33 results about "Initial dose" patented technology

The invention discloses a dose rate measurement method, device and equipment based on Poisson maximum likelihood estimation, and a storage medium, and the method comprises the steps: obtaining and preprocessing environment nuclear radiation monitoring data, and obtaining a fusion data sequence; dividing the fused data sequence into a plurality of time windows and determining an envelope spectrum sequence; performing radiation dose rate estimation on the envelope spectrum sequence through a Poisson maximum likelihood estimation method to obtain a preliminary dose rate estimation value; determining an environment correction factor according to the temperature data sequence, the humidity data sequence and the air pressure data sequence; correcting the initial dose rate estimation value to obtain a corrected dose rate estimation value; and inputting the corrected dose rate estimated value and the average dose rate and the maximum dose rate in each time window into a support vector machine model for prediction to obtain a future nuclear radiation dose rate prediction result, and updating the optimal estimated value of the dose rate in real time by introducing a Poisson maximum likelihood estimation algorithm. The accuracy and response speed of dose rate estimation are improved.

The invention provides a dose optimization method and device, equipment and a medium, and the method comprises the steps: obtaining a functional image of a target object and the initial dose information of a target part; the functional image comprises a target part, and the initial dose information comprises initial doses of different areas in the target part; determining a damage prediction result of the target part through a pre-trained damage prediction model based on the functional image and the initial dose information; the damage prediction result is used for indicating the probability of radioactive damage at each position in the target part; based on the damage prediction result, optimizing the initial dose information to obtain target dose information of the target part; the target dose information comprises target doses of different areas in the target part. By applying the embodiment of the invention, the dose optimization effect can be ensured, the risk of radioactive injury of the target part is reduced, and the protection effect on the target part is improved.

The present disclosure provides a deep reinforcement learning model training method for generating a treatment plan, a treatment plan generation method, equipment and media, relating to the technical field of medical treatment, especially to the technical field of radiotherapy. The method comprises: obtaining initial dose distribution state data of a target target region; determining target data based on the initial dose distribution state data of the target target region, the current policy data of the plurality of action prediction network layers and the current policy data of the evaluation prediction network layer; updating the current policy data of the plurality of action prediction network layers and the current policy data of the evaluation prediction network layer based on the target data, completing the current training of the deep reinforcement learning model; iteratively performing the above training process until the training of the deep reinforcement learning model reaches a preset number of times, and obtaining the trained deep reinforcement learning model.

The invention provides an individualized ovulation promoting scheme and gonadotropin initial dose prediction method based on multi-cycle time sequence modeling and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: firstly, receiving multi-cycle clinical data and basic information of a target user for IVF / ICSI pregnancy-assisted patients, and eliminating unqualified samples to generate an initial data set; after SHAP screening features, training / test set division and SMOTE sample equalization are carried out, an initial model is constructed through a sliding window time sequence module and a multi-algorithm engine; a target model is generated through training and evaluation, and a COS scheme prediction signal and multi-period Gn dose adjustment information are generated in combination with hierarchical classification and weighted integration; calibration information is generated through clinical data verification, after target user information is subjected to standardization processing, an individualized COS scheme and a Gn initial dose are output through a target model, and multi-cycle dynamic accurate recommendation is achieved.

The invention relates to a delivery dose control method for adaptive radiotherapy, computer equipment, a storage medium and a program product. The method comprises the following steps: collecting a positron signal while carrying out first delivery on beam energy of an initial dose to a target position; stopping beam irradiation in a preset time period, and collecting a positron signal in the preset time period to generate a positron activity distribution image; registering the positron activity distribution image with a preset template, and calculating to obtain a deviation value; judging whether the deviation value exceeds a preset threshold value or not; when the judgment result is yes, the irradiation parameters of the beam are adjusted, and the target position is covered with the beam energy of the residual dose; wherein the residual dose is greater than the initial dose. Through staged dose delivery, a small amount of dose is delivered first, then the acquired activity distribution image is registered with the preset template, and the remaining dose delivery is adjusted in real time according to the deviation value, so that the subsequent dose can cover the target position, and the method is suitable for the radiotherapy plan in different times, and the dose delivery precision is high.

