16 results about "Clinical effectiveness" patented technology

The invention belongs to the technical field of application of an artificial intelligence technology to medical rehabilitation instruments, and relates to a method for designing an orthosis based on an AI artificial intelligence technology, and the method comprises the steps: S1, data collection and preprocessing: converting patient data into structured data; s2, feature extraction: extracting geometric and biomechanical features related to orthosis design, and constructing a feature set; s3, processing the features by using a deep learning model, and predicting personalized parameters; s4, designing and optimizing an orthopedic module, and optimizing pressure distribution; s5, a three-dimensional orthopedic CAD system is developed and deployed, and patient information and orthopedic device design are managed; and S6, evaluation and optimization: fusing clinical data, and optimizing the system according to an evaluation result. According to the method, the AI technology is utilized to improve the design efficiency and the adaptation accuracy, the personalized requirements are met, the rehabilitation effect is optimized, digital management of data and iteration of an AI model are achieved, a treatment basis is provided for rehabilitation medical employees such as a brace teacher and a rehabilitation therapist, and the clinical effectiveness of a rehabilitation treatment scheme is improved.

The invention discloses an attention deficit hyperactivity disorder subtype recognition method based on brain network topology hub deviation, relates to the technical field of medical diagnosis, and has the technical key points that a norm model of a brain structure form similarity network is constructed based on multi-center big data; quantifying a brain network topology hub index as a target phenotype of the norm model; then, performing semi-supervised clustering analysis by utilizing the individual deviation phenotype of the topological hub index so as to divide different biological subtypes and reveal unique clinical and biological characteristics of the biological subtypes; finally, strict cross validation is carried out in an external independent queue, and it is ensured that the potential biological subtypes recognized by the typing model have good generalization and clinical effectiveness.

To confirm clinical validity of grounds for an inference made by a machine learning model.SOLUTION: A medical information processing device according to an embodiment includes an acquisition unit, an inference grounds determination unit, a concept reflection degree calculation unit, and a dependency determination unit. The acquisition unit acquires a machine learning model and learning data used to train the machine learning model. The inference grounds determination unit determines each of inference grounds of the learning data and generates a visualized result of the inference grounds using the machine learning model. The concept reflection degree calculation unit determines a concept seriously considered during an inference made by the machine learning model on the basis of the visualized result of the inference grounds and calculates each concept reflection degree of the learning data with respect to a concept. The dependency determination unit generates visualized information on the dependency between the concept and a user-interpretable feature of the learning data on the basis of the concept reflection degree and the user-interpretable feature.SELECTED DRAWING: Figure 1

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.

The invention discloses a nomogram pancreatic cystic disease diagnosis model construction method, which comprises the following steps of S1, acquiring enhanced MRI (Magnetic Resonance Imaging) images and clinical pathological data, and completing data set division; s2, acquiring a focus region of interest for analyzing region calibration; s3, preprocessing the focus region of interest, extracting radiomics features, completing repeatability evaluation and feature screening, and constructing a candidate feature set; s4, an improved quadratic discriminant analysis algorithm is adopted to construct a radiomics model, hyper-parameters are adjusted, indexes such as AUC are evaluated, and radiomics scores are output; s5, screening key clinical variables based on regression analysis, and generating a clinical input feature set; s6, combining radiomics scores and clinical features to construct a combined diagnosis model; and S7, outputting a Nomoh map, performing three-data-set performance evaluation, and verifying model discrimination capability, calibration consistency and clinical effectiveness. The intelligent, accurate and visual pancreatic cystic lesion diagnosis system realizes intelligent, accurate and visual pancreatic cystic lesion diagnosis.

A method for improving the health of a population is provided comprising the use of a trained artificial intelligence to: • receive requests for provision of clinical services in relation to a patient, the request comprising a specification of the clinical services required and an indication of presence or absence of relevant patient acuity factors in the patient concerned; • allocate or assist in allocation of patients to clinical service providers wherein allocation is based on optimising use and clinical effectiveness of clinical personnel and / or resources by the healthcare provider taking into account the specified clinical services and patient acuity factors; • receive feedback on performance of one or more of the specified clinical services the artificial intelligence being trained on performance data relating to clinical personnel, clinical service providers, clinical resources, referrers, patient acuity factors, and patient outcome, the performance data being modified continuously or periodically to reflect the feedback.

The invention provides a method and a system for constructing a sicca syndrome blood stasis syndrome prediction model. The construction method of the sicca syndrome blood stasis syndrome clinical prediction model comprises the steps that S1, prediction factors are screened, specifically, clinical data of a plurality of sicca syndrome patients are collected, the clinical data comprise clinical symptoms and physicochemical indexes, and multi-factor logistic regression analysis is conducted on the clinical data to obtain prediction factors; screening out an index of which P is less than 0.05 in the multi-factor logistic regression analysis as a predictive factor for constructing a sicca syndrome blood stasis syndrome clinical predictive model; s2, constructing a column diagram model: constructing a column diagram as a sicca syndrome blood stasis syndrome clinical prediction model by adopting R software; s3, verifying the performance of the model: verifying the distinction degree, the accuracy, the goodness of fit and the clinical effectiveness of the sicca syndrome blood stasis syndrome clinical prediction model. The method can be used for objectively, quickly and accurately predicting the blood stasis syndrome occurrence probability of the sicca syndrome patient.

The invention discloses a macrolide non-reactive mycoplasma pneumonia prediction model and a construction method, and belongs to the technical field of artificial intelligence. The invention aims to solve the problem of clinical macrolide non-reactive mycoplasma pneumonia (MUMPP) recognition lag. The method comprises the following steps: constructing a standardized data set; preprocessing the data, namely processing missing values by adopting a chain equation multi-interpolation method, and converting continuous variables into classification variables based on clinical significance or an ROC curve; training by adopting a random forest and a neural network model, screening an optimal feature subset in combination with a recursive feature elimination method, and optimizing model hyper-parameters through a grid search algorithm; and finally, evaluating the model from multiple dimensions of distinction degree, calibration degree and clinical effectiveness. Through a systematic modeling process, the constructed prediction model is high in accuracy, early warning of MUMPP can be realized, and time is won for clinically adjusting treatment strategies in time and preventing disease deterioration.

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.