128 results about "Treatment protocol" patented technology

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 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.

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 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.

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 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 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 belongs to the technical field of wound assessment, and discloses a chronic wound assessment and diagnosis system based on intelligent image recognition, which comprises an image acquisition module, a region recognition module, a three-dimensional reconstruction module, an assessment module and a treatment scheme recommendation module, the image acquisition module can acquire multi-source heterogeneous chronic wound image data through a unified interface protocol; and the region identification module can segment a wound region from the processed chronic wound image data through a U-Net3 network algorithm and an analysis model, generate an edge weight map, classify necrotic tissues and analyze the trend of the restored sinus tract. The wound area is accurately segmented through the deep learning network, the wound features are intelligently recognized in combination with the image, the accuracy and reliability of evaluation are improved, the dynamic three-dimensional point cloud model is further constructed by applying the binocular vision principle, the spatial form, depth, area, volume, sneaking, sinus tract and flaccidity duct information of the wound is visually displayed, and the evaluation accuracy and reliability are improved. And the subsequent wound assessment effect can be improved.

The invention provides a mattress type sleep monitoring data real-time processing method based on edge calculation, relates to the technical field of medical health data processing, and aims to perform macroscopic state layer calculation, pathological event layer calculation and stability layer calculation at the same time in a layering manner based on a constructed dynamic sensing matrix, generate a structured sleep health report, and realize real-time sleep monitoring through one-time sleep monitoring. The comprehensive information about the sleep macrostructure, the respiratory pathology event burden and the sleep stability can be synchronously output; through a sleep health assessment report, total sleep duration and sleep efficiency calculated by the macroscopic state layer, apnea and type and severity calculated by the pathological event layer, and a sleep micro-awakening state and sleep fragmentation degree calculated by the stability layer can be clearly known, and a multi-dimensional and integrated comprehensive assessment result with high medical value is realized. The information amount of the sleep health portrait is enriched, and a comprehensive and reliable data support is provided for accurately positioning the root of the sleep problem and formulating a personalized intervention or treatment scheme.

The present disclosure introduces an AI-based system (100) and method for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) over three months. The system interfaces with consumer devices through REST APIs, ensuring secure communication via HTTPS. Client modules collect and pre-process raw data for integrity and compatibility. This data is then sent to the server (104) which is then transmitted to Input module, the input module (112) authenticates and validates it. The module processes personal details, respiratory history, allergies, vaccination and medication history, and demographic data for air quality analysis. The prediction module (116) uses a CART Model with 90% accuracy to evaluate COPD risk. Based on predefined thresholds, the clinical pathway module (118) and risk prediction response module (120) offer personalized treatment protocols and clinical decision support. The system complies with ISO 13485 standards for safety and reliability.

An intelligent interactive neuromodulation system by intravesical and pelvic floor stimulation is provided, including: an electromyography (EMG) acquisition module, an intelligent diagnostic module, a display interaction module, and an electrical stimulation module. The intelligent diagnostic module is configured to receive an original EMG signal acquired by the EMG acquisition module and obtain feature information, generate a first treatment protocol by using the feature information and a preconfigured intelligent fitting model, and generate a classification result of an abnormal bladder activity based on the feature information and a preconfigured intelligent classification model; in response to identifying that a detrusor activity of a patient changes, automatically complete dynamic switching between a first treatment mode and a second treatment mode. The display interaction module is configured to receive a first treatment protocol and a second treatment protocol, and send the first treatment protocol to the electrical stimulation module to apply electrical stimulation pulses.

The invention discloses a medical data storage method based on semantic recognition, and belongs to the technical field of data processing, and the method comprises the steps: receiving medical data, carrying out the parallel processing of an unstructured medical text in the medical data through a large language model, and generating a structured semantic triple and a semantic vector representing the overall semantics; constructing a semantic graph index and a vector index to form a mixed index structure; the method comprises the following steps: receiving a natural language query, analyzing the query into a structured query condition and a query vector, forming an executable multi-modal query instruction, executing atlas query and a vector similarity search process in parallel, and mapping the structured query condition to a semantic atlas execution path for matching. In the implementation process of the technical scheme, natural language processing and a large language model are introduced, entity recognition, relation extraction and semantic role labeling are conducted on medical unstructured texts, and structured conversion of key information such as illness state description, diagnosis conclusions and treatment schemes is achieved.