The invention discloses a radiotherapy plan automatic optimization design method based on machine learning, and relates to the technical field of radiotherapy, and the method comprises the steps: firstly collecting and preprocessing radiotherapy core data to generate a training set, training feature extraction, dose prediction and optimization adjustment of a model; processing to-be-processed data through a feature extraction model, screening three optimal similar cases in combination with cosine similarity, and extracting effective features; based on the feature vectors and the effective features, outputting an initial dose plan, constraint parameters and a deposition matrix by a dose prediction model; and finally, constructing a target function, and iteratively optimizing the radiation field parameters and the photon flux by adopting an improved ant colony algorithm and a conjugate gradient method. According to the method, clinical experience and individual differences are fused, the radiotherapy plan is efficiently and accurately generated, and the quality stability and clinical suitability of the plan are improved.

The use of AST-3424 in the preparation of a drug for treating a patient with hepatocellular carcinoma. A pathological paraffin block or pathological section of ex-vivo liver tumor tissue from the patient with hepatocellular carcinoma is subjected to immunohistochemical staining to detect the protein expression level of an AKR1C3 enzyme, with the detection result showing an H-score greater than or equal to 200; or the pathological paraffin block or pathological section of ex-vivo liver tumor tissue from the patient with hepatocellular carcinoma is subjected to immunohistochemical staining to detect the protein expression level of an AKR1C3 enzyme, with the detection result showing that the sum of percentages of moderate-intensity staining and high-intensity staining is greater than or equal to 70%. The dosing regimen for AST-3424 is as follows: in each 21-day cycle, administration is performed on day 1 and day 8; the initial dose is 6.0 mg / m2 per administration; and the patient will be permitted to receive treatment for a maximum of 34 cycles.

To provide a method for treating a subject having cardiac failure.SOLUTION: A method includes administering an initial dose of cardiac sarcomere activator (CSA) to a subject for a first period, and subsequently, administering a dose of CSA based on the plasma concentration of CSA in the subject at the end of the first period to the subject.SELECTED DRAWING: None

The invention belongs to the technical field of traditional Chinese medicine individuation, and provides a synergistic effect method of a traditional Chinese medicine dosage gradient scheme in insomnia treatment based on prescription dosage-efficiency science, which comprises the following steps: performing traditional Chinese medicine syndrome differentiation and typing on patients, and matching corresponding basic prescriptions according to syndromes; collecting individual characteristic parameters of a patient, and calculating an initial dose of traditional Chinese medicine according to the individual characteristic parameters; in the treatment cycle, the insomnia severity index and the Pittsburgh sleep quality index are adopted for curative effect evaluation, safety monitoring is conducted in combination with blood routine examination and liver and kidney function detection, and an evaluation result is obtained; according to an evaluation result, combining pharmacokinetic parameters, dynamically adjusting the dosage of the traditional Chinese medicine, and forming a dosage gradient scheme; synchronizing real-time lifestyle intervention and psychotherapy; through deep combination of the traditional Chinese medicine dialectical theory and modern pharmacokinetics, digital modeling and dynamic regulation and control are carried out on the dose-effect relationship of the traditional Chinese medicine, and the accuracy, safety and long-term curative effect of the traditional Chinese medicine for treating insomnia are ensured.

This invention relates to the field of transfer learning technology and discloses a method for predicting radiotherapy plan dose distribution based on domain adaptive transfer learning. The method includes: identifying source and target domain medical institutions; collecting radiotherapy plan data from the source domain medical institutions; extracting radiotherapy plan features from the radiotherapy plan data to train an initial dose distribution analysis model for the source domain medical institutions; constructing a federated learning network for the source and target domain medical institutions; outputting a dose distribution analysis map of the initial dose distribution analysis model; identifying domain differences between the source and target domain medical institutions; defining the optimization objective of the initial dose distribution analysis model; performing domain adaptive transfer learning on the initial dose distribution analysis model to obtain a local dose distribution analysis model; and generating the target dose distribution for patients in the target domain medical institutions. This invention can improve the efficiency and accuracy of radiotherapy plan dose distribution prediction.

