterization vector; generating an initial therapeutic schedule result based on the joint patient characterization vector; and the scheme generation server is used for performing feature decision processing on the initial treatment scheme result to obtain a final treatment report. According to the embodiment, waste of computing resources can be reduced, and system response time can be shortened.

The invention provides a self-adaptive clinical trial design method based on machine learning, relates to the technical field of clinical trials, and adopts a method integrating reinforcement learning, causal inference and data enhancement technologies to predict an optimal treatment scheme and adjust a distribution proportion in real time through a dynamic optimization module, and limits high-risk treatment selection in combination with dynamic ethical constraints. The problems that a traditional clinical test preset rule lacks real-time performance and neglects patient safety are solved, a data collection and enhancement module generates virtual patient data conforming to a causal relationship, real and virtual data are combined to form a unified data set, a high-quality data set is constructed, the data size is effectively expanded, the statistical power of a small sample scene is improved, and the accuracy of a clinical test result is improved. The termination rule module of comprehensive statistics, ethics and resources shortens the test period through weighted evaluation, significantly reduces the risk of improper allocation of high-risk groups, enhances the benefit of patients, ethics compliance and test result reliability, and expands the application range in rare disease research and personalized medical treatment.

Systems and methods are provided for modeling and for providing a graphical representation of tissue heating and electric field distributions for medical treatment devices that apply electrical treatment energy through one or a plurality of electrodes. In embodiments, methods comprise: providing one or more parameters of a treatment protocol for delivering one or more electrical pulses to tissue through a plurality of electrodes; modeling electric and heat distribution in the tissue based on the parameters; and displaying a graphical representation of the modeled electric and heat distribution. In another embodiment, a treatment planning module is adapted to generate an estimated target ablation zone based on a combination of one or more parameters for an irreversible electroporation protocol and one or more tissue-specific conductivity parameters.

The invention discloses a remote intelligent home peritoneal dialysis system, a peritoneal dialysis control method, a terminal and a medium. The system comprises a patient terminal device, a remote server and a mobile terminal. The patient end device is used for collecting and transmitting multi-dimensional data. And the remote server is used for collecting the multi-dimensional data collected by the patient end equipment, analyzing and processing the multi-dimensional data by using an AI model, and outputting early warning information, a prediction result and a treatment scheme. And the mobile terminal is used for acquiring the early warning information, the prediction result and the treatment scheme pushed by the remote server, and generating a visual data board. The remote intelligent home peritoneal dialysis system provided by the invention is beneficial for helping a patient clearly know the health condition of the patient, meanwhile, a doctor can carry out omnibearing monitoring on the patient, and three-in-one integration of all-dimensional monitoring, AI intelligent decision making and doctor-patient closed-loop management is facilitated.

The invention discloses a personalized acupuncture treatment scheme recommendation method and system based on data analysis, and relates to the technical field of acupuncture. The method comprises the following steps: constructing a feature vector matrix according to multi-dimensional feature data of a target patient; taking the matrix as a constraint, retrieving similar historical cases in a pre-constructed acupuncture case database, and obtaining a sample case set and a similarity set; configuring a data selection strategy based on the similarity set, constructing a sample training set, and training to generate an acupuncture parameter evaluation plug-in; and defining an acupuncture parameter optimization space based on the sample case set, performing iterative optimization in the space by using the evaluation plug-in, and finally outputting an acupuncture treatment parameter scheme adaptive to the target patient. According to the method, the whole-process personalized recommendation of the acupuncture treatment scheme from case screening, model evaluation to parameter optimization is realized, and the accuracy, objectivity and clinical operability of treatment are improved.

A method includes receiving natural language input text characterizing clinical data for a patient. The method also includes receiving a prompt composition that includes adjudication rules for performing lines of therapy adjudication and an instruction parameter that specifies a task for a LLM to synthesize a group of multiple synthetic experts that each use the adjudication rules to perform chain-of-thought reasoning for making lines of therapy (LoT) adjudication decisions. The method also includes structuring an adjudication prompt by concatenating the prompt composition to the natural language input text, processing, using the LLM, the adjudication prompt to cause the LLM to synthesize the group of multiple synthetic experts and generate a respective group answer. The method also includes determining a final answer based on the respective group answer generated from the group of multiple synthetic experts.

The invention discloses an emergency rescue room multi-parameter collaborative diagnosis and treatment system based on intelligent chip operation, and relates to the technical field of emergency rescue. The system comprises a data acquisition and preprocessing module, an intelligent chip operation module, a diagnosis decision support module, a multi-department collaboration module, a data storage and management module and a user interface module. According to the system, multi-parameter data is collected and integrated in real time, analysis is carried out by utilizing the rapid operation capability of the intelligent chip, accurate diagnosis suggestions and treatment schemes can be provided for medical staff in a short time, the diagnosis and treatment decision time is greatly shortened, and the emergency rescue efficiency is improved. Various physiological parameters are comprehensively analyzed, the limitation of single parameter analysis is avoided, the illness state of a patient can be judged more comprehensively and accurately, and misdiagnosis and missed diagnosis are reduced.