A method includes displaying a cohort report; receiving a request to determine a therapy identification; causing information to be transmitted to a remote cloud server; receiving clinical trial data; and updating the cohort report. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: display a cohort report; receive a request to determine a therapy identification; cause information to be transmitted to a remote cloud server; receive clinical trial data; and update the cohort report. A computer-readable medium having stored thereon a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: display a cohort report; receive a request to determine a therapy identification; cause information to be transmitted to a remote cloud server; receive clinical trial data; and update the cohort report.

A method for determining a spinal treatment protocol for a patient in real time is provided, the method including selecting a three-dimensional imaging and processing system; the three-dimensional imaging and processing system identifying bony landmarks including the patient's head and shoulders; the three-dimensional imaging and processing system determining an edge of each shoulder; determining positions of the bony landmarks while tilting and rotating at least one of the patient's head and shoulders to provide an output; and the three-dimensional imaging and processing system processing the output to provide the spinal treatment protocol.

Metastasis is a major cause of fatality and expense in cancer. Many metastatic patients express calcified, fibrotic, or deep-seated (lymph / Jung / brain) lesions that are not easily profiled. For such patients, decision-making regarding treatment selection is population-based and heavily reliant on standards of care and are not tailored to the individual's tumor. The present disclosure provides methods of identifying treatment protocols for cancer patients using released tumor cells (RTCs). Method described herein include isolating / enriching RTCs, profiling RTCs and comparing RTCs to reference RTC profile libraries.

The invention relates to the technical field of medical treatment, in particular to a chronic infectious disease patient management system, which comprises a registration module used for registering identity information, body information, chronic infectious disease type information, chronic infectious disease condition information and other medical history information of a patient; the information database is used for storing disease condition characteristic data, historical treatment scheme data and precautions data of the chronic infectious diseases; the management platform comprises a doctor work station, a patient information station and a feedback module, and the doctor work station is used for obtaining cooperative information of a treatment scheme; the patient information station is used for generating a medicine taking scheme, a doctor seeing scheme and life precautions, and data of the patient information station is sent to the patient terminal, so that a patient obtains treatment and life related information; and the feedback module is used for inputting discomfort information of the patient. The treatment management efficiency of a hospital on the chronic infectious disease patient can be improved, and the chronic infectious disease patient can feed back own conditions in time.

The invention discloses an optimization method and device for medical large model auxiliary diagnosis and treatment and a computer program product, and the method comprises the steps: firstly carrying out the term standardization of target case information of a target patient, and keywords in a preliminary diagnosis and treatment scheme outputted by an LLM; after TF-IDF vectors and dense semantic vectors of a target keyword set composed of the keywords and the standard medical terms of the keywords are generated, fusion processing is carried out on the TF-IDF vectors and the dense semantic vectors, and mixed vectors are obtained; a clinical path knowledge graph conforming to personalized features of a target patient is dynamically constructed by using more accurate candidate knowledge texts retrieved from a medical knowledge base, and a preliminary diagnosis and treatment scheme output by a medical LLM is continuously and intelligently corrected and detected medical contradictory points are adjusted in combination with a portrait of the target patient, so that the clinical path knowledge graph is obtained. And finally, obtaining a final optimized diagnosis and treatment scheme. Therefore, closed-loop correction optimization of medical LLM auxiliary diagnosis and treatment results is realized, and an ideal optimization effect is achieved.

Computer-implemented methods for providing interactive electronic treatment assistance for benign prostatic hyperplasia and advanced prostate cancer are disclosed. The methods can include automatically sending, by a disease and disorder treatment device, an electronic pathway sign-up link to a smart mobile device. The method can include sending a plurality of inquiries about the BPH condition and / or the advanced prostate cancer condition, to the smart mobile device. The method can include receiving a plurality of patient responses from the smart mobile device. The method can include processing and storing the plurality of patient responses. The method can include providing, by the disease and disorder treatment device, patient education about various treatment therapies for advanced prostate cancer. The method can include automatically monitoring a patient having the advanced prostate cancer condition during the various treatment therapies. The method can include intervening with the various treatment therapies using the smart mobile device.

