2023 results about "Treatment regimen" patented technology

The invention relates to the technical field of cardiovascular disease treatment, and discloses a cardiovascular disease medical decision support method based on a large model, and the method comprises the steps: obtaining multi-modal cardiovascular disease medical data, constructing a multi-source heterogeneous data set, carrying out the preprocessing and feature fusion, and constructing a combined representation model based on the pre-training large model and the fusion of traditional and western medicine knowledge. And designing a hierarchical space-time diagram convolutional network to generate a dynamic physiological state code of the patient, and constructing a multi-target optimization decision tree to generate a treatment scheme containing western medicines, traditional Chinese medicine prescriptions and non-drug intervention. A federal learning framework is adopted to cooperatively train a local model, data security is guaranteed, a global model is optimized, and a curative effect attribution module is further arranged to correct decision deviation. According to the method, multi-modal data and traditional Chinese and western medicine knowledge are integrated, personalized treatment scheme generation is achieved, data safety is guaranteed, and the cardiovascular disease diagnosis and treatment level is improved.

The invention relates to the technical field of remote medical monitoring, in particular to a medical patient remote monitoring system which comprises a health monitoring module, a disease course prediction module, a physiological regulation module, a medicine management module, a behavior analysis module, a therapy customization module, a dose adjustment module and a system comprehensive management module. According to the invention, accurate physiological monitoring is realized through a data fusion algorithm and a noise filtering technology, the accuracy and reliability of data are ensured, the recurrent neural network model can provide more accurate disease development trend analysis, early diagnosis and timely intervention are facilitated, and the accuracy and reliability of the system are improved. A self-adaptive control algorithm and a biological feedback technology enable a patient to more effectively adjust the physiological state by themselves, the treatment initiative is improved, and the safety and effectiveness of drug treatment are improved through a mode recognition algorithm and drug interaction analysis of drug management. Through deep data mining and personalized treatment strategies of the behavior analysis module and the therapy customization module, a more comprehensive and customized treatment scheme is provided for the patient.

The invention relates to the technical field of medical information, in particular to a hospital pharmacy dispensing information intelligent processing method and system, and the method comprises the following steps: S1, obtaining the basic data of a medicine in real time; s2, acquiring medical record information, current medication conditions and physiological parameters of the patient from a hospital information system; s3, analyzing an existing drug combination, and detecting potential drug incompatibility; s4, a medication scheme of the patient is automatically generated; s5, pushing the medication scheme and the taboo information generated in the step S4 to work terminals of doctors and pharmacists, and providing medication suggestions at the same time; and S6, continuously monitoring the reaction of the patient, and when abnormity is monitored, performing drug adjustment through an adjustment algorithm. According to the system and the method, the medicine data is updated in real time, the patient information is integrated, and incompatibility identification and medication adjustment are carried out by utilizing an intelligent algorithm, so that the safety and the efficiency of hospital pharmaceutical dispensing are remarkably improved, and meanwhile, the individuation and the scientificity of a treatment scheme are optimized.

The invention discloses a transcranial magnetic stimulation treatment individualized parameter optimization system based on artificial intelligence, and relates to the technical field of transcranial magnetic stimulation optimization systems. The transcranial magnetic stimulation treatment individualized parameter optimization system based on artificial intelligence comprises a data acquisition preprocessing module, a feature extraction module, a treatment parameter prediction module and a treatment effect evaluation module, and the data acquisition module is used for acquiring brain function data of a patient in real time; the feature extraction module is used for performing feature extraction on the preprocessed brain function data and clinical data; the treatment parameter prediction module is used for determining a treatment scheme suitable for the current condition of the patient according to the transcranial magnetic stimulation treatment parameter prediction result; the treatment effect evaluation module is used for continuously monitoring brain function data of the patient in the treatment process. The problems that in traditional transcranial magnetic stimulation treatment, parameter setting depends on experience and a general guide, individualized optimization and parameter adjustment are lacked, and consequently the treatment effect is inaccurate and the treatment time is long are solved.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention discloses a digital intelligent tumor prevention and treatment management platform and management method, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting the multi-modal data of a patient in real time, carrying out the standardized cleaning and space-time alignment processing, and generating a space-time tagging data set; inputting the multi-modal joint feature representation into an anti-fact inference engine, simulating potential effects under intervention of different treatment schemes, calculating individual processing effects, and generating an anti-fact rehabilitation suggestion set; according to the anti-fact rehabilitation suggestion set, a dynamic causal enhancement decision model is constructed, real-time physiological data feedback of the patient is continuously received through a near-end strategy optimization algorithm, and a treatment scheme is dynamically adjusted. According to the method, through multi-modal data space-time alignment and feature fusion, in combination with a tumor biological mechanism and a machine learning algorithm, the accuracy and clinical credibility of individualized tumor treatment scheme recommendation are remarkably improved.