A method and system for in-line mixing of components of a medical fluid in a mixing system (20) for mixing a medical fluid having a final predetermined composition of pure water, a small signal response (SSR) component, and optionally at least one electrolyte component. The method includes providing a fluid stream (S1) including pure water or a mixture of pure water and an electrolyte component into a main fluid line (21), monitoring the concentration of the fluid with a concentration sensor (26, 60), providing the SSR component into the fluid stream upstream of the concentration sensor (26, 60) with an SSR dosing mechanism (25a) (S3), and adjusting the dosing rate of the SSR component using the SSR dosing mechanism (25a) to determine a relationship between the dosing rate of the SSR component and the resulting concentration of the SSR component in the fluid, such that the concentration monitored with the concentration sensor (26, 60) corresponds to the intended final predetermined concentration (c) of the SSR component in the medical fluid. SSR_final ) is greater than the initial concentration of the SSR component in the fluid (c SSR_init and controlling (S4) the initial dosing rate to a value that indicates a final predetermined concentration (c) of the SSR component in the fluid using the SSR dosing mechanism (25a) based on the determined relationship. SSR_final ) is achieved. SSR_final ) (S5).

A system and method for predicting, proposing and / or evaluating suitable medication dosing regimens for a specific individual as a function of individual-specific characteristics and observed responses of the specific individual. Mathematical models of observed patient responses are used in determining an initial dose. The system and method use the patient's observed response to the initial dose to refine the model for use to forecast expected responses to proposed dosing regimens more accurately for a specific patient. More specifically, the system and method uses Bayesian averaging, Bayesian updating and Bayesian forecasting techniques to develop patient-specific dosing regimens as a function of not only generic mathematical models and patient-specific characteristics accounted for in the models as covariate patient factors, but also observed patient-specific responses that are not accounted for within the models themselves, and that reflect variability that distinguishes the specific patient from the typical patient reflected by the model.

The present application relates to the medical physics technical field, disclose a kind of radiotherapy dose prediction method, device, equipment and medium.The method comprises: obtaining the medical image of the radiotherapy region of patient, including the multiple two-dimensional slice images of radiotherapy region, planned target volume and organ at risk in radiotherapy region are region of interest;Each two-dimensional slice image and its adjacent two-dimensional slice image are input into conditional diffusion generation network, with the anatomical information of its region of interest as control condition, guide the process of gradually denoising of the noise map generated by diffusion model to the random dose distribution map of radiotherapy region, generate initial dose distribution map;Two-dimensional slice image is input into dose calibration network, and the dose of its region of interest is predicted, to calibrate the dose of region of interest in initial dose distribution map, generate target dose distribution map;The three-dimensional dose distribution of patient radiotherapy is determined in combination with the target dose distribution map of all two-dimensional slice images.

The invention relates to the technical field of medical physics, and discloses a radiotherapy dose prediction method and device, equipment and a medium. The method comprises the steps that a medical image of a radiotherapy area of a patient is acquired, the medical image comprises a plurality of two-dimensional slice images of the radiotherapy area, and a planned target area and an organ at risk in the radiotherapy area are areas of interest; inputting each two-dimensional slice image and the two-dimensional slice image adjacent to the two-dimensional slice image into a conditional diffusion generation network, and by taking anatomical information of a region of interest of the conditional diffusion generation network as a control condition, guiding a process of gradually denoising a noise graph generated by adding noise to a random dose distribution graph of a radiotherapy region by a diffusion model to generate an initial dose distribution graph; inputting the two-dimensional slice image into a dose calibration network, predicting the dose of the region of interest of the two-dimensional slice image, calibrating the dose of the region of interest in the initial dose distribution diagram, and generating a target dose distribution diagram; and determining the three-dimensional dose distribution of the radiotherapy of the patient by combining the target dose distribution diagrams of all the two-dimensional slice images.