A therapeutic protocol and method for addressing posttraumatic stress and other negative emotional states through visualization techniques, dissociation, neuro-linguistic programming (NLP), and reimprinting of emotions. The protocol, known as the Trauma Resiliency Protocol (TRP) and Emotions Management Process (EMP), mitigates the psychological impacts of traumatic events. The protocol applies NLP principles, uses both secular and faith-based scripts, and incorporates specific grounding exercises to ensure emotional stability. The protocol emphasizes non-clinical interventions and peer coaching, making the protocol accessible for use by mental health professionals, peer coaches, and individuals suffering from PTSD, C-PTSD, and other related conditions. The protocol is applicable in both clinical and non-clinical settings.

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 present application relates to a method and system for evaluating drug effectiveness in the presence of a partially cured population, the method comprising the steps of: obtaining observational study sample data, determining proxy variables and covariates, and dividing the sample into a treatment group and a control group according to a treatment protocol; performing parameter estimation on the treatment group and the control group using a mixed cure model respectively to obtain a latent incurable rate and a survival function of non-cured persons for the treatment group and the control group; calculating a drug cure rate causal effect parameter based on the latent incurable rate of the treatment group and the control group; calculating a survival function of an incurable main layer based on the latent incurable rate of the treatment group and the control group and the survival function of non-cured persons; and evaluating the effectiveness of the drug based on the drug cure rate causal effect parameter and the survival function of the incurable main layer.

The invention provides an image-clinical characterization-combined multi-endpoint prognosis evaluation method for transjugular vein intrahepatic portal vein shunt, which comprises the following steps of: constructing a few-label portal vein segmentation module to obtain a preoperative CT portal vein label of a full-dose patient, and extracting deep learning features and radiomics features of the region; establishing a multi-modal interactive representation learning module for implementation, and performing cross-modal fusion with clinical features to form unified representation; and designing a multi-endpoint prognosis prediction module, inputting the data to a plurality of prognosis task decoders for postoperative survival, portal vein pressure gradient, hepatic encephalopathy prediction and the like, and adopting a multi-task learning optimization model to obtain a postoperative multi-endpoint prognosis evaluation result. According to the method, efficient fusion and multi-endpoint prognosis prediction of images and clinical information can be realized under limited labeling, clinical doctors can be assisted in preoperative patient screening and treatment scheme making, and the method has good clinical application value.

A computer-implemented method enables conversational personalization of therapeutic outputs using biomarker-specific retrieval-augmented generation. Physiological biomarkers and their relational mapping, integral coherence scores, and causal chains are provided as context to a large language model or specialized AI assistants. The system generates tailored explanations, asks clarifying questions, and iteratively refines suggestions based on user responses and updated biomarker data. Specialized assistants, each focused on a subset of biomarkers or categories, interact with the user to modify therapeutic protocols in real time. Live biofeedback can be triggered during dialogue to re-compute features and adapt outputs, achieving highly individualized coherence restoration and health and wellness guidance.

An operation room system includes a processing unit configured to receive data associated with a patient; determine a treatment for the patient; identify a control setting associated with one or more treatment devices that are (i) in communication with the processing unit, and (ii) operable to perform the treatment; and generate a display including the control setting and at least one view of the data.

An AI-based system (100) designed to assess prediabetes risk in individuals. The system (100) integrates seamlessly with the user devices via rest APIs, ensuring secure communication through https. A data collection module (122) collects raw data forwards the data to standardization and pre-processing modules (124) for preparation. The pre-processed data is transmitted to the server (104) through a network (102) using an API integration module (126) and thereafter forwarded to the input module (112) that authenticates and validates patient data, which is transmitted to the processing module (114) that refines the data, while a prediction module (116) employs a deep learning model with an AUC of 0.87 to evaluate prediabetes risk. A clinical pathway (118) and risk prediction response (120) modules provide personalized protocols, risk status, and clinical recommendations. It (100) complements clinician decision-making, adheres to ISO 13485 standards, and serves as a cost-effective community-based prediabetes risk assessment.

