56 results about "Initial dose" patented technology

The invention provides a model training method, a treatment planning method, equipment and a medium, and relates to the technical field of medical treatment, in particular to the technical field of radiotherapy. The deep reinforcement learning model is configured to comprise an action selection network and an evaluation network. The method comprises the following steps: acquiring initial dose distribution state data of a target region; determining output data of a plurality of sub-threads in parallel by using the plurality of sub-threads based on the initial dose distribution state data of the target region and the current strategy data of the action selection network and the evaluation network; updating the current strategy data of the action selection network and the evaluation network based on the output data of each sub-thread in the plurality of sub-threads in sequence, and completing the current training of the deep reinforcement learning model; and iterating the training process until the number of times of training the deep reinforcement learning model reaches a preset number of times, and obtaining the trained deep reinforcement learning model.

The invention relates to the technical field of computers, and discloses an automatic segmentation and intelligent plan generation method and device suitable for self-adaptive radiotherapy of prostate cancer. Comprising the following steps: acquiring medical image data of a treated object, clinical dose data of a target area and an organ at risk in a focus area of the treated object, and clinical treatment data of a preset priority level; performing automatic segmentation based on the medical image data to obtain target segmentation data; performing dose distribution prediction according to the target segmentation data to obtain initial dose distribution data, and performing iterative optimization on the initial dose distribution data by using clinical treatment data to obtain target dose distribution data of the treated object; and performing iterative optimization of a radiotherapy plan based on the target dose distribution data, and generating a target radiotherapy plan of the treated object. Therefore, on the basis of realizing accurate, efficient and automatic segmentation of the prostate cancer target region and the endangered organ, multi-round iterative optimization of dose distribution and the radiotherapy plan is carried out, and finally a high-quality target radiotherapy plan is obtained.

Provided herein are methods of treating a subject with heart failure, comprising administering to the subject an initial dose of a cardiac sarcomere activator (CSA) for an initial time period, and subsequently administering to the subject a dose of the CSA based on the subject's plasma concentration of the CSA at the end of the initial time period.

Provided herein are methods of treating a subject with heart failure, comprising administering to the subject an initial dose of a cardiac sarcomere activator (CSA) for an initial time period, and subsequently administering to the subject a dose of the CSA based on the subject's plasma concentration of the CSA at the end of the initial time period.

The invention relates to a self-adaptive radiotherapy dose optimization method and device and a storage medium, which are used for optimizing dose distribution in radiotherapy. Through combination of deep reinforcement learning (DRL) and a heuristic algorithm (a particle swarm optimization algorithm), the method maximizes the dose of a tumor area, and protects surrounding normal tissues at the same time. Firstly, an anatomical structure and a tumor area of a patient are extracted through medical image data, and initial dose distribution is generated; then, the deep reinforcement learning agent adjusts dose distribution according to the dynamic anatomical change of the patient, a heuristic algorithm is further optimized, and a local optimal solution is avoided; real-time feedback in the treatment process is used for adjusting dose distribution, and accuracy and timeliness of a treatment scheme are ensured. Finally, the generated optimal dose distribution scheme is provided for a clinician to refer, dose maximization of the tumor area is ensured, and normal tissue is protected. According to the method, the precision and effect of radiotherapy are improved, and a personalized treatment scheme is provided.

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.

Methods for training a deep reinforcement learning model for generating a treatment plan and an electronic device are provided in the present disclosure. The method may include performing a training process. The training process includes obtaining initial dose distribution state data of an objective target volume; determining target data based on the initial dose distribution state data of the objective target volume, current policy data of a plurality of actor network layers, and current policy data of a critic network layer; and completing the current training by updating the current policy data of the plurality of actor network layers and the current policy data of the critic network layer based on the target data; and obtaining a trained deep reinforcement learning model by iterating the training process until a count of iterating the training process reaches a preset value.

The invention discloses a radiotherapy medical information comprehensive management system, and relates to the technical field of dose management. The first proportion and the second proportion of the patient are introduced, the two threshold values are combined, and the comprehensive index, the lesion volume and the treatment cycle length of the patient are comprehensively considered, so that more accurate and personalized initial dose calculation is realized. The first proportion and the second proportion are introduced, so that the system can more comprehensively evaluate the disease progress and treatment requirements of the patient, and the problem of inaccurate dosage caused by dependence on a single parameter in a traditional method is avoided. The comprehensive indexes are constructed, multi-dimensional data such as pathological features, physiological indexes and historical reactions of the patients are fused, and the treatment scheme can be adjusted according to the specific situation of each patient. And the safety and the effectiveness of the dosage are further improved. A weight factor based on a physiological characteristic value and a physiological characteristic reference value is introduced, and the dose is adjusted in real time through a closed-loop feedback mechanism, so that the flexibility and safety in the treatment process are ensured.

