36 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.

The application provides a risk prediction model for 28-day mortality of an elderly sepsis patient after being transferred into an ICU and a construction method thereof, determines independent risk factors related to death within 28 days through single factor and multi-factor logistic regression analysis, makes a nomogram, and constructs the prediction model of the patent, and the comparison result of the AUC of the ROC curve of the standard APACHE II score and the frailty index (FI-lab) or other multi-index model in recent years indicates that the prediction model proposed in the patent has better performance.

The invention relates to the technical field of biomolecular markers, and provides a chicken butyric acid level and / or death rate related SNP marker and application thereof, the SNP marker comprises a 106476774th nucleotide site corresponding to a No.3 chromosome of a GRCg6a-galGal6 genome, and the basic group of the nucleotide site is A or T. The invention also provides an application of the SNP marker in chicken butyric acid level and / or death rate related SNP marker in chicken butyric acid level and / or death rate related SNP marker in chicken butyric acid level and / or death rate related SNP marker in chicken butyric acid level and / or death rate related SNP marker. By optimizing the dominant alleles of the SNP, broilers with the butyric acid level and / or high and low mortality can be rapidly identified, broilers with high mortality can be eliminated, the frequency of the dominant alleles is increased generation by generation through breeding, the butyric acid level and / or mortality of the broilers, especially the 28-42-day-old stage, of the broilers is reduced, the genetic improvement progress of the domestic broilers is accelerated, and the breeding efficiency of the domestic broilers is improved. Therefore, the economic benefit of chicken breeding is effectively improved, and the application value is high.

The present application relates to the field of insect control, and particularly relates to a SIRT1 gene of Rhopalosiphum padi and application thereof and a method for controlling Rhopalosiphum padi. The gene encodes a protein with an amino acid sequence as shown in SEQ ID NO: 2. The present application finds that the SIRT1 gene plays a role in the growth and development of Rhopalosiphum padi. Inhibition of the expression of SIRT1 can significantly increase the mortality rate of Rhopalosiphum padi nymphs, significantly reduce the proportion of winged aphids, and ultimately cause the population to decline and the toxin transmission ability to weaken. By using RNA interference technology, it is found that inhibition of the expression of the SIRT1 gene of Rhopalosiphum padi can significantly increase the mortality rate of the nymphs, and significantly reduce the proportion of winged adults after development. In addition, by spraying dsRNA of the SIRT1 gene interference sequence of Rhopalosiphum padi encapsulated by a nano material, the mortality rate of the Rhopalosiphum padi nymphs is significantly increased.

The application provides a nematocide of a light-loving flower extract and a preparation method and application thereof, and particularly relates to application of the light-loving flower extract in preparation of a nematocide. Through determination of a mortality rate and a hatching rate of southern root-knot nematodes, it is found that ethyl naphthalene in the light-loving flower has good insecticidal activity on nematodes and has obvious inhibition on hatching of eggs of the southern root-knot nematodes. The nematocidal activity of ethyl naphthalene increases with increase of a concentration of the medicine, and the inhibition rate on hatching of the nematode eggs increases with increase of a treatment time and the concentration of the medicine.

The application discloses application of psoralen in preparation of a medicine for treating sepsis. As a natural product, the psoralen has high safety and good tolerance, and can intervene in a pathological process of the sepsis through various action mechanisms such as inhibition of an inflammatory reaction, regulation of an immune reaction and promotion of tissue repair. When the psoralen is applied to treatment of the sepsis, the inflammatory reaction of the sepsis can be effectively controlled, tissue damage can be reduced and the mortality rate can be lowered, so that the survival rate and the life quality of the sepsis patients can be improved.

The invention discloses a method for constructing a chronic endometritis rat model based on Hungata (preservation number: DSM 13479), and belongs to the technical field of animal models. The bacterial strain is subjected to anaerobic culture to prepare a bacterial solution, 150 mu L vagina perfusion is performed twice every 3 days for 3 weeks, rat endometrial inflammation reaction is induced, and endometrial atrophy and endometrial inflammation cell infiltration are taken as pathological characteristics. According to the invention, the problems of high infection, high death rate and the like caused by traditional injection of lipopolysaccharide or pathogenic bacteria through the uterus horn are avoided, and an efficient experimental tool is provided for mechanism research and drug development of chronic endometritis.

