14 results about "High risk patients" patented technology

The application belongs to the technical field of biological detection, and particularly relates to the use of apolipoprotein A-I in the preparation of a diagnostic reagent or system for predicting the risk of severe acute hepatitis E. The application first discloses and verifies the significant correlation between the serum apolipoprotein A-I (apoA-I) level and the adverse clinical outcomes (including severe jaundice, liver failure and death) of patients with acute hepatitis E, and establishes the risk interpretation threshold (0.675 g / L and 0.435 g / L) with clinical practical value. The application can objectively and early identify high-risk patients, and provides a powerful auxiliary tool for clinical stratified management and resource optimization.

The present invention relates to methods of treating previously untreated diffuse large B-cell lymphoma (DLBCL) defined as high risk according to circulating tumor DNA (ctDNA) by administering galatifamab in combination with chemotherapy.

The invention belongs to the technical field of bioinformatics and medical detection, and particularly relates to a marker combination for neuroblastoma (NB) noninvasive risk level grading, a prediction model, a prediction method and application thereof. The marker combination is used for determining the sex, determining whether the month age is greater than 18 months, determining whether plasma MYCN is amplified, determining whether tumors are metastatic, and determining the content of neuron-specific enolase and lactic dehydrogenase; a machine learning algorithm is used for constructing an NB noninvasive risk degree grading prediction model, the comprehensive performance of the random forest model is optimal, the area value under a subject working characteristic curve reaches 0.956, the sensitivity is 92.9%, the specificity is 82.1%, the accuracy rate is 87.5%, the Kappa value is 0.75, the F1 score is 0.881, and NB middle and low risk patients and NB high risk patients can be effectively distinguished; the NB non-invasive risk level grading prediction model constructed by the invention can quickly, accurately and non-invasively perform NB risk level grading, and has a relatively good clinical application value.

This invention discloses a nursing resource allocation and management method and system based on emergency triage information. It introduces a dynamic split weight mechanism, first calculating the global correlation density of each triage feature to quantify its statistical value in the overall population, and simultaneously calculating local mutation significance factors to capture its weak but crucial discriminative information in critically ill samples. Then, the two are combined to generate dynamic split weights, which are used to optimize the split gain of features in the prediction model. This allows the gradient boosting decision tree algorithm to overcome the limitations of traditional global gain bias towards strong features during feature selection, effectively identifying those latent critically ill key features with low global importance but significant local mutations, significantly improving the prediction accuracy for latently critically ill patients. Based on accurate risk prediction results, nursing resources are dynamically allocated, realizing a shift from passive response to proactive intervention, and solving the technical problem of existing technologies failing to intervene in a timely manner due to missed identification of high-risk patients.

The application discloses a biomarker related to bladder cancer and application thereof, the biomarker is tRF-1:28-chrM.Ser-TGA and / or tiRNA-1:34-Glu-CTC-1-M2, which can be used for preparing a bladder cancer early high-risk patient diagnosis and / or prognosis evaluation product, and belongs to the technical field of biological medicine. The biomarker is screened through RNA sequencing, has higher sensitivity and specificity as a biomarker, and is verified through qRT-PCR and ROC curve analysis, and it is proved that tRF-1:28-chrM.Ser-TGA and tiRNA-1:34-Glu-CTC-1-M2 can excellently distinguish bladder cancer patients from normal people, and can be used as an early diagnostic biomarker and a new treatment target of bladder cancer patients, which has great clinical value and practical significance for early clinical diagnosis of bladder cancer, related treatment target evaluation and improvement of the survival rate of patients.

The present application belongs to the field of artificial pneumothorax making device, and particularly relates to an artificial pneumothorax puncture device for chest wall puncture and establishment of a pleural cavity channel. In view of the defects that the existing puncture needle is easy to puncture the visceral pleura, the position of the needle tip is ambiguous, iatrogenic pneumothorax, gas embolism and other complications are easily caused, and the puncture needle cannot be adapted to high-risk patients such as emphysema, the present application adopts a coaxial structure, designs a blunt and smooth pneumothorax needle, and / or increases the contact area of the front end of the outer sleeve tube with the visceral pleura, cooperates with the side wall vent hole to realize direct positioning of the pleural cavity negative pressure, integrates double operation paths, and is matched with frosted development, scale marking and other optimizations. The device can greatly reduce the risk of complications, expand the applicable population, shorten the positioning time, balance the operation flexibility and accuracy, and effectively improve the safety and efficiency of the artificial pneumothorax operation.

This application discloses a plasma biomarker for early assessment of myocardial fibroblast activation in patients with acute myocardial infarction (AMI) and its application. This application is the first to propose HBP as a biomarker for assessing the activation level of myocardial fibroblasts in AMI patients, aiming to utilize HBP levels for effective early assessment of myocardial fibroblast activation in AMI patients, ensuring timely clinical intervention and ultimately improving the prognosis of these high-risk patients. The biomarker (HBP) provided in this application enables non-invasive in vivo monitoring of the dynamic activity of myocardial fibrosis, overcoming the limitations of traditional imaging techniques such as late gadolinium enhancement in capturing the real-time activation status of fibroblasts during the critical repair period after myocardial infarction, and has promising clinical application prospects.

