9 results about "Clinical effectiveness" patented technology

The invention discloses an accurate rhythm generation method based on a generative adversarial network. The method comprises the following steps: data preprocessing; carrying out data downsampling processing; performing standardized optimization; an optimized GAN architecture model with CNN-LSTM fusion is constructed; setting a DCMA start-stop strategy; making a multi-stage evaluation screening strategy, and making a screening strategy to carry out further examination and screening on the generated data; constructing a simulation database; verifying the validity of the enhanced data set; and experiment comparison. Through the multi-stage screening mechanism, the powerful learning ability of the GAN model in time series data generation can be fully played, and the physical authenticity and clinical effectiveness of generated ECG data can be strictly ensured, so that high-quality and balanced-distribution training data is provided for the AI diagnosis model, the problem of data long-tail distribution is effectively relieved, and the method has the advantages of being high in practicability and high in practicability. The diagnosis performance of the model on rare arrhythmia is improved, and clinical transformation of an ECG intelligent analysis technology is promoted.

The invention relates to the technical field of artificial intelligence, and provides a multi-modal perception nerve regulation method and device, and the method comprises the steps: obtaining a regulation target of a user and a current psychological state portrait obtained based on the current multi-modal physiological data; on the basis of the current psychological state portrait, the previous psychological state portrait and an adjustment reward function constructed based on expert knowledge corresponding to the adjustment target, a previous adjustment reward is obtained through calculation; and inputting the current psychological state portrait, the adjustment target and the previous adjustment reward into a neuro-adjustment feedback strategy model to obtain a current adjustment strategy output by the neuro-adjustment feedback strategy model. According to the method provided by the invention, closed-loop intelligent decision-making under multi-modal perception and expert knowledge guidance is realized, the clinical effectiveness and safety of a feedback strategy are ensured, and further based on an adjustment strategy capable of dynamically adapting to the state change of a user, highly personalized, accurate and efficient nerve adjustment is realized.

A system may model, in a computer system, a first model representing a plurality of decision-options. A system may model, in the computer system, a second model representing an effectiveness of the plurality of decision-options. A system evaluates sensitivities, uncertainties, and alternative scenarios related to the plurality of decision-options of the first model and the effectiveness of the plurality of decision-options of the second model. A system provides an output indicating recommendations and tradeoffs among the set of design options selected from the plurality of decision-options. A system and method for decision-making support in medical technology strategy integrates Multi-Attribute Tradespace Exploration (MATE) and Clinical Cost-Effectiveness Analysis (CEA) models. The system models a plurality of decision-options, and evaluates their effectiveness using clinical effectiveness metrics such as quality-adjusted life years (QALYs). The system optimizes these decision-options by performing probabilistic analyses, including Monte Carlo simulations, to account for uncertainties.

Described herein are methods and computer devices for interpreting human input upon reviewing an orthodontic treatment plan and making requested changes to the plan or providing requested information on the plan A multi-modal user interface processes the inputs, classifying them as either requests for information or requests for changes to the proposed treatment plan. When changes are requested, the user interface passes appropriate parameters to one or more modules to implement those changes. In handling requests for changes, the computer device involves determining whether the inputs are intended as: (1) modifications, adjustments, or refinements to the target dentition associated with the treatment plan, directing these to a refinement module that implements these adjustments to the current target dentition while minimising the non-requested changes effected to the target dentition in order to ensure clinical validity; or (2) major changes, such as alterations to clinical treatment parameters and preferences, which are directed to an automated treatment planning module that re-computes a new treatment plan, including a new target dentition, based on the original dentition using updated criteria.

A System and method enhance clinical effectiveness for monitoring for a change in a level of fluid in tissue by using a device attached to a body portion that wirelessly reports to a remote apparatus or receiver power level for indications of extravasation or infiltration. Adjusting an activation rate of fluid detection, reporting or both extends service life of the device.

This invention discloses a Markov chain Monte Carlo sampling method based on a dual-constraint set projection mechanism, which solves the problems of poor target distribution adaptability, slow convergence, and sensitivity to initial values ​​in traditional MCMC sampling in personalized drug delivery scenarios. The method includes: sampling initialization, using the ppk+MAPB algorithm to construct a four-layer nested target probability distribution model; determining a multivariate normal distribution as the proposed distribution; introducing a dual-constraint mechanism of Euclidean distance constraint set and orientation constraint set during iterative sampling, mapping candidate samples to the intersection of the two sets through a projection operator, and sampling numerical parameters θ and covariance matrix Σ in stages using the MH criterion; and performing convergence judgment. This invention improves the clinical effectiveness and accuracy of the sampled samples, achieves faster convergence, and can be widely applied to parameter estimation and protocol optimization scenarios in personalized drug delivery.

The invention, which belongs to the technical field of intelligent medication, discloses an intelligent medication scheme recommendation method based on big data, and the method specifically comprises the following steps: collecting daily activity characteristics and medical record data of a patient; according to the diagnosis information and the inspection result, screening drugs which are consistent in indications and have no taboo components to obtain a primarily screened drug set; determining life scene categories according to occupational types and work and rest laws of the patients, and calculating life scene interference values of the medicines based on a preset interference coefficient library; screening a reference group similar to the target patient, and calculating an expected curative effect score of each drug by analyzing a re-diagnosis record of the reference group; and normalizing the interference value and the curative effect score, weighting according to a preset weight to obtain a comprehensive adaptation degree, and selecting the drug with the highest adaptation degree to output. According to the invention, the daily life scene of the patient is fused into the medication decision, the fusion of clinical effectiveness and life feasibility is realized, and the medication compliance is improved.

A System and method enhance clinical effectiveness for monitoring for a change in a level of fluid in tissue by using a device attached to a body portion that wirelessly reports to a remote apparatus or receiver power level for indications of extravasation or infiltration. Adjusting an activation rate of fluid detection, reporting or both extends service life of the device.

A system may model, in a computer system, a first model representing a plurality of decision-options. A system may model, in the computer system, a second model representing an effectiveness of the plurality of decision-options. A system evaluates sensitivities, uncertainties, and alternative scenarios related to the plurality of decision-options of the first model and the effectiveness of the plurality of decision-options of the second model. A system provides an output indicating recommendations and tradeoffs among the set of design options selected from the plurality of decision-options. A system and method for decision-making support in medical technology strategy integrates Multi-Attribute Tradespace Exploration (MATE) and Clinical Cost-Effectiveness Analysis (CEA) models. The system models a plurality of decision-options, and evaluates their effectiveness using clinical effectiveness metrics such as quality-adjusted life years (QALYs). The system optimizes these decision¬ options by performing probabilistic analyses, including Monte Carlo simulations, to account for uncertainties.