The invention relates to a chronic heart failure dynamic course evolution prediction model construction method. Comprising the following steps: uniformly mapping continuous variables including LVEF and heart rate and event variables into a time trajectory frame through an event alignment and time domain nesting strategy; using a local change rate algorithm to identify inflection points including states before acute deterioration and intervention reactions in the course of disease of each patient; constructing a state fragment set for supporting hierarchical modeling in an evolution stage; a bidirectional fusion method of trajectory clustering and medical knowledge embedding is used to construct a state space with clinical interpretability including a compensation period, edge decompensation and an acute deterioration period; taking the trajectory vector as a main input, taking a state space as a prediction target, and introducing a dual-channel structure; predicting a future path based on the current state; the disease course track change of early medication / non-hospitalization / treatment scheme change is simulated; a doctor is supported to deduce a result; the change of the output state is analyzed through perturbation of the current trajectory, and key variables are found out.

The invention aims to provide an acne precise treatment and drug delivery system based on multi-modal data and feedback control. The system comprises an image acquisition and identification module, which carries out acne focus identification on an obtained skin image based on a deep learning algorithm, and extracts focus feature data; the sensor acquisition module is used for acquiring skin physiological parameters and environmental parameters related to acnes; the multi-modal fusion module carries out fusion processing on the focus characteristic data, the skin physiological parameters and the environmental parameters to generate current acne state parameters; the digital twinborn prediction module constructs a digital twinborn model of the patient according to the current acne state parameters and predicts the development trend of acnes; the individualized treatment decision module formulates an individualized treatment scheme and a corresponding administration dosage according to a prediction result of the digital twinning prediction module; and a set of system capable of realizing multi-source data acquisition, automatic evaluation, prediction analysis and personalized treatment scheme recommendation of the acne of the patient is constructed.

The invention relates to an auxiliary diagnosis and treatment method, device and equipment and a storage medium, and the method comprises the steps: verifying the quality of medical data through an intelligent matching mechanism, and generating a supplementary signal containing standardized guidance for unqualified image data in real time; a cross-modal fusion technology is adopted to integrate the tongue image, the pulse image, the image and the inspection data, and a comprehensive feature vector of a unified dimension is constructed; outputting correlation mapping of the traditional Chinese medicine syndromes and the western medicine diseases based on the pathological reasoning model, and generating a drug treatment scheme and a non-drug treatment scheme through a treatment scheme reasoning model; and finally, dynamically optimizing the dialectical logic parameters and the treatment weight parameters according to user feedback. The method solves the problems of difficulty in multi-source medical data fusion, one-sided diagnosis basis and lack of curative effect feedback closed loop, significantly improves the accuracy and adaptability of personalized diagnosis and treatment, and provides standardized intelligent support for traditional Chinese and western medicine combined diagnosis and treatment.

Characterization of drug-drug interaction properties and pharmacological properties of maribavir is useful to inform potential drug-drug interactions and dosing strategies when administering with co-medications.

The invention discloses an AIE type fluorescent probe with tumor targeting as well as preparation and application thereof, and belongs to the technical field of medicines, the probe can accurately target HDAC6 high-expression tumor cells, the specific binding capacity of the probe reduces interference to normal cells, and the detection accuracy is improved. The probe is fused with AIE performance dye molecules and emits strong fluorescence during aggregation, so that the imaging sensitivity and definition are remarkably enhanced. By optimizing the structure and synthesis conditions, the fluorescence intensity and stability of the probe are improved, the sensitivity of early tumor detection is ensured, and the survival rate and life quality of patients are improved. The preparation method is simple, convenient, feasible, low in cost and beneficial to practical application. The probe can be used for preparing tumor detection products, assisting in tumor diagnosis and classification, monitoring treatment changes and evaluating the curative effect. High-precision imaging can be realized in the aspect of tumor imaging, the method can be used for evaluating curative effect and guiding formulation of subsequent treatment schemes, and the application prospect is wide.