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.

The application belongs to the technical field of biological medicine, and relates to a construction method of a thyroid-associated ophthalmopathy animal model, which comprises the following steps: after a pseudo-sterile animal model is constructed, fecal bacteria liquid of a mild patient with thyroid-associated ophthalmopathy is first administered to the pseudo-sterile animal model in a first stage, and Ad-TSHR is injected once in the second half of the first stage; then fecal bacteria liquid of a moderate patient with thyroid-associated ophthalmopathy is administered to the pseudo-sterile animal model in a second stage, and Ad-TSHR is injected once in the second half of the second stage; then fecal bacteria liquid of a severe patient with thyroid-associated ophthalmopathy is administered to the pseudo-sterile animal model in a third stage, and Ad-TSHR is injected once in the second half of the third stage; finally, Ad-TSHR is injected once in the second half of a fourth stage; and thus the thyroid-associated ophthalmopathy animal model is obtained. The model has a high incidence of 80% and a significantly reduced mortality rate under the condition of less immunization times.

The invention discloses a prediction method and system for prolonging human life years by health intervention service, and relates to the technical field of data processing, and the method comprises the steps: building a health prediction model, and according to all-cause mortality and disease morbidity data of different genders and age groups, calculating a health prediction model; predicting the death probability of the crowd of each age group, the number of years of survivors of each age group, the total number of years of survivors and the expected life; in combination with the prevalence condition of general population health risk factors and the relative risk degree of all-cause death, calculating the life number of individuals who carry out health intervention earnings on different risk factors, and calculating the overall health score of the current individual; evaluating the health condition of the current individual according to the total health score of the current individual, discovering key risk factors, and making an intervention scheme. The life expectancy of healthy or chronic disease patients can be measured and calculated, and the method is suitable for application scenes of health intervention service evaluation.

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 invention discloses a high-risk chest pain prediction analysis method and device based on machine learning and a readable medium, and relates to the field of data analysis, and the method comprises the steps: carrying out the data enhancement and denoising of a data set through employing an SMOTE-ENN; screening baseline features in the processed data set by using a sequence forward selection method based on feature importance of a tree model, and verifying the validity of the screened features by using a Boruta algorithm to obtain important features; acquiring values of important features of a to-be-predicted chest pain patient, and inputting the values into the trained chest pain risk layering prediction model to obtain a corresponding chest pain risk layering result; and determining the maximum value of troponin in the important features as the feature influencing the prediction result of the chest pain risk hierarchical prediction model through SHAP analysis, analyzing the relationship between the maximum value of troponin and the death rate in the hospital, and determining the numerical value of the maximum value of troponin corresponding to the maximum death rate in the hospital. The problem of low accuracy of chest pain risk layered prediction is solved.

The invention provides a 30-day death rate prediction device and method for cerebral hemorrhage patients, equipment, a medium and a program product, relates to the technical field of medical biology, and analyzes data in MIMIC and eICU-CRD databases. And performing feature selection by using Lasso regularization, mutual information and recursive feature elimination (RFE). And training to obtain a prediction model, and performing internal verification and external verification by applying comprehensive performance indexes. The feature importance is interpreted using an SHAP value. Determining key predictors includes acute physiological score III (APSIII), age, leukocyte (WBC), and albumin level. Compared with traditional scoring systems such as GCS and APSIII, the model shows higher prediction accuracy in the aspect of predicting the death rate in 30 days, and has more balanced sensitivity and specificity. The machine learning method provided by the embodiment of the invention shows a promising prognosis value in the aspect of predicting the 30-day death risk of ICH patients received by ICU. It needs to be noted that the models can only serve as supplementary judgments and cannot replace clinical judgments.