The invention provides a scheduling decision-making method and system for tasks in a hospital, terminal equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data; performing text analysis on the medical record data and the image interpretation data, and extracting disease semantic nodes, symptom semantic nodes and treatment scheme nodes to obtain a semantic node set; extracting timestamp information based on a time sequence of the vital sign data, the operation schedule information and the bed resource data; when matched semantic nodes and timestamp information exist, generating a node pair set with aligned time sequences; constructing an initial knowledge graph; obtaining sign image feature data of a patient, performing cross-modal correlation analysis, and updating the initial knowledge graph according to a cross-modal correlation analysis result; and searching the historical task scheduling strategy to obtain a target task scheduling strategy. According to the method, the timeliness of task scheduling in the hospital is improved, scheduling delay caused by disjunction of resource information is avoided, and the deviation of time accuracy is reduced.

The invention relates to the technical field of medical data processing, in particular to a VTE intelligent monitoring system supporting a multi-role demand, and provides the following scheme: a doctor role end constructs an evidence hypergraph based on multi-source clinical data, generates an evidence association vector through partial sequence conflict lattice merging, and stores the evidence association vector into a database; and searching in the guide version dictionary tree to obtain a current effective node, and outputting prevention and treatment suggestions and version pointing information to form a traceable evidence chain. The nursing role end receives and analyzes the tracing chain, screens matched prevention and treatment schemes from an intervention measure set in combination with real-time monitoring data, generates a nursing execution instruction and performs dynamic adjustment and feedback according to an intervention effect, so that closed-loop cooperation of risk assessment, intervention execution and result certification among multiple roles is realized, and the risk assessment efficiency is improved. And the timeliness, the traceability and the execution consistency of the prevention and treatment suggestions are ensured.

The invention discloses an automatic orthodontic risk assessment method and device, and the method comprises the steps: firstly obtaining clinical data of a to-be-assessed patient, carrying out the optimization processing of the clinical data, obtaining first optimization data, carrying out the analysis of the first optimization data through a neural network, a knowledge graph network or a large language model, and obtaining second data, the second data is processed by using a knowledge graph network or a large language model to generate a plurality of risk parameters, and finally the orthodontic risk prediction large language model is trained by using the first optimization data, the second data and the risk parameters to predict the orthodontic risk level, so that the disease and the risk of treating the disease can be comprehensively evaluated, and the risk of treating the disease can be comprehensively evaluated. The orthodontic risk assessment is automatically processed through deep learning and a large language model, the orthodontic risk can be dynamically assessed according to different treatment schemes, the accuracy of orthodontic risk assessment is effectively improved, and dependence on doctor experience is reduced.

The present invention relates to a specialized AI-driven data analytics platform tailored for optimizing engineered T cell therapies in patients, particularly those undergoing treatment for cancer, autoimmune diseases, and inflammatory conditions. Unlike general-purpose AI systems, this platform integrates advanced machine learning, deep learning, and fuzzy logic algorithms to continuously analyze and prioritize real-time data from multiple sources, including patient monitoring systems, laboratory tests, imaging modalities, wearable devices, and genomic profiles. The platform is specifically designed to predict and manage adverse events unique to T cell therapies, such as Cytokine Release Syndrome (CRS) and Tumor Lysis Syndrome (TLS), offering clinicians real-time, personalized guidance that dynamically adjusts treatment protocols during and after T cell infusion. The system's adaptive learning capabilities allow it to evolve by incorporating clinical feedback and patient outcomes, continuously refining its predictive models to enhance precision and effectiveness. By providing robust support for managing complex side effects and delivering actionable recommendations, this invention marks a significant advancement in the application of AI to oncology, offering a highly specialized, innovative approach to enhancing the safety and efficacy of engineered T cell therapies.