The invention discloses a self-adaptive aid decision-making method, system and device for patient radiotherapy, and a medium, and relates to the field of smart medical treatment. The method comprises the following steps: standardizing and preprocessing multi-source original data; performing automatic segmentation and change detection on tumor and normal tissues based on the processed data; delineating a tumor target region and a normal tissue in radiotherapy in a detection result; generating radiation therapy dose distribution according to the sketching result; evaluating the radiation treatment dose distribution by adopting an optimization evaluation method to generate a plurality of alternative treatment schemes; determining an optimal treatment regimen based on the plurality of alternative treatment regimens; and performing radiotherapy based on the optimal treatment scheme, monitoring anatomical changes of the patient in real time in the radiotherapy process, and dynamically adjusting the optimal treatment scheme. According to the invention, a plurality of operations of patient radiotherapy can be effectively integrated, and precision and individuation of patient radiotherapy can be realized, so that complex changes in the treatment process can be better coped with, and the overall treatment effect can be improved.

The invention discloses a tumor disease assessment method based on big data, and particularly relates to the technical field of tumor disease assessment. Medical data and living habit data of a patient are collected through multiple data interfaces, integration is carried out through preprocessing and a multi-modal data fusion technology, tumor microenvironment features and drug metabolism features are extracted, a comprehensive feature vector is generated, the effectiveness of a chemotherapy regimen is predicted by using a machine learning model, and when the effectiveness of the chemotherapy regimen is low, the chemotherapy regimen is identified. Newest clinical tests and drug data are introduced regularly, model parameters are dynamically optimized in combination with the chemotherapy regimen improvement effect, it is ensured that the model reflects the newest clinical progress in real time, the problems of model updating lag and drug innovation mismatching are effectively solved, the accuracy and effectiveness of a treatment regimen are improved, and the treatment cost is reduced. And meanwhile, side effects caused by blind attempts are reduced, and powerful support is provided for personalized precise medical treatment.

The invention discloses a CNN-Transform method based on multi-scale feature interaction, and mainly relates to the technical field of medical image analysis. Comprising the following steps: S1, acquiring sufficient amount of pancreas and tumor CT data, labeling the data, defining an ROI (Region of Interest), and distributing corresponding labels; s2, dividing the data set obtained in the step S1 into a training set and a test set, and preprocessing the training set and the test set by a data enhancement method including random rotation, scaling and mirroring; and S3, a CNN-Transform pancreas and tumor segmentation model PanUNet based on multi-scale feature interaction is constructed. Visual and accurate image information can be provided for doctors through accurate pancreas and tumor segmentation, personalized treatment schemes can be formulated in time, and the survival rate of patients is increased.

An apparatus and method for measuring a biosignal of a monitored patient, processing the measured biosignal to generate a treatment protocol for guiding clinical care of the monitored patient, and outputting the treatment protocol for use by a clinical caregiver. In one example, the monitored patient is a preterm infant, the biosignal is a respiratory pattern of the infant during oral feeding, and the treatment protocol provides a recommended treatment regimen setting forth therapeutic interventions and / or medical interventions for a clinical caregiver to follow for achieving a predetermined outcome for improving oral feeding of the monitored infant.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention relates to the technical field of health risk assessment, in particular to an intelligent early warning system for postoperative infection risk of orthopedic trauma. The system comprises a data acquisition module for acquiring physiological feature data and biomarker data; the key monitoring and screening module screens key monitoring patients according to the continuous abnormal representation growth degree of the initial physiological signs; the infection risk assessment module obtains a marker level index by using the initial deviation level and the growth amplitude of the biomarker, and obtains a wound infection risk coefficient in combination with the prediction condition of historical biomarker data; and the early warning report generation module is used for dividing patient early warning levels according to the physiological parameter abnormality and the wound infection risk coefficient to generate a risk report. According to the method, the evaluation accuracy of postoperative infection is improved through the physiological sign abnormity continuity and the biomarker sudden increase and prediction trend, the subsequent early warning level and risk report generation are more reliable, and a more personalized and accurate treatment scheme can be provided for a patient.

The invention discloses an intelligent analysis method and system of an oral cavity image, relates to the technical field of image processing, and aims to calculate a tooth deviation index YPZ through intelligent processing and analysis of the oral cavity image and accurately reflect the overall deviation degree of tooth arrangement and occlusion relationship. Through comparison with a preset clinical statistical deviation threshold LZ, an oral suggestion evaluation scheme can be generated, the deviation index and the deviation threshold are iteratively optimized according to feedback information of a patient, in the traditional orthodontic process, a doctor depends on experience and manual judgment, subjective errors may exist, and the adjustment process is lack of a real-time feedback mechanism, so that the accuracy of orthodontic treatment is poor. The lagging of the treatment effect is caused. Through a feedback and adjustment mechanism of the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, dynamic evaluation of the oral health condition and real-time optimization of the treatment scheme are realized by using a back propagation algorithm, and the accuracy and efficiency of the treatment effect are greatly improved.