The application discloses a kind of inpatient hierarchical management method and system, belong to medical risk management technical field.The method includes: obtaining the personal information of inpatient and determining its risk index;According to risk index, dynamic correction electronic fence;Through real-time acquisition patient position by wearable device, combine fence relationship to calculate risk index;While calculating vital sign index according to vital sign information;Comprehensive risk index and vital sign index determine early warning grade, and execute corresponding early warning prompt.The application realizes the change from passive patrol to active early warning through multi-dimensional risk fusion and dynamic fence adjustment, improves the timeliness and accuracy of high-risk patient monitoring, while reducing false positive rate and nursing interference.

This invention discloses a model, system, and device for predicting the risk of chronic postoperative pain after surgery in adolescents with idiopathic scoliosis, belonging to the field of medical decision support technology. This application provides the first CPSP prediction model integrating psychological and physical factors, and a risk assessment system built based on this model. By acquiring relevant patient parameters, it can calculate an individualized probability of chronic postoperative pain risk and further output risk levels and personalized perioperative management recommendations. This invention is the first to integrate psychological factors (depressive symptoms) with physiological factors (gender, surgical extent, etc.) to construct a prediction model, which has been verified to have excellent discriminative power (AUC up to 0.852), enabling rapid and accurate preoperative identification of high-risk patients and providing a usable decision support tool for clinical implementation of integrated "psychological-pain" interventions.

The application provides a sepsis cardiomyopathy prognosis risk stratification system and a construction method thereof. The method first classifies SIC patients through multi-modal cardiac ultrasound, and then adopts a machine learning algorithm to screen out key ultrasound variables: left ventricular overall longitudinal strain (GLS) and tricuspid annular plane systolic excursion (TAPSE) which are strongly related to prognosis. Based on these variables, cluster analysis is performed, and SIC risk subphenotypes with significant prognosis difference are successfully constructed, and the mortality of the high-risk subtype is significantly higher. The study found that a single indicator, TAPSE, has extremely high predictive efficiency in identifying high-risk subphenotypes and can achieve rapid bedside assessment. The application first establishes a prognosis risk stratification system for SIC, simplifies the evaluation process, realizes early identification of high-risk patients, and provides key technical support for clinical precise intervention and improvement of prognosis.

This invention discloses a teaching method for intelligent simulation analysis of fall risk in elderly hypertensive patients based on machine learning, belonging to the field of fall risk simulation analysis technology. It includes: acquiring patients' frailty data to obtain a frailty dataset; classifying patients into at least three risk levels based on the frailty dataset, with each risk level corresponding to a different time folding coefficient, thus obtaining a frailty level classification item. This invention establishes a triangular assessment system of age, comorbidities, and cognition, and dynamically compensates for the underestimation of elderly or multi-disease patients by traditional models through a frailty adjustment coefficient, thereby improving the accuracy of high-risk patient identification. Furthermore, through intelligent time window compression technology, the monitoring period is automatically scaled based on the frailty level, shifting the golden window for patient intervention earlier, increasing the monitoring frequency for severely frail individuals, and improving the high-risk event capture rate.

This invention provides the application of sIL-2R and CA19-9 as biomarkers in the preparation of prognostic assessment products for pancreatic ductal adenocarcinoma. This invention uses sIL-2R and CA19-9 as detection biomarkers, which can be obtained through routine preoperative blood tests, eliminating the need for invasive or high-cost examinations such as tissue biopsies or gene testing, making it suitable for widespread application in medical institutions at all levels. Compared to using only CA19-9 as a single biomarker, this invention, combined with sIL-2R, can identify high-risk patients with normal CA19-9 but elevated sIL-2R, overcoming the limitation of CA19-9's inapplicability in Lewis antigen-negative individuals. Furthermore, it provides prognostic information from the perspective of the immune microenvironment that CA19-9 cannot reflect, significantly improving the comprehensiveness and accuracy of prognostic assessment.

Disclosed is a method for early warning of a prognosis risk in a patient with acute ST-segment elevation myocardial infarction. The biomarker comprises HBP. The present application can effectively evaluate whether patients with acute ST-segment elevation myocardial infarction will experience poor prognosis within 30 days after PCI by monitoring changes in HBP levels, thereby achieving early identification and timely clinical intervention for high-risk patients, improving the response speed and effect of treatment, reducing the incidence of adverse cardiovascular events, and ultimately improving prognosis of patients.

This invention relates to the field of medical data analysis technology, specifically to a method for constructing an early warning model for urosepsis. The method includes: collecting multimodal time-series clinical data of patients; performing time-series alignment and imputation of missing values ​​on the data; fusing features from different modalities and time points based on a dynamic weight allocation mechanism to generate a comprehensive feature representation of the patient; employing a hierarchical cascade classification strategy, first using a lightweight rapid screening model to preliminarily identify high-risk patients, then applying a refined assessment model to conduct a detailed risk assessment of high-risk patients; and finally generating early warning signals and clinical decision recommendations. This invention effectively integrates multimodal time-series data, focuses on key information through dynamic weights and attention mechanisms, and balances screening efficiency and assessment accuracy by combining a two-stage classification strategy. It solves the problems of low sensitivity, neglect of dynamic time-series changes, and difficulty in clinical deployment in existing technologies, enabling early and accurate risk warning.