331 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 computer-implemented method for designing a patient-specific orthopedic implant can include creating a user account associated with a patient. A patient-specific orthopedic implant can be designed based on patient data and imaging data. A healthcare provider can provide feedback for a design of the patient-specific orthopedic implant, treatment protocol, or other aspects of treatment. The patient can provide data and feedback.

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 relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

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 provides a clinical auxiliary decision-making method based on artificial intelligence and a related product, and the method comprises the steps: building a knowledge graph covering medical entities and relationships through learning authoritative medical data and medical records; a doctor inputs patient information, and data standardization and cleaning are carried out; based on the multi-dimensional information of the patient, recommending suspected diagnosis and differential diagnosis; recommending a preset examination scheme according to the suspected diagnosis and differential diagnosis results; when a doctor's advice is issued, the system checks conflicts between inspection and inspection items in real time and provides risk prompts; similar medical records are recommended based on patient clinical information, and diagnosis and treatment reference is provided for doctors. The clinical aid decision-making method based on artificial intelligence and related products have the beneficial effects that the disease list of suspected diagnosis and definite diagnosis is generated by automatically analyzing the illness state of the patient, and examination, inspection and treatment schemes are recommended in a targeted manner, so that the problems of missed diagnosis and misdiagnosis are effectively avoided.

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 present invention provides systems, methods, and devices for electroporation-based therapies (EBTs). Embodiments provide patient-specific treatment protocols derived by the numerical modeling of 3D reconstructions of target tissue from images taken of the tissue, and optionally accounting for one or more of physical constraints or dynamic tissue properties. The present invention further relates to systems, methods, and devices for delivering bipolar electric pulses for irreversible electroporation exhibiting reduced or no damage to tissue typically associated with an EBT-induced excessive charge delivered to the tissue.

The invention, which relates to the technical field of nursing management, discloses a patient information safety management system for liver, gall and pancreatic internal medicine nursing, comprising a nursing information management center. The nursing information management center is in communication connection with a patient information collection module, an information classification processing module, a health trend analysis module, a risk assessment analysis module, a risk early warning planning module and an information synchronous updating module. According to the patient information safety management system for nursing in the department of hepatobiliary and pancreatic, through implementation of the patient information safety management system, medical staff can access latest and most accurate patient data in real time, and a risk assessment analysis module comprehensively analyzes the medical history, the current illness state and the treatment progress of a patient and calculates a risk assessment coefficient; in addition, by updating the medical information of the patient in real time, making and executing a personalized nursing plan, the change of the illness state of the patient is quickly responded, and the nursing and treatment scheme is adjusted in time.

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 a personalized medical decision-making method based on artificial intelligence, and relates to the field of medical health and artificial intelligence, and the personalized medical decision-making method comprises the following steps: S1, inputting medical data of a patient in a patient information input module, including historical cases, examination reports, genomic sequences and real-time health monitoring data; and S2, based on the patient information, the patient health comprehensive evaluation module evaluates the health state of the patient, and the medical decision module outputs a preliminary treatment scheme according to the evaluation result. By collecting comprehensive medical data of a patient and comprehensively evaluating the health state of the patient based on the data, the illness state of the patient is more accurately grasped, a more accurate treatment scheme is made, individual differences, including age, physiological indexes, psychological conditions and disease duration, of the patient are fully considered, and by calculating the comprehensive evaluation score of the patient, the patient health state is comprehensively evaluated. The timeliness and effectiveness of a treatment scheme are ensured, and the rehabilitation speed of a patient is increased.

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 discloses a treatment response prediction method and system, a storage medium and a terminal, and the method comprises the steps: carrying out the heterogeneity analysis and cell division of first single cell sequencing data of a patient through employing a clustering algorithm, and obtaining a plurality of corresponding cell subgroups; performing feature extraction and biomarker recognition on all the cell subgroups to obtain corresponding cell subgroup features; wherein the cell subset characteristics comprise a cell development trajectory and a plurality of cell markers; and inputting the clinical information and the cell subset characteristics of the patient into the treatment response prediction model, so that the model performs multi-factor analysis and outputs a treatment response prediction result of the patient for each treatment scheme. By adopting the embodiment of the invention, the tiny key difference between the cells is captured, the cells are divided based on the difference between the cells, and then feature extraction and biomarker recognition are performed on each cell subset, so that the specific gene expression modes of different cell subpopulations corresponding to diseases can be recognized, and the treatment response prediction accuracy is further improved.