An interactive pain management system is provided comprising a laser treatment device and an application executing on the device that receives entry of identification information for a first patient and receives entry of a body region to receive treatment. The system also receives entry of a present pain level experienced in the body region, accesses stored information describing previously reported conditions of the first patient and based on the received entries and the accessed store information, determines a treatment regimen for the first patient. The laser treatment device executes the treatment regimen by manual administration by the first patient or a qualified technician. The system requests entry of the first patient's present condition, the condition comprising one of feeling worse, feeling better, and experiencing no change since an immediately previous treatment. Changes in the condition of the first patient are incorporated into the determination of the treatment regimen.

The invention relates to the technical field of clinical treatment scheme recommendation. The invention relates to a clinical treatment scheme recommendation method based on brain diseases. The method comprises the following steps: S1, collecting brain examination data of a patient and other collectable body data, and then extracting disease features and other features of the brain; s2, collecting a historical clinical treatment scheme set and corresponding historical patient data, and combining the historical clinical treatment scheme set with the historical patient data to score the effect of the treatment scheme; according to the scheme adjusting model, the system can carry out personalized adjustment on the treatment scheme according to individual differences of patients, the unique characteristics of each patient are considered, the most suitable treatment scheme is customized for the patient, and meanwhile, the treatment effect of the treatment scheme is predicted by applying a simulation analysis technology; a doctor and a patient can roughly know effects possibly brought by different schemes before treatment, so that the doctor can select a scheme with a better curative effect expectation, and blind attempts are avoided.

The invention discloses an intelligent medical system suitable for an internet hospital, and relates to the technical field of intelligent medical treatment, and the system comprises the following steps: building an electronic health record, and obtaining the information of a patient; performing remote diagnosis and treatment according to the patient information to obtain a diagnosis and treatment result; performing medicine management according to the diagnosis and treatment result to obtain medicine management information; performing patient health monitoring according to the medicine management information to obtain patient health data; personalized health suggestions are made according to the health data of the patient, and health suggestions are obtained; performing patient health tracking and feedback according to the health suggestions to obtain health feedback; making and adjusting a rehabilitation plan according to the health feedback to obtain the rehabilitation plan; and performing health assessment and diagnosis and treatment feedback according to the rehabilitation plan. The accuracy of drug recommendation is improved, the safety and effectiveness of a treatment scheme are ensured, the scientificity and individual adaptability of health suggestions are improved, and the effectiveness and sustainability of a health management scheme are enhanced.

A method or use for managing one or more acute ischemic events. The method can include determining criteria of a clot; classifying the clot based on the criteria and generating a classification; determining an individualized treatment protocol for the clot based on the classification, the individualized treatment protocol comprising one or more techniques selected from using aspiration, restoring perfusion using a first reperfusion device, and / or restoring perfusion using a second reperfusion device; and treating the clot based on the individualized treatment protocol.

The invention discloses a gravitational field model-based lattice space segmentation radiotherapy lattice point optimization method, and relates to the technical field of radiotherapy. According to the method, by combining a PET-CT functional image, a key region, especially a hypoxic region (functional target region), of a tumor is recognized more accurately, and the positions and the distances of lattice points are dynamically adjusted according to biological characteristics of the tumor; arrangement of lattice points is dynamically adjusted through a gravitational field model, it can be guaranteed that a high-dose area accurately covers a key area of a tumor, unnecessary side effects can be reduced to the maximum extent, the risk of tumor recurrence is reduced, the method can better adapt to functional heterogeneity of the tumor, personalized treatment is achieved, and the method has good application prospects. And the influence on normal tissues is reduced while the treatment accuracy is improved, a more reliable treatment scheme is provided for treatment of complex tumors, and accurate dose distribution aiming at different tumor biological characteristics is realized.

A neurostimulation system that has a programmer which has a power supply and processors with processing circuitry and a neurostimulation device which has power supply, processors and communication circuitry for providing communication with a neurostimulation device as well as communication circuitry for communication with the programmer. The neurostimulation system is configured to deliver treatment to a patient in accordance with a clinician prescribed protocol of a regimen. The neurostimulation system compares a delivered therapy treatment which has a treatment protocol to a defined treatment protocol of a prescribed treatment and adjusts the prescribed treatment stimulation parameters to compensate for a difference between the delivered neurostimulation therapy treatment and the prescribed treatment protocol for closely approximating the prescribed treatment protocol.

