8 results about "Mortality rate" patented technology

This invention provides a method for predicting individual mortality rates based on activity data from wearable devices, comprising the following steps: acquiring multi-source characteristic data of the target individual, wherein the multi-source characteristic data includes at least activity data and baseline characteristic data, the activity data being collected by the wearable device worn by the target individual over a continuous monitoring period, and the baseline characteristic data including at least one of demographic characteristics, health characteristics, living environment characteristics, and lifestyle characteristics; processing the activity data to calculate an average activity data feature value representing the overall activity level of the target individual; inputting the average activity data feature value and the baseline characteristic data together into a trained nonlinear machine learning prediction model, and the prediction model outputting a predicted mortality risk value for the target individual within a preset future time window. This invention integrates the average activity data feature value with traditional baseline characteristic data, enabling risk assessment to be updated synchronously with an individual's daily activity level.

Methods and applications of the present disclosure include promoting blood clotting, controlling hemorrhage, and or inhibiting fibrinolysis in soldiers and civilians having experienced massive bleeding from a wound by administering antifibrinolytic agents, such as tranexamic acid, and or one or more procoagulants, in preferably autoinjector format.

This invention provides an interpretability system for mortality prediction models, comprising a prediction model module, a global interpretation module, an individual interpretation module, and a visualization interface module. By introducing a dual interpretation mechanism of the global and individual interpretation modules, the SHAP analysis unit of the global interpretation module generates a global feature importance ranking and an individual feature contribution distribution visualization, demonstrating the importance ranking of multi-source features in predicting mortality risk, enabling users to establish a macro-level understanding of the key aspects of the mortality prediction model. The individual interpretation module generates personalized interpretation reports, indicating the quantitative contributions of key features and crucial characteristics that drive increases or decreases in mortality prediction values. Through this combination of macro-level understanding and micro-level attribution, the mortality prediction model's predictions are transformed from an incomprehensible "black box" into a verifiable chain of evidence, effectively solving the core pain point of traditional models being difficult to apply due to their lack of interpretability.

The present application relates to the technical field of disease risk prediction, and particularly relates to a ten-year risk prediction method, system and device for sudden cardiac death, the method comprising: obtaining 48-hour electrocardiogram data of an individual sample and a risk classification evaluation result based on a China-PAR risk assessment model; splitting and combining the electrocardiogram data to obtain a preset multi-dimensional waveform graph; constructing a convolutional neural network model, taking the waveform graph as input and the corresponding risk classification evaluation result as output to train the model and obtain a sudden cardiac death risk prediction model; inputting the processed waveform graph of 48-hour electrocardiogram data of a subject into the prediction model, and outputting the result as a ten-year sudden cardiac death risk prediction level result. The present application can create a non-linear, high-precision, and iterative sudden cardiac death risk deep learning model for the general population and doctors, improve the detection rate of sudden cardiac death, and reduce the recurrence rate and mortality.

The application discloses a kind of based on deep learning's seedling mortality prediction method in flower cultivation process, comprising: obtaining the characteristic data of seedling in whole growth cycle and its corresponding mortality, generates the death characteristic data set of seedling;According to death characteristic data set, establish first mortality prediction model, second mortality prediction model;Obtain the environmental data and growth data of seedling in current growth cycle, generate the current characteristic data set of seedling;According to current characteristic data set, first mortality prediction model predicts output first predicted mortality, and second mortality prediction model predicts output second predicted mortality;First weight, second weight are respectively given to first predicted mortality, second predicted mortality, and the integrated mortality of seedling in next growth cycle is obtained by accumulating both.The application can predict the mortality of seedling in advance, and the grower can take rescue measures according to the integrated mortality of seedling, to improve the survival rate of flower seedling.

This invention discloses a lethal gene related to soybean thrips, MuαCOPI, and its application in the control of soybean thrips. This invention provides a lethal gene related to soybean thrips, MuαCOPI. Feeding soybean thrips with the dsRNA targeting MuαCOPI significantly increases the mortality rate. Further analysis using a combination of dsMuαCOPI and Serratia marcescens to feed soybean thrips resulted in a corrected mortality rate of approximately 88% for second-instar nymphs and approximately 52% for adults within 7 days. Synergistic virulence index analysis showed that the combined treatment of second-instar nymphs and adults with dsMuαCOPI and HvSm-1 produced a synergistic effect in virulence. This invention provides a safe and efficient method for the control of soybean thrips, with promising application prospects.

The application provides a preterm infant late-onset sepsis early warning system based on a flora maturity slope and a construction method thereof. The application can capture the signal that the flora maturity process is blocked before the clinical symptoms of late-onset sepsis appear by dynamically monitoring the change trend of the intestinal flora maturity slope K value of the preterm infant, the early warning time is earlier than that of traditional blood culture, sufficient time is provided for early clinical intervention, and the incidence rate and mortality rate of late-onset sepsis are effectively reduced.

This invention relates to a management platform and its implementation path for elderly atrial fibrillation (AF) patients in rural areas, primarily aimed at addressing the heavy management burden and uneven distribution of medical services for AF patients. AF has high disability and mortality rates, increasing the risk of stroke, heart failure, and sudden death, especially in the rapidly aging rural areas of Chengdu, where the management burden is particularly prominent. This invention achieves this through the following technical solutions: First, it conducts routine opportunistic screening for AF based on government-led rural public health check-up programs, incorporating diagnosed patients into digital grid management units; second, it creates digital data on high-risk AF patients, achieving grid-based and information-based management; the expected results include realizing a digital grid-based AF management cloud platform for "decision sharing" between doctors and patients, standardizing the comprehensive management of AF patients in rural areas, forming a theoretical framework for a "rural elderly AF management model" with Chengdu characteristics, and providing a scientific basis for policy formulation.