16 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 application belongs to the technical field of microorganisms, and particularly relates to a kind of actinomycete, compound and its preparation method and application. The actinomycete is preserved in China General Microbiological Culture Collection Center, and the preservation number is CGMCC No.20306. The actinomycete can be separated and purified to obtain kailing ketone mycin. The compound has obvious stomach poison activity on greenhouse whitefly adults. When the compound concentration is 100 μg / mL and treated for 72 h, the corrected mortality rate reaches 90.30%, and the compound also has touch kill activity on greenhouse whitefly. When the concentration is 100 μg / mL and treated for 72 h, the corrected mortality rate reaches 82.55%.

This invention relates to the field of medical immunology, and particularly to the application of CD39+ regulatory T cells as a biomarker in products for prognostic assessment of childhood sepsis. This invention is based on flow cytometry detection of CD39+ T cells in peripheral blood of patients. + Treg levels, and dynamically assess patient prognosis based on changes in their expression. CD39 + Treg expression levels were significantly negatively correlated with childhood sepsis scores (Phoenix and pSOFA scores) and negatively correlated with mortality rates in children with sepsis. When CD39... + When Treg expression is below 12.45%, it indicates a higher risk of death in the patient. The method of this invention can provide accurate prognostic assessment in the early screening of childhood sepsis, with high specificity and sensitivity. This method can also be used for dynamic monitoring of patients, helping doctors to adjust treatment plans in a timely manner and optimize personalized treatment, demonstrating significant practical application value.

The present disclosure relates to methods and systems for predicting intensive care unit (ICU) mortality. More specifically, the methods and systems for predicting a likelihood of ICU mortality described herein enable robust modeling of ICU mortality that addresses biases in automated data collection, including variations in documentation practices across different units, different hospital systems, and across time. In certain embodiments, the methods described herein include: providing an ICU mortality prediction system; obtaining a plurality of records for a patient in an ICU covering at least a first time period; extracting a plurality of different defined ICU prediction features for the patient; analyzing the extracted plurality of different defined ICU prediction features using a trained ICU mortality prediction model; generating a likelihood ICU mortality for the patient based on the analysis; and presenting the generated likelihood of ICU mortality for the patient via a user interface.

The invention provides a chronic disease health-care system for institutional old-age care, and relates to the field of intelligent medical treatment. The system comprises a periodic physical examination module for performing periodic physical examination on a crowd to obtain a physical examination result; the grading and bidding module is used for dividing the crowd into low-risk, medium-risk and high-risk groups according to the physical examination result; the low-risk group, the medium-risk group and the high-risk group are managed by units of different levels; the high-risk group management module at least comprises a chronic disease special case unit and is mainly used for creating a chronic disease file for a patient and performing chronic disease management service on the patient; the chronic disease type comprises any one or more of hypertension, diabetes, cerebral apoplexy, coronary heart disease, chronic obstructive pulmonary disease and fundus disease. According to the invention, a patient-centered'Internet + chronic disease management 'mode is adopted, chronic diseases are classified, graded and bid-divided, a county hospital-community / town health center-health station integrated system network is formed, early discovery of chronic diseases is truly realized, the morbidity of high-risk groups is reduced, and the disability and fatality rates are reduced.

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 the application of a miR-5588-3p inhibitor, belonging to the field of biomedical technology. Studies have found that miR-5588-3p expression is abnormally elevated in the plasma of epilepsy patients. Administration of the miR-5588-3p inhibitor 72 hours before constructing a tree shrew epilepsy model can effectively reduce the seizure severity, seizure rate, and mortality rate in epileptic tree shrews, and alleviate brain pathological damage. The miR-5588-3p inhibitor described in this invention is used to prepare drugs for treating epilepsy, and its nucleotide sequence is as follows: GCUGGCAUUAGUGGGACUU. This invention can effectively reduce the seizure rate and mortality rate in epileptic tree shrews, effectively prolong the clonic latency and seizure latency, shorten the seizure duration, and effectively reduce the seizure severity. The miR-5588-3p inhibitor can significantly reduce hippocampal neuronal damage.

The invention discloses an acute respiratory distress syndrome (ARDS) prediction method and system based on multi-modal data and deep learning, relates to the technical field of computer-aided diagnosis, and provides a computer algorithm for intelligently predicting the severity of an ARDS patient based on computed tomography (CT) imaging and clinical indexes. Therefore, doctors can be assisted in early intervention and treatment. According to the technical key points, inflammation can be effectively segmented by using a 2.5 D U-Net model and a sub-visual recognition algorithm; according to the method, the ARDS is predicted by introducing an Extreme Graduent Boosting (XGBoost) algorithm after the segmented inflammation features are extracted, so that the ARDS prediction based on the imaging features is realized; the system can shorten the diagnosis time and reduce the death rate, and is of great significance to early diagnosis of ARDS. Experimental results show that the method provided by the invention has high accuracy in predicting the severity of the ARDS, the Dice coefficient of lung inflammation segmentation is 90.2%, and the accuracy of predicting the severity of the ARDS is 86.5%.

The invention relates to the technical field of feed additives, in particular to a traditional Chinese medicine composition for preventing and treating streptococcus iniae diseases of humpback perches as well as a preparation method and application of the traditional Chinese medicine composition. The traditional Chinese medicine composition comprises the following components in parts by weight: 10-15 parts of eucommia ulmoides, 10-15 parts of folium artemisiae argyi, 10 parts of radix angelicae, 10-13 parts of rhizoma zingiberis, 10-12 parts of liquorice, 7-9 parts of caulis spatholobi, 7-8 parts of herba ecliptae, 7-8 parts of radix ophiopogonis, 6-7 parts of poria cocos, 7-8 parts of hawthorn, 3-5 parts of radix aucklandiae, 3-4 parts of dendrobe, 3-5 parts of radix pulsatillae and 3-5 parts of radix puerariae. By feeding the traditional Chinese medicine composition provided by the invention, the weight gain rate of the humpback perches is increased by 20%, and the death rate of the humpback perches infected with streptococcus iniae is reduced by 10%.

The invention discloses a necrotizing enterocolitis prediction method based on machine learning. The method comprises the following steps: collecting multi-source clinical data of neonatal cases; carrying out systematic data preprocessing on all the collected data; carrying out feature engineering and variable screening by adopting a plurality of feature selection algorithms, and randomly dividing finally obtained feature data into a training set and a test set according to a proportion; and after model training is completed, performance verification is carried out on each candidate model by adopting an independent test set, and comprehensive evaluation is carried out in combination with multiple indexes derived from the classification confusion matrix. According to the method, the problem of limited feature dimension of a traditional model can be broken through, and the information utilization rate is improved through multi-modal data fusion; the method can solve the problem that a traditional linear model is difficult to capture a nonlinear relation between variables, achieves the interaction recognition between complex features, provides an intelligent auxiliary decision-making tool for intensive care of newborns, reduces the disease progress risk, and reduces the case fatality rate and the complication occurrence rate.

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.