13 results about "High risk factors" patented technology

The invention provides an old people dyskinesia risk monitoring method, system and device and a medium, basic information of old people is acquired, a compliance evaluation model is established, and training parameters are adjusted based on a medical care manual intervention interface; identifying a high-risk factor combination by using an association rule, analyzing and predicting a movement function change trend of a future preset time length by combining a synchronous time sequence, and optimizing a decision weight by using a reinforcement learning module according to a medical care manual adjustment record; a dynamic risk assessment model is constructed by using a Bayesian network, cross-mechanism data cooperative training is realized by using a federal learning framework, time sequence features are extracted through feature engineering, and motion function indexes and risks of old people in a period of time in the future are predicted. And comprehensive evaluation, intelligent early warning and personalized intervention on the dyskinesia of the old people are realized. Rehabilitation training execution conditions of old people are monitored in real time, and rehabilitation prescriptions are dynamically adjusted according to execution data and subjective feedback, so that effectiveness of intervention is ensured.

The invention discloses an intelligent decision-making method and system for intraoperative stress injury based on machine learning, and relates to the technical field of electric digital data processing. The intelligent decision-making method for the intraoperative stress injury based on machine learning comprises the following steps: S1, selecting and analyzing a sample; s2, performing multi-collinearity evaluation; s3, evaluating information loss; and S4, intelligent decision making. According to the method, sample selection adjustment is carried out through sample selection analysis data, then principal component dimension reduction is carried out based on multi-collinearity evaluation data, finally potential factors are introduced step by step based on information loss evaluation data, and whether risk weights of high-risk factors are obtained or not is judged based on multi-collinearity evaluation values after the potential factors are introduced. And intelligent decision-making of the intraoperative stress injury is performed in combination with the high-risk factors, so that the screening accuracy of the high-risk factors in the intelligent decision-making of the intraoperative stress injury is improved, and the problem of low screening accuracy of the high-risk factors in the intelligent decision-making of the intraoperative stress injury in the prior art is solved.

The invention discloses a multifunctional detection device for cerebral apoplexy screening, which comprises a main body, an opening and closing door, a lifting rod and an ultrasonic probe, the front end of the main body is rotatably provided with the opening and closing door, the right side of the main body is fixedly provided with the lifting rod, and an ultrasonic Doppler blood flow detector is placed in the middle of the interior of the main body. An electronic sphygmomanometer and a blood glucose tester are arranged at the left end and the right end in the main body respectively, and a weighing machine is arranged in the upper end of the main body. According to the multifunctional detection device for cerebral apoplexy screening, the blood flow speed in the body of a patient can be detected through the ultrasonic Doppler blood flow detector, then the blood glucose of the patient is detected through the blood glucose tester, the detection result can be transmitted to the interior of the ultrasonic Doppler blood flow detector, and self-evaluation of high-risk factors is completed; the detection content of specific markers in the body of a patient is detected through the medical consumables, and the use convenience of the device is improved through mutual cooperation of the devices.

The application discloses a fine segmentation method for prostate and internal lesion areas based on large pathological sections, and specific steps are as follows: step 1, sequentially performing fixation, paraffin embedding, continuous transverse sectioning and HE staining operations on the whole tissue; step 2, extracting an HE staining image; step 3, sequentially scanning the pathological sections in step 2 into digital pathology; step 4, processing the digital pathology; step 5, performing image registration and three-dimensional image reconstruction on analysis results of multiple pathological sections processed in step 4; step 6, training a magnetic resonance multi-modal sequence segmentation model; step 7, extracting features of normal prostate tissue and lesion tissue in three sequences; step 8, acquiring high-risk factor information and quantifying features; step 9, constructing a sample feature matrix; and step 10, predicting the malignancy degree of prostate cancer. The method can realize more accurate benign and malignant evaluation of prostate lesions and prediction of the malignancy degree of prostate cancer.

The invention relates to the technical field of public health, and particularly provides a microscopic simulation method for evaluating a chronic disease prevention and treatment strategy. According to the method, population census microscopic data and electronic health records are integrated, high-risk factors are distributed by adopting a conditional probability model, and an initial population database is constructed; establishing a behavior influence model containing screening participation and intervention compliance, and simulating disease progression in combination with Markov state transition; the implementation process comprises the steps of data preparation, database construction, high risk factor distribution, screening strategy simulation, intervention effect evaluation, cost calculation, annual updating and the like. The method has the technical effects that the goodness of fit between the simulated crowd prevalence rate and actual data reaches 92%, the screening participation rate prediction error is smaller than 5%, and the medical cost estimation precision is improved by 9% compared with that of a traditional method; the method can provide quantitative basis for cost effect analysis of chronic disease prevention and treatment strategies, and assists public health decision making.

The invention relates to a shallow water Christmas tree development mode risk identification and quantitative evaluation method and system, processing equipment and a storage medium, and the method is characterized in that the method comprises the steps: carrying out the risk qualitative analysis of a shallow water Christmas tree drilling stage, constructing a fault tree analysis model of each drilling stage in a shallow water Christmas tree development mode, and determining potential risk factors; based on the risk factors in the fault tree analysis model, performing quantitative evaluation by adopting a strategy test and evaluation laboratory method, and determining the weight of each risk factor in the fault tree analysis model; based on the weight of each risk factor in the fault tree analysis model, high risk factors in the shallow water Christmas tree drilling stage are determined, risk identification and quantitative evaluation of the shallow water Christmas tree development mode are completed, potential risks can be more comprehensively identified and evaluated, quantitative evaluation is carried out, and the method can be widely applied to the technical field of risk analysis.

