186 results about "Treatment protocol" patented technology

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.

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 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 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 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 preset invention discloses a handheld device for providing skin treatment, preferably a type of noninvasive skin treatment, using at least one of a set of predetermined treatment protocols comprising: - a main body comprising: - a power source, - a user interface, and - a device treatment interface comprising at least one device light source and at least one device electrode at a device treatment surface of the device, - a device attachment interface comprising electric device contacts for providing a device attachment feature for an exchangeable head unit to be powered and / or controlled by means of the electronic contacts.

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 example systems, methods, and / or computer-readable media described herein help with design treatment plans for orthodontic treatments. Treatment templates reflecting a user's treatment preferences are expressed in a domain-specific treatment language and include treatment protocols. Orthodontic treatment plans are customized based on the treatment templates and a physical condition of the patient. Treatment appliances are fabricated based on the customized orthodontic treatment plans.

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.

Methods of treating a health condition by standardizing patients and donors with pre-treatment protocols to enhance the effects of probiotic administration, improving preservation steps of samples handled, and selecting best matching donors for selection for probiotic administration to effectively treat the health condition.

To provide a surgical system causing a result effective to biological tissues.SOLUTION: An apparatus and associated method relate to controlling electrical power of an electrotherapeutic signal that is provided to a biological tissue engaged by an electrosurgical instrument during a medical procedure. Electrical power, a product of a voltage difference across and an electrical current conducted by the engaged biological tissue, is controlled according to a therapeutic schedule. The electrotherapeutic schedule can be reduced or terminated in response to a termination criterion being met. In some examples, the termination criterion is a current characteristic, such as a decrease in current conducted by the engaged biological tissue. In some examples, the termination criterion is a biological tissue resistance characteristic, such as an increase in the biological tissue resistance that exceeds a predetermined delta resistance value.SELECTED DRAWING: Figure 1

A transcranial magnetic stimulation (TMS) treatment system is provided. The system includes a sensor device that senses EEG signals from a subject through one or more leads and a server device configured to receive EEG data corresponding to the subject. The server includes an analysis module configured to process the EEG data and determine a personalized resonant brain frequency and a minimum neuronal activation threshold of the subject based at least in part on EEG data corresponding to one or more leads of the sensor device. The analysis module is also configured to determine a TMS treatment protocol where the treatment protocol includes at least a frequency based on the personalized resonant brain frequency and an amplitude based on the minimum neuronal activation threshold. The system also includes a treatment device configured to deliver a TMS treatment to the subject based on the TMS treatment protocol received from the server.

The invention belongs to the technical field of osteoarthritis management, and particularly relates to an osteoarthritis management system based on artificial intelligence and a computer storage medium. The invention provides an osteoarthritis management system based on artificial intelligence. The system integrates an LLM-DL hybrid architecture, a multi-specialist module cooperation framework and an RAG knowledge base system, and can cover a complete clinical process from patient preliminary diagnosis to treatment scheme formulation. The system can comprehensively and accurately evaluate the disease state of a patient, provides personalized disease progress prediction, not only predicts 2-year and 4-year functional results and iconography progress, but also can identify specific risk factors of the patient, and provides a basis for accurate intervention. Through the design of the whole system, the clinical working efficiency and the prediction accuracy are remarkably improved, the dependence on specialist doctors and high-end equipment is reduced, high-quality KOA management can also be implemented in resource limited areas, and therefore the overall medical resource requirement is effectively reduced.

The invention relates to the technical field of intelligent medical treatment, in particular to an AI-based brain disease test data management intelligent question-answering system, which comprises a multi-modal data acquisition module, an intelligent data processing module, an AI core model module, an interaction service module and a treatment guidance module, data aggregation is realized through a hospital interface, an equipment protocol and the like, and cleaning, standardization and multi-modal fusion are performed on multi-source data; precise question answering is achieved through the intelligent question answering unit, brain disease classification and stage division are completed through the patient layering unit, and a personalized scheme is generated by means of the treatment scheme generation unit; the interaction mode can be dynamically adjusted according to the cognitive level of a patient, emotion is recognized, guidance is provided, a doctor is supported to correct a scheme, and an iterative model is learned through feedback. The problems that in the prior art, the disease type is single, the data dimension is limited, and interaction adaptation is insufficient are solved, multi-disease-type whole-course precise management of brain diseases is achieved, and scheme scientificity and doctor-patient interaction experience are improved.

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.