A method of treating mild to moderate psoriasis in a patient by administering an IL-23-specific antibody, e.g., a paleobukumab, in an initial dose and a subsequent dose in order to respond the patient to the antibody and satisfy one or more of the clinical endpoints.

The invention provides a dose prediction method, which comprises the following steps: generating an initial vector based on initial prescription information and patient structure data; generating an initialization parameter based on the initial vector; generating initial dose distribution according to the initialization parameters; and adjusting the initial dose distribution based on a plurality of agents, and generating a target dose distribution, where a single agent is responsible for at least partial adjustment of the initial dose distribution. The invention further provides a device for executing the method and electronic equipment.

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.

Therapeutic combinations of kapasitinib and vonetog are described. The combination can be used for treating B-cell malignancies. In some embodiments, reducing the dosage (e.g., reducing the frequency and / or reducing the amount) of Venatog may mitigate or eliminate effects such as weight loss that may be experienced in an initial dosing regimen.

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.

To provide methods for the treatment of inflammatory diseases utilizing anti-IL-23A antibodies.SOLUTION: A method for treating psoriasis or psoriatic arthritis comprises administering to a patient an anti-IL-23A antibody. The method comprises: a) administering an initial dose of the anti-IL-23A antibody to the patient; b) administering a first maintenance dose of the anti-IL-23A antibody to the patient 4 to 24 weeks, e.g., 4 to 12 weeks, e.g., 6 to 24 weeks, e.g., 6 to 12 weeks, after the initial dose is administered; and c) administering at least one additional maintenance dose to the patient 4 to 24 weeks, e.g., 4 to 12 weeks, e.g., 6 to 24 weeks, e.g., 6 to 12 weeks, after the first maintenance dose is administered.SELECTED DRAWING: None

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.

This invention relates to methods of immunotherapy comprising administering allergen, such as peanut protein, to an individual in need thereof at regular time intervals. The dose of allergen increases incrementally by 1 to 10% at each administration from an initial dose to a maintenance dose. The maintenance dose of the allergen is then administered at regular time intervals. Also provided are compositions and dosing devices for use in such methods.

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.

A method for training a deep reinforcement learning model includes performing a training process. The training process including the following operations acquiring initial dose distribution state data of an objective target volume; determining, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of a plurality of sub-threads in parallel by using the plurality of sub-threads; and updating the current policy data of the actor network and the critic network based on the output data of each sub-thread of the plurality sub-threads in sequence, so as to complete a current training for the deep reinforcement learning model; and iterating the training process until a count of training the deep reinforcement learning model reaches a preset count, so as to obtain the deep reinforcement learning model that has been trained.

To provide pharmaceutical compositions for use in treating obesity.SOLUTION: Provided is a pharmaceutical composition including tirzepatide or a pharmaceutically acceptable salt thereof, which is administered subcutaneously at least one escalation dose once weekly for a minimum of four weeks and at least one maintenance dose once weekly for a minimum of four weeks following the escalation dose, the escalation dose being selected from the group consisting of 2.5 mg, 7.5 mg, and 12.5 mg of tirzepatide or a pharmaceutically acceptable salt thereof, the initial dose of the escalation dose being 2.5 mg, the escalation dose being a 2.5 mg incremental increase, and the maintenance dose being selected from the group consisting of 5.0 mg, 10.0 mg, and 15.0 mg of tirzepatide or a pharmaceutically acceptable salt thereof.SELECTED DRAWING: None

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.

The present invention provides a method and system for automatically determining the dosage of personalized medication adjustment. The present invention calculates the initial medication regimen based on the patient's physiological data and medication types, using the weighted results of a one-compartment model and a two-compartment model. At the same time, when the patient's initial medication regimen is ineffective or has adverse reactions, the patient's medication regimen is adjusted. Compared with the prior art, the present invention can not only accurately predict and adjust the medication dosage, but also provide adjustment suggestions in real time through a feedback mechanism to ensure the safety and efficacy of personalized medication.

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.