The invention discloses a kit for screening cerebral arterial thrombosis risk and a screening method, and belongs to the technical field of biological detection. The kit for screening the cerebral arterial thrombosis risk is a kit for detecting gene mutation of a C677T site of an MTHFR gene and comprises a forward primer and a reverse primer, the sequence of the forward primer is shown as SEQ ID NO.1, and the sequence of the reverse primer is shown as SEQ ID NO.2. The kit for screening the cerebral arterial thrombosis risk is a kit for detecting gene mutation of the C677T site of the MTHFR gene. The invention creatively finds that the gene polymorphism of the C677T site of the MTHFR gene has certain relevance with the disease of the cerebral arterial thrombosis, the site mutation is one of high-risk factors of the cerebral arterial thrombosis, and the A1298C site mutation of the MTHFR gene is not obviously associated with the susceptibility of the cerebral arterial thrombosis. The risk of the cerebral arterial thrombosis is evaluated by screening a susceptibility gene locus, the operation is simple, the cost is reduced, the prevention of the cerebral arterial thrombosis is enhanced, and the incidence rate of the cerebral arterial thrombosis is further reduced.

The invention discloses an HCV risk intelligent assessment method and system, and the method comprises the steps: obtaining an electronic medical record text and a domain knowledge graph containing HCV high-risk factors, negative words and time restriction word entities in an HCV high-risk risk assessment step, dynamically generating a context bias matrix, fusing a pre-training language model, and precisely outputting a high-risk risk recognition result; in the emotion calculation step, bottom acoustic features of voice of a patient are extracted, emotion and physiological status features related to drug side effects are processed in parallel through a double-flow network, emotion features are corrected through an attention gate, and accurate judgment of depression, anxiety and other emotions is achieved. According to the application, the HCV high risk and patient out-of-hospital compliance can be evaluated, and the leakage rate and the emotion misjudgment rate are reduced.

The invention provides a stamping device for communication base station shielding case production, and relates to the technical field of stamping, the stamping device comprises a main body, and a transmission plate is fixedly mounted in the center of the top of the main body; a transmission groove is formed in the top of the transmission plate; guide rails are fixedly mounted on the left and right sides of the top end in the main body; a transmission part is fixedly mounted at the rear end of the interior of the guide rail; and a supporting rail is fixedly mounted on the rear side of the top of the guide rail. In the forward moving process of the stamping lower die, the unloading column ascends under the cooperation of a corresponding structure to eject out a workpiece stamped and formed in the stamping lower die, so that a worker can take out the workpiece conveniently, and the problems that the workpiece is often clamped in the lower die after being stamped, the worker needs to take out the workpiece specially, an operation area for taking out the workpiece is located between the upper die and the lower die, and the work efficiency is high are solved. The space is limited, the danger coefficient is high, and inconvenience and safety risks are brought to work of workers.

The present invention provides an industrial robot tool setting and processing method for chamfering large gears, belonging to the field of robot application. One gear tooth is processed at a time, and the chamfering of the entire gear is completed through multiple processing on a turntable. The method mainly solves the problems of low accuracy, low efficiency and high risk factor of manual tool setting. The present invention uses a laser displacement sensor at the end of the robot to scan various positions of the gear workpiece to determine the coordinates of the workpiece center and the gear tooth vertex coordinates, thereby achieving accurate and stable tool setting and facilitating subsequent chamfering processing.

This application discloses an intelligent HCV risk assessment method and system. In the high-risk HCV assessment step, the method acquires electronic medical record text and a domain knowledge graph containing entities with HCV high-risk factors, negation words, and time-limited words. It dynamically generates a context bias matrix and integrates it with a pre-trained language model to accurately output high-risk identification results. In the emotion computing step, it extracts the underlying acoustic features of the patient's speech, processes the emotion and drug side effect-related physiological state features in parallel via a two-stream network, and corrects the emotion features through attention gating to achieve accurate judgment of emotions such as depression and anxiety. This application can assess HCV high-risk risk and patient outpatient compliance, reducing the missed screening rate and the rate of emotion misjudgment.

The invention belongs to the technical field of fracture prediction, and discloses a postmenopausal woman vertebral osteoporotic fracture prediction model establishment method. Comprises: obtaining a training queue; performing univariate logistic regression analysis on the training queue, and screening out important high-risk factors related to an imminent fracture; performing minimum absolute shrinkage and selection operator regression on the training queue, and screening out key prediction factors from the important high-risk factors; and combining the key prediction factors into a logistic regression model, performing fitting based on the training queue, determining a coefficient of the logistic regression model, and obtaining a fracture prediction model. And the risk that a woman with postmenopausal osteoporosis is about to have OVCF can be predicted.

This invention discloses a machine learning-based method for predicting postoperative pain and extracting high-risk factors after cesarean section, belonging to the field of postoperative pain detection technology. It involves a unified modeling of static variables such as preoperative and intraoperative demographic characteristics, medical history, and surgical parameters, along with dynamic variables such as physiological monitoring indicators and analgesia intervention data obtained at different postoperative recovery stages. A two-branch prediction model with a static coding branch and a dynamic evolution branch is constructed. After model training, the SHAP attribution algorithm is introduced to interpret and analyze the prediction model, decomposing the model output into the contribution of each input variable to the prediction result, thereby quantifying the influence of each factor on pain risk. This invention utilizes a recurrent neural network to model the dynamic feature sequence that changes over time, enabling the model to learn the evolutionary pattern of postoperative pain as it changes with the recovery stage, improving the accuracy and stability of chronic pain risk prediction.