4 results about "High risk factors" patented technology

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 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.

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.

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.