Systems and methods are provided for modeling and for providing a graphical representation of tissue heating and electric field distributions for medical treatment devices that apply electrical treatment energy through one or a plurality of electrodes. In embodiments, methods comprise: providing one or more parameters of a treatment protocol for delivering one or more electrical pulses to tissue through a plurality of electrodes; modeling electric and heat distribution in the tissue based on the parameters; and displaying a graphical representation of the modeled electric and heat distribution. In another embodiment, a treatment planning module is adapted to generate an estimated target ablation zone based on a combination of one or more parameters for an irreversible electroporation protocol and one or more tissue-specific conductivity parameters.

A system for personalized neuromodulation includes a neuroimaging recording device including sensors for detecting neural activity in a patient. An optimization module determines a personalized neuromodulation treatment protocol for the patient. The optimization module includes a neuromodulation target optimization module for determining neural targets in the patient for the personalized neuromodulation treatment protocol. The optimization module includes a neuromodulation parameters optimization module for determining optimized neural stimulation parameters for the personalized neuromodulation treatment protocol. A personalized neural stimulation device receives the neural targets from the neuromodulation target optimization module and receives the optimized neural stimulation parameters from the neuromodulation parameters optimization module. The personalized neural stimulation device delivers the personalized neuromodulation treatment protocol to the patient. The personalized neural stimulation device includes at least one neural stimulation device for stimulating the targets in the patient. A neuroimaging navigation system determines a placement of the neural stimulation device(s) about the patient.

The invention relates to the field of medical information processing, in particular to a deep learning-based medical multi-source heterogeneous data fusion and knowledge discovery system, which comprises a heterogeneous data management and mapping module, a medical data unified representation module, a medical knowledge mining engine, a knowledge-driven decision support module and a hypothesis verification and visual exploration module, the system utilizes the Riemannian manifold theory to construct a multi-modal medical data unified characterization framework, realizes multi-scale medical knowledge characterization in combination with a curvature perception mechanism, excavates multi-entity association through multi-view learning and a graph neural network, calculates the consistency among diseases, symptoms, signs and genes based on a knowledge graph, provides accurate diagnosis and treatment schemes, and realizes the purpose of accurate diagnosis and treatment. According to the method, the technical problems that medical multi-source heterogeneous data are difficult to fuse, complex association is difficult to mine and knowledge representation is not accurate enough are effectively solved, and the data utilization efficiency, representation quality and diagnosis accuracy are remarkably improved.

The present disclosure provides apparatus and methods to both infer stent expansion (vessel expansion or compliance) and probability of procedural success (vessel patency) from pretreatment diagnostic imaging at the point-of-care and also train an inference model to generate such an inference, thus allowing a physician to choose with greater certainty an optimal treatment tool and treatment protocol for treating a vessel of a patient.

Systems, devices, and methods for electrically stimulating peripheral nerve(s) to treat various disorders are disclosed, as well as signal processing systems and methods for enhancing diagnostic and therapeutic protocols relating to the same.

A data processing system is configured to identify treatment responsive to a health risk determined from feature data provided by one or more networked data sources. A classification engine generates a feature vector based on a natural language processing (NLP) of input data representing words provided by a user. Features of the feature vector represent health risk factors. Machine learning logic classifies the features to generate a classification metric indicating whether the features are indicative of health risks or not indicative of health risks. A prediction value is generated indicating a likelihood of each health risk factor for the patient. The patient can be diagnosed with a health condition or disease based on the identified health risks.

The invention discloses a medical aid decision-making system based on a multi-modal large model, and relates to the technical field of medical artificial intelligence, and the system comprises a multi-modal data fusion module, a treatment scheme coding and management module, a prognosis prediction model module, a scheme simulation and deduction module, and a visual comparison module. The comprehensive state vector of a patient and the action vector of a candidate treatment scheme are jointly calculated, multiple long-term prognosis indexes after the scheme is executed are directly simulated, the model converts the treatment scheme into a computable variable by learning rules in historical treatment data, probabilistic prediction is carried out on a future result, and the prediction accuracy is improved. This enables a doctor to clearly see the risk brought by different selections and the long-term influence of treatment changes before making a decision, thereby converting the decision mode from experience-based inference to future simulation-based anticipation.

The invention relates to the technical field of physiological status monitoring, in particular to a urinary surgery critical patient intelligent monitoring system which comprises a time sequence analysis module, an illness state prediction module, a complication alarm module, a dynamic monitoring adjustment module, a data optimization extraction module, a health data comprehensive analysis module, a monitoring parameter calibration module and a curative effect evaluation module. According to the method, the autoregressive integral moving average and seasonal decomposition algorithm and the long-short-term memory network are combined, accurate time sequence analysis is performed on the medical data, the convolutional neural network and the recurrent neural network are combined, and the autoencoder and the attention mechanism are matched, so that the disease prediction accuracy is improved; through the application of the random forest, the gradient elevator algorithm and the isolated forest, the early recognition reliability of complications is improved, timely prevention is facilitated, the dynamic monitoring and adjusting module adjusts parameters according to real-time data, the data optimization and extraction module deeply analyzes health data, and the effectiveness and pertinence of a treatment scheme are ensured.