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 present application relates to the technical field of medical immunology, and particularly relates to application of plasma cell-like dendritic cells as a marker in a prognosis product for children sepsis. The present application detects the pDC level in the peripheral blood of a patient based on flow cytometry, and evaluates the prognosis of the patient based on the expression level. The pDC expression level is significantly positively correlated with the children sepsis score (Phoenix and pSOFA score), and is positively correlated with the mortality rate of children with sepsis. When the expression of pDC is higher than 0.96%, it indicates that the patient has a higher risk of death. The method of the present application can provide accurate prognosis evaluation in the early screening of children sepsis, has high specificity and sensitivity, helps doctors to timely adjust the treatment scheme, optimizes personalized treatment, and has good practical application value.

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 invention relates to a maintenance hemodialysis patient death risk prediction model based on a plasma amino acid spectrum and construction and application of the model, and belongs to the technical field of biological medicine. The construction method comprises the following steps: detecting a baseline plasma sample of a maintenance hemodialysis patient through a Bruker nuclear magnetic resonance spectrometer; a cox proportional risk model is adopted to perform correlation analysis on each amino acid, and after important clinical covariables are corrected, it is determined that eight amino acid parameters are significantly related to the total cause death rate; obtaining a p value lt through a cox proportional risk model; the method comprises the following steps of: taking a parameter of 0.05 as a candidate parameter, screening potential biomarkers by adopting a support vector machine algorithm in combination with conventional clinical parameters which are proved to be related to hemodialysis patient prognosis, and constructing and obtaining a prediction model by adopting a logistic regression model according to the screened parameters; through the model provided by the invention, the patient death risk prediction capability is remarkably improved.

The invention provides a cerebral hemorrhage death rate prediction device, method, equipment, medium and program product for young adult patients, and relates to the technical field of medical biology, in the invention, wide data in a medical information intensive care database (MIMIC) and an electronic ICU cooperative research database (eICU-CRD) are utilized for research, and the young adults diagnosed as ICH and living in ICU are focused. The LassoCV and the RFECV are used for feature selection, and the synthetic minority oversampling technology-edit neighbor (SMOTEENN) is used for solving the problem of data imbalance. A machine learning model (logistic regression, random forest, multi-layer perceptron, k-nearest neighbor and XGBoost) is developed, and hyper-parameters are optimized through Bayesian. The model performance is evaluated by using sensitivity, specificity, accuracy, F1 score and AUC indexes, model interpretation is performed based on SHAP values, and a machine learning model is developed to determine key clinical indexes affecting 14-day and 30-day death rate results, so that clinicians are helped to identify high-risk patients and formulate treatment strategies to support clinical decisions.

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.

A neonatal intensive care unit-specific big data-based neonatal acute kidney injury prediction artificial intelligence system enables rapid and clear diagnosis of neonatal acute kidney injury, which has high morbidity and mortality rates. The neonatal intensive care unit-specific big data-based neonatal acute kidney injury prediction artificial intelligence system guides doctors to make clinical decision by performing deep learning calculations on an EMR (electronic medical record) data-based disease prediction model and a biosignal-based disease prediction model that are output by acquiring EMR data and clinical observation data from a neonatal intensive care unit.

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.

A method for monitoring and / or modifying behavior to reducing maternal mortality risk, including the steps of receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression; determining, via the one or more computer processors, a total abnormality point value of trends for in the subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of the trends for the subject exceeds a pre-selected point value an abnormality alert is generated.

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 invention discloses a distributed mobile smart medical remote system based on an active health model, and relates to the technical field of cerebral apoplexy health management, the system comprises a smart medical remote platform, the smart medical remote platform is in communication connection with the following modules: a multi-source data acquisition module, a multi-source data processing module and a multi-source data processing module; the cerebral apoplexy multi-source data collection module is used for collecting cerebral apoplexy multi-source data including clinical data and health data and preprocessing the collected data. The deep Q network and the GPT large model are utilized to construct the virtual patient generator, the synthetic data conforming to the cerebral apoplexy characteristics of the target area are generated, the defect of real data is overcome, the high-fidelity medical record text can be generated through the reinforcement learning algorithm, the medical knowledge graph is called to ensure the physiological rationality, and the method is suitable for popularization and application. And the synthetic data is used for training the cerebral apoplexy risk prediction model, so that the performance of the cerebral apoplexy risk prediction model on real data is remarkably improved, high-risk patients can be found in time in the early stage of diseases, and the disability rate and the death rate of cerebral apoplexy are reduced.