Medication delivery devices having a dosage button which automatically returns to a prime dose setting position after dosing the device by fully depressing a dose button. An injection device having an initial dose setting suited for immediately dispensing a prime dose, wherein the injection device returns to the initial prime dose setting after the dose button is activated and released.

The invention provides an intestinal flora metabolite monitoring-based depression drug dose dynamic adjustment method and device, and is applied to the field of medical data processing. The method comprises the following steps: acquiring intestinal flora metabolite data including short-chain fatty acid concentration and drug in-vitro parameters including metabolic rate; processing the data to capture action rules of metabolites and drugs, and generating initial dose regulation reference data and metabolic risk prompts; simulating interaction under different doses, and combining a safety threshold and an active target to obtain a target adjustment parameter and a curative effect prediction report; through drug half-life period and environmental stability correction, a scheme considering both curative effect and safety is formed. Further generating a comprehensive strategy fused with a metabolism rule; and finally integrating the information to generate dose regulation effect evaluation information containing in-vitro effective rate and metabolic safety coefficient.

A method and a mixing system (20) for in-line mixing components of a medical fluid in a mixing system (20), the medical fluid having a final predetermined composition of pure water, a small-signal-response, SSR-, component and optionally at least one electrolyte component. The method comprises: providing (S1) a flow of fluid comprising pure water, or a mixture of pure water and an electrolyte component, in a main fluid line (21); monitoring (S2), with a concentration sensor (26, 60), a concentration of the fluid; providing (S3), with an SSR dosing mechanism (25a), the SSR-component into the flow of fluid upstream the concentration sensor (26, (60); and controlling (S4), with the SSR dosing mechanism (25a), the dosing rate of the SSR-component to an initial dosing rate at which the concentration monitored with the concentration sensor (26, 60) indicates an initial concentration (cSSR_init) of the SSR-component in the fluid that is greater than an intended final predetermined concentration (cSSR_final) of the SSR-component in the medical fluid, in order to determine a relationship between the dosing rate of the SSR-component and the resulting concentration of the SSR-component in the fluid. The method further comprises downscaling (S5), with the SSR dosing mechanism (25a), the dosing rate of the SSR-component to a final dosing rate (QSSR_final) at which the final predetermined concentration (cSSR_final) of the SSR-component in the fluid is achieved, based on the determined relationship.

The invention discloses a tumor patient individualized medication dose optimization method, which comprises the following steps: establishing a patient file, calculating an initial dose based on evidence-based knowledge, making a structured follow-up visit plan, integrating data from communities, families and patient reports, analyzing by using a fusion rule and an intelligent algorithm, and generating a dose optimization suggestion. After doctor-patient auditing, a situational medication reminding and recording tool is automatically updated and pushed, and finally continuous loop feedback and iteration of a treatment scheme are achieved; according to the invention, long-term follow-up visit and dosage decision are bound, and medication reminding, patient education and clinical support are combined, so that real-time, accurate and individualized adjustment of the medication dosage is effectively realized in a basic scene, and the treatment safety and compliance are improved.

The present invention concerns a method of treating peanut allergy in a subject, or a population of subjects, the method comprising administering to said subject or population of subjects a peanut allergen by an oral immunotherapy (OIT) regimen comprising a dose escalation phase in which the peanut allergen is administered in a dose which is increased in increments from an initial dose of the allergen equivalent of 5 mg peanut protein or less to a dose of the allergen equivalent of 200 mg peanut protein or more within 4 to 9 weeks from the administration of the initial dose. The treatment reduces the length of the build-up phase in peanut OIT methods and improves the likelihood or odds of the subject achieving sustained unresponsiveness.