Embodiments treat inflammation of a user by applying a first patch on a dermis of the user adjacent to an afferent nerve, the first patch comprising first electrodes, and applying a second patch on the dermis of the user adjacent to a vagus nerve or a vagus nerve connecting branch, the second patch comprising second electrodes. Embodiments generate a treatment protocol during a time period comprising simultaneously applying a first electrical stimuli to the afferent nerve via the first electrodes of the first patch and applying a second electrical stimuli to the vagus nerve or the vagus nerve connecting branch via the second electrodes of the second patch.

The invention relates to the technical field of medical data processing, and discloses an operation period treatment risk intelligent prompting method, which comprises the following steps of: acquiring physiological index time sequence data and a treatment scheme execution parameter matrix of a perioperative period patient, generating a composite state tensor through multi-source feature correlation calculation, constructing a risk-intervention correlation nodal topological map, and calculating the risk-intervention correlation nodal topological map. Inputting a risk transfer model to simulate a risk transfer path and outputting an association influence probability value set, establishing a bidirectional association equation through space-time association mapping operation to generate a risk-intervention coupling prediction curve, calculating a risk prompt factor, and performing operation on the risk prompt factor and an association influence probability value to generate a standardized thermodynamic diagram; and extracting the operation quantitative index set, dynamically comparing the operation quantitative index set with a preset safety threshold value, and generating an intelligent prompt scheme containing a risk early warning level and an intervention scheme adjustment strategy when the index breaks through the threshold value. According to the method, the accuracy and effectiveness of risk prompting during the operation period are improved.

The embodiment of the invention provides a hemodialysis fatigue patient-oriented health monitoring and propaganda treatment system, which comprises a cloud server and a first communication device, the patient holds the first communication device, and the cloud server comprises a health data collection module used for obtaining subjective health data and objective health data of the patient; the objective health recognition module is used for determining a first health state of the patient according to the objective health data; the subjective health recognition module is used for recognizing emotion information of the patient according to the objective health data; the reminding propaganda and education module is used for determining a pushing time point according to the living habits of the patient, the first health state and a preset treatment scheme, and pushing treatment reminding and health propaganda and education data to the first communication equipment at the pushing time point in combination with the emotion information of the patient; according to the system provided by the invention, an omnibearing and intelligent health management and adjuvant therapy platform is provided, help can be provided for the patient physiologically and psychologically at the same time, and the compliance of the patient to treatment is improved.

The invention relates to the technical field of intelligent medical treatment and clinical nutrition, and discloses a multi-dimensional sign sensing personalized enteral nutrition infusion regulation and control device which comprises a multi-parameter sign sensing module, an intelligent analysis control module, an adjustable infusion execution module and a man-machine interaction and early warning module. The intelligent nutrition management platform is in communication connection with the modules; the intelligent nutrition management platform is composed of a nutrition screening and evaluation unit and a statistical analysis unit. According to the method, a full link from nutrition risk automatic screening, individualized scheme intelligent generation, bedside safety execution and self-adaptive regulation and control to whole-process data statistical analysis is formed; information integration and initial decision making are automatically completed, manual operation links and cognitive loads are reduced, scientificity and normalization of a treatment scheme based on newest clinical guidelines and patient data are ensured, and medical workers are liberated from tedious transactional work and focus on higher-value clinical judgment.

The invention discloses a muscular stiffness symptom quantitative evaluation method based on machine learning and related equipment. The method comprises the following steps: collecting a preliminary data set of a patient, wherein the preliminary data set comprises physiological signal feature data and label data; dividing the data set into a training set and a test set to train the evaluation model, and performing accuracy verification and optimization on the trained evaluation model, the evaluation model comprises an XGBOOST model constructed on the basis of arm joint resistance data, a Light GBM model constructed on the basis of arm joint electromyographic signals and a random forest model constructed on the basis of arm joint tremor signals; and acquiring physiological signal feature data of a target patient, and performing quantitative evaluation on the myotonia symptom of the target patient through the optimized evaluation model. The problems that existing diagnosis and symptom evaluation of myotonia mainly depend on subjective judgment of clinicians, objective quantitative standards are lacked, and formulation of treatment schemes and evaluation of curative effects are affected can be solved.

The invention discloses a traditional Chinese and western medicine combined clinical decision support system and method, and relates to the technical field of medical equipment, the system comprises a user interface module, a data management module, a knowledge base module, a rule engine module and a decision analysis module, the user interface module is used for obtaining patient information input by a clinician; the data management module is used for performing data cleaning and Chinese and western medicine medical term mapping processing on the patient information; the knowledge base module is used for tracking and updating medical knowledge and diagnosis and treatment guidelines in real time; the rule engine module is used for establishing a medical diagnosis and treatment rule base according to the medical knowledge and the diagnosis and treatment guide, calling a corresponding medical diagnosis and treatment rule from the medical diagnosis and treatment rule base according to the processed patient information, and generating a preliminary diagnosis result and a treatment scheme; and the decision analysis module is used for further performing decision analysis and optimization to obtain a final diagnosis result and a treatment scheme. The accuracy and efficiency of medical decision making of the clinical decision support system can be improved.