The invention discloses an emergency ICU ward monitoring data processing system based on big data, and relates to the technical field of big data processing, the monitoring data processing system comprises a data collection module, an abnormity marking module, a real-time data analysis module, an abnormity evaluation module and an early warning information generation module, and the modules are in electric signal connection. According to the emergency ICU ward monitoring data processing system based on big data, by analyzing vital sign data of a patient and equipment operation data, once abnormal fluctuation is found, early warning information is immediately generated and sent to medical staff, it can be ensured that the medical staff know the abnormal condition of the patient in the first time, so that processing measures are rapidly taken, and the medical staff are prevented from being damaged. Compared with a traditional manual monitoring mode, the system has the advantages that the response time is greatly shortened, the first-aid efficiency is improved, and meanwhile, the system can also provide detailed monitoring data and analysis reports to help medical staff to judge the illness state more accurately and formulate a more accurate treatment scheme.

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.

The invention relates to a patient diagnosis and treatment method based on a vector database and a medium, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting patient medical data, vectorizing the medical data comprising digital information, text information and image information, carrying out the feature fusion of the vectorized data, and obtaining a first fusion vector; calculating the similarity between the first fusion vector and a second fusion vector corresponding to each historical patient to obtain a preset number of second fusion vectors with the highest similarity; and obtaining an optimal treatment scheme based on a preset number of second fusion vectors with the highest similarity. Compared with the prior art, dependence on the diagnosis and treatment level of doctors can be reduced with the help of previous excellent diagnosis and treatment cases, a better treatment scheme can be quickly obtained, and the diagnosis and treatment level of existing doctors can be quickly improved.

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 computer-implemented method for designing a patient-specific orthopedic implant can include creating a user account associated with a patient. A patient-specific orthopedic implant can be designed based on patient data and imaging data. A healthcare provider can provide feedback for a design of the patient-specific orthopedic implant, treatment protocol, or other aspects of treatment. The patient can provide data and feedback.

The invention relates to the technical field of data processing, and discloses an esophageal cancer patient nursing remote monitoring system and method capable of improving the nursing level, and the system comprises a data collection module, a personalized data transmission module, a remote monitoring center and a remote service module. The personalized data transmission module is used for predicting potential key data of a patient by using a collaborative recommendation algorithm, distributing corresponding personalized transmission parameters to the potential key data and non-potential key data in the monitoring data, and transmitting the monitoring data to the patient based on a 5G communication technology and the corresponding personalized transmission parameters; the potential key data and the non-potential key data are transmitted to a remote monitoring center; and the remote monitoring center is used for receiving the monitoring data and performing remote monitoring analysis on the nursing condition according to the monitoring data. According to the method, the doctor seeing burden of the patient can be relieved, medical staff can obtain key data in the first time, a more appropriate treatment scheme is formulated for the patient, and therefore the overall medical care level is improved.

The invention discloses a case data analysis system based on a large model, and the system comprises a data collection module, a preprocessing module, a large model training and optimization module, an analysis module, a visualization and interaction module, a security and privacy protection module, a data preprocessing module with an innovative algorithm, a large model training module with an adaptive learning rate formula, and a disease risk prediction module with an evaluation formula. System functions and performance indexes are clear, equipment and medium requirements are clear, the steps of the method are complete, and high efficiency, accuracy and safety of case data analysis are guaranteed; the case data processing efficiency and accuracy are improved, and powerful support is provided for medical decision making. A large model is accurately analyzed, visual and convenient to check, and a security module guarantees data privacy. The medical quality can be improved, the treatment scheme can be optimized, and the method has important application value.

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 system and a method for managing patient therapy protocols are disclosed. The system includes a patient support apparatus for supporting a patient, a first input device for use by a first caregiver, a location system for determining a location of the first caregiver, and a therapy management system. The therapy management system receives a location of the first caregiver and a selection of a patient therapy protocol, which includes a location requirement. The therapy management system then determines whether the first caregiver location input signal satisfies the location requirement before initiating the selected patient therapy protocol or notifying a second caregiver if the location of the first caregiver does not satisfy the location requirement.

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.