Automated glucose level control system that provide therapy to a subject, such as glucose control, are disclosed. The system generates dose control signals using control algorithms configured to autonomously determine doses of insulin to be infused into a subject. When the system determines that an initial dose control signal does not indicate health-appropriate therapy, the system can modify a manual therapy instruction and transmit an emergency dose control signal to the medicament pump. Disclosed systems and devices can track insulin therapy administered to the subject over a tracking period, including storing an indication of the autonomously determined doses of insulin delivered to the subject. The system can generate a backup therapy protocol with insulin therapy instructions based at least in part on the insulin therapy administered to the subject over the tracking period.

The invention relates to the technical field of biological medicine, discloses a method and a system for determining the dose of an exogenous blood coagulation factor, and aims to solve the problems of large individual dose prediction deviation and adjustment lag caused by dependence on a population pharmacokinetic mean value and lack of dynamic feedback and multi-source data fusion capability in the prior art. The method comprises the following steps: collecting physiological parameters and gene information of a patient, and inputting an individualized pharmacokinetic model to generate a metabolic curve; in combination with a clinical treatment target, an initial dose is calculated through a multi-constraint numerical solution algorithm. According to the application, by integrating individual physiological and genetic characteristics, dynamic clinical feedback and an international guide knowledge graph, a closed-loop optimization system with self-learning ability is constructed, accurate and intelligent recommendation of the blood coagulation factor dose is realized, and the treatment safety and individualized level are improved.

The present disclosure provides a model training method, a treatment plan generation method, equipment and a medium, relating to the technical field of medical treatment, in particular to the technical field of radiotherapy. The deep reinforcement learning model is configured to include an action selection network and an evaluation network. The method comprises: obtaining initial dose distribution state data of a target target region; based on the initial dose distribution state data of the target target region and the current policy data of the action selection network and the evaluation network, using multiple sub-threads to determine the output data of the multiple sub-threads in parallel; based on the output data of each sub-thread in the multiple sub-threads in turn, updating the current policy data of the action selection network and the evaluation network, completing the current training of the deep reinforcement learning model; iteratively performing the above training process until the number of training of the deep reinforcement learning model reaches a preset number, obtaining the trained deep reinforcement learning model.

The present disclosure provides a dose prediction method, comprising: generating an initial vector based on initial prescription information and patient structure data; generating an initialization parameter based on the initial vector; generating an initial dose distribution according to the initialization parameter; and adjusting the initial dose distribution based on a plurality of agents to generate a target dose distribution, wherein a single agent is responsible for at least partial adjustment in the initial dose distribution. The present disclosure also provides an apparatus and an electronic device for performing the method.

The present invention concerns a method of treating peanut allergy in a subject, or a population of subjects, the method comprising administering to said subject or population of subjects a peanut allergen by an oral immunotherapy (OIT) regimen comprising a dose escalation phase in which the peanut allergen is administered in a dose which is increased in increments from an initial dose of the allergen equivalent of 5 mg peanut protein or less to a dose of the allergen equivalent of 200 mg peanut protein or more within 4 to 9 weeks from the administration of the initial dose. The treatment reduces the length of the build-up phase in peanut OIT methods and improves the likelihood or odds of the subject achieving sustained unresponsiveness.

The invention provides a method for treating hepatocellular carcinoma by using AST-3424 and application of the AST-3424 in preparing a medicine for treating a hepatocellular carcinoma patient, and is characterized in that the expression level of AKR1C3 zymoprotein is detected by a pathological wax block or a pathological section of an in-vitro liver tumor tissue of the hepatocellular carcinoma patient through an immunohistochemical staining method, and the score of a detection result H-score is greater than or equal to 200; or the expression level of AKR1C3 zymoprotein is detected by the pathological wax block or pathological section of the in-vitro liver tumor tissue of the hepatocellular carcinoma patient through an immunohistochemical staining method, and the detection result shows that the sum of the percentage of moderate-intensity staining and the percentage of high-intensity staining is greater than or equal to 70%. According to the administration scheme of the AST-3424, the AST-3424 is administered once on the first day and the eighth day every 21 days, the first dose is 6.0 mg / m < 2 > each time, and the AST-3424 is allowed to receive treatment of 34 periods at most.