The invention provides a dynamic adjustment method based on a depression mind-correcting treatment scheme, and relates to the technical field of medical care information, and the method comprises the steps: collecting multi-dimensional information of a patient, and collecting life behavior risk data, psychological scale data and cognitive function evaluation data; comprehensively evaluating the depression level of the patient; determining the priority of a treatment therapy module according to the multi-dimensional information of the patient; generating an initial treatment scheme; calculating a daily task comprehensive weight value, and dynamically adjusting the training content of the next day; and evaluating the state of the patient in stages, recalculating the priority of each therapy module, and optimizing the therapeutic scheme. Through multi-dimensional data fusion evaluation and intelligent algorithm dynamic adjustment, a complete closed-loop feedback optimization system is constructed, precision and individuation of a depression treatment scheme are achieved, the treatment effect and patient compliance are effectively improved, the recurrence risk is reduced, and a more scientific and effective treatment scheme is provided for depression patients.

The invention relates to the technical field of artificial intelligence precision medical treatment, in particular to a multi-mode hepatocellular carcinoma recurrence risk prediction method and system and application, and the prediction method comprises the steps of multi-mode data acquisition, data standardization preprocessing, model training and recurrence risk prediction. The method comprises the following steps: firstly, collecting multi-modal data of a patient, wherein the multi-modal data comprises preoperative data, postoperative data and a recurrence risk label; secondly, carrying out standardized preprocessing on the collected multi-modal data; and updating parameters of the pre-operation model and the post-operation model by using an adaptive moment estimation algorithm to obtain a trained multi-modal hepatocellular carcinoma recurrence risk prediction model which comprises the pre-operation model and the post-operation model and can be used for performing recurrence risk prediction before and after an operation respectively. According to the method, the deep learning technology is used, the multi-modal data before and after the operation can be respectively utilized, the recurrence risk of the HCC patient can be accurately predicted before and after the operation, and a targeted treatment scheme is formulated for the HCC patient.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

The present invention relates to bispecific antibodies (bsAbs) and the use of such antibodies in the treatment of disease in subjects. Moreover, advantageous treatment regimens are provided for the treatment of B-cell Non-Hodgkin Lymphoma (B-NHL).

The invention discloses a traditional Chinese medicine diagnosis and treatment aided decision-making method and system, and relates to the technical field of traditional Chinese medicine intelligent diagnosis and treatment aided decision-making, and the method comprises the steps: carrying out the wavelet transformation analysis of pulse condition waveform time series data, extracting a pulse condition dynamic change feature sequence, inputting a tongue image color change rate and a pulse condition feature change rate into a space-time convolution network, and obtaining a time-space convolution network; generating a tongue pulse dynamic association index and outputting a preliminary syndrome classification; fourier transform analysis is carried out on the spleen channel acupoint impedance spectrum, false-true state judgment is carried out in combination with tongue pulse dynamic correlation indexes, preliminary syndrome classification is corrected, and a false-true state quantification result and corrected syndrome classification are output; and matching the corrected syndrome classification with the traditional Chinese medicine knowledge map, adjusting the dosage according to a virtual-real state quantification result, reversely verifying the drug-syndrome specificity through a curative effect database, screening an optimal treatment scheme, and outputting a personalized diagnosis and treatment scheme. According to the method, the syndrome classification precision is improved, and an objective basis is provided for personalized dose adjustment.

The invention belongs to the technical field of probiotics, and particularly relates to lactobacillus reuteri ShuiMian with a sleep aiding effect and application of the lactobacillus reuteri ShuiMian. A new ShuiMian strain is separated from feces of a healthy infant in Guangzhou city, Guangdong province, PTZ is adopted to construct a zebra fish insomnia model, and research finds that the strain can inhibit eye movement frequency, inhibit brain discharge, reduce dopamine secretion, promote GABA secretion and promote Gabra gene expression, and the zebra fish insomnia model can be used for treating insomnia. The effect of promoting sleep is achieved. Therefore, the strain has a unique sleep-promoting mechanism, can assist in developing novel sleep-promoting medicines in the field of medicines, and provides a safer and more effective treatment scheme for insomnia patients. In the health care industry, a nutritional supplement can be prepared, the mass sleep quality is improved, and the development of sleep health related industries is promoted.