The invention belongs to the technical field of injectors, and particularly relates to an instrument for inputting a medium into a human body, in particular to a dose termination nut, an anti-rotation assembly and an injection pen. According to the dose stopping nut, the anti-rotation assembly and the injection pen, the limiting hook arranged at the bottom of the nut body is in interference fit with the limiting rib arranged on the transmission connecting rod, so that the dose stopping nut is connected with the transmission connecting rod; therefore, when the dose termination nut and the transmission connecting rod are installed, the situation that the dose termination nut rotates and displaces due to shaking, the initial dose position of the dose termination nut is changed, and the accuracy of injection dose is affected is avoided.

The invention relates to the field of experimental animal model construction, and discloses a method for improving the stability of a pigeon hyperuricemia model, which comprises the following steps: selecting and monitoring: selecting healthy adult pigeons, and obtaining individual metabolic parameters through pharmacokinetic experiments and circadian rhythm monitoring; the pigeons are placed in a controllable environment to be bred, the initial dosage of an inducer is calculated through a space-time coupling model on the basis of individual metabolic parameters, and the inducer comprises a uric acid precursor substance and a uricase inhibitor; and optimization induction: according to the initial dose, giving an inducer to the pigeons in stages, optimizing the dose of the inducer in real time in combination with a predictive control algorithm, and inducing the pigeons to generate hyperuricemia. Through combination of a space-time coupling model in a controllable environment, individual metabolic parameters are converted into adaptive initial doses, neglect of fixed doses on individual differences is avoided, and through staged administration and combination of a predictive control algorithm, the doses are dynamically adjusted to compensate metabolic fluctuations.

The invention relates to the technical field of radiotherapy plan assistance, and discloses a chest tumor intensity modulated radiotherapy assisted analysis method based on AI dose prediction, and the method comprises the steps: obtaining a four-dimensional CT, a plan CT, a radiotherapy plan, and an AI initial dose prediction result; establishing a treatment coordinate system and unifying coordinates; calculating a three-dimensional electron density gradient field and a cleaned three-dimensional deformation vector field; analyzing the beam to generate a two-dimensional flux gradient, and calculating a physical instability index in combination with a projection motion vector; calculating an anatomical motion coupling index based on the deformation field and the density gradient field; and fusing the two indexes to generate a failure risk index and a confidence map, and performing superposition display in a thermodynamic diagram form. According to the method, a high-risk area of AI prediction failure is visually recognized by quantifying the flux cutting and anatomical motion coupling effect, the defect that a static model neglects dynamic characteristics is overcome, and the reliability of chest tumor intensity modulated radiation therapy auxiliary analysis is improved.

The present invention concerns a method of treating peanut allergy in a subject, or a population of subjects, the method comprising administering to said subject or population of subjects a peanut allergen by an oral immunotherapy (OIT) regimen comprising a dose escalation phase in which the peanut allergen is administered in a dose which is increased in increments from an initial dose of the allergen equivalent of 5 mg peanut protein or less to a dose of the allergen equivalent of 200 mg peanut protein or more within 4 to 9 weeks from the administration of the initial dose. The treatment reduces the length of the build-up phase in peanut OIT methods and improves the likelihood or odds of the subject achieving sustained unresponsiveness.

This invention relates to the field of phototherapy technology, and more particularly to a method and system for intelligent adjustment and monitoring of phototherapy dose. An initial dose plan is generated based on the initial physiological state information of the skin area to be treated. The area is divided into multiple spatial sub-regions, and baseline tolerance thresholds and conduction coefficients are recorded. The response intensity of each sub-region is monitored in parallel. The cross-influence between adjacent sub-regions is calculated using a diffusion-response coupling equation. A cumulative matrix of cross-influence is constructed and superimposed on the baseline tolerance threshold to obtain the predicted tolerance threshold and remaining tolerance margin. While maintaining a constant overall cumulative energy density, the differentiated light intensity adjustment amount is calculated based on the remaining tolerance margin and then executed. This achieves intelligent and personalized phototherapy dose adjustment, improving treatment safety and effectiveness.