74 results about "Predictive factor" patented technology

The invention discloses a power control method, system, equipment and medium for an optical storage and charging station, and belongs to the field of optical energy storage. A health degree factor is determined based on collected equipment operation data; determining a predictive factor based on the collected photovoltaic data; based on the power allocation weight and operation factors, a weight function is established to calculate the instruction weight of each device, and the operation factors comprise a planned maintenance factor, a sudden failure factor, an energy storage correlation factor, a health degree factor and a prediction factor of the parallel devices; calculating to obtain a power instruction of each parallel device; and controlling the parallel equipment to operate according to the power instruction through the station controller. According to the method, the bearable degree of the equipment is measured by calculating the dynamic weight comprehensively obtained on the basis of the health factor, the prediction factor, the maintenance factor, the sudden failure factor and the energy storage correlation factor, and the power instructions with inconsistent stagger degrees are distributed according to the actual operation condition, so that the operation stability is improved.

The invention relates to the technical field of biomedical engineering, and discloses a method and system for early warning and analyzing high-risk groups with high altitude polycythemia, and the method comprises the steps: screening a subject data set from an extremely high altitude area; performing structured processing on the subject data set to obtain a structured feature matrix, and extracting a core prediction factor from the structured feature matrix; candidate early warning models of the core predictive factors are generated, and the candidate early warning models comprise a logistic regression model, an XGBoost model and a random forest model; screening an optimal early-warning model from the candidate early-warning models, and establishing a high altitude polycythemia early-warning system of the subject data set through the optimal early-warning model; and effect verification is carried out on the high altitude polycythemia early warning system so as to realize high altitude polycythemia high-risk group early warning analysis processing of the subject data set. According to the method, the core problem that the early warning result is one-sided and unreliable due to three defects of data dimension missing, static evaluation limitation and extensive privacy mechanism can be solved.

The invention discloses a preoperative risk stratification system based on hepatic resection after transformation treatment in the technical field of biomedicine, and the system comprises a sample module which is used for selecting a proper hepatocellular carcinoma patient as a sample, distributing the patient sample to a training queue and an internal verification queue, and building an external verification queue; the treatment module is used for providing a treatment strategy for the patient based on whether liver resection is performed or not after conversion treatment; the tracking module is used for carrying out regular follow-up visit on the patient subjected to the liver resection or not subjected to the liver resection after the conversion treatment; and the data analysis module is used for determining prediction factors related to recurrence-free survival in the training queue by adopting a minimum absolute contraction and selection operator regression analysis method, further analyzing and screening out independent prognosis factors through multivariable Cox regression, and constructing a column graph based on the factors to predict the postoperative recurrence rate. Through verification of the internal and external verification queues, the method can effectively guide and select a patient who is most likely to benefit from the liver resection operation after transformation treatment.

The invention discloses a peripheral blood and CT-based liver transplantation recipient sarcopenia classification method and device, and the method comprises the steps: carrying out the single-factor COX regression preliminary screening of collected peripheral blood biochemical indexes of liver transplantation recipients, and obtaining potential prediction factors related to sarcopenia; performing LASSO regression screening on the potential predictive factors to obtain core predictive factors related to sarcopenia; multiplying each core predictive factor by a regression coefficient calculated in LASSO regression, performing accumulation calculation to obtain an LASSO + COX model score, and dividing liver transplantation recipients into different risk groups according to the size of the LASSO + COX model score; and carrying out definite diagnosis on sarcopenia according to a risk group division result in combination with a CT image examination result. According to the method, a new strategy of peripheral blood prediction preliminary screening and high-risk CT confirmation is realized, the radiation risk, cost and detection threshold are reduced, and the sarcopenia management efficiency is improved.

The invention relates to a method and system for predicting post-induced hypotension of a patient, and belongs to the technical field of hypotension prediction.The method comprises the steps that original data reflecting the cardiovascular autonomic nerve regulation capacity of the patient is obtained; preprocessing the original data to obtain target data, and calculating a target pressure reflex sensitivity BRS and a target pressure reflex effective index BEI based on the target data and the same-direction change sequence of the continuous heartbeat; inputting the target pressure reflex sensitivity BRS, the target pressure reflex effective index BEI and the patient baseline data into a Logistic regression model to obtain a hypotension risk probability; and determining a hypotension risk level of the patient based on the hypotension risk probability and a preset threshold range. According to the method, the target pressure reflex sensitivity BRS and the target pressure reflex effective index BEI serve as predictive factors, so that the probability of predicting hypotension of a patient can be improved before the patient receives anesthesia induction.

The invention discloses a prediction method for kidney stone prognosis based on CT imaging omics, and relates to the field of medicine. Comprising the following steps that CT images and clinical data are collected, retrieval is conducted through a hospital medical record system, clinical information is collected, and the CT images are scanned through a scanner; cT image processing and feature extraction are carried out, calculus areas are sketched layer by layer on all cross sections of the CT image, information is recorded, and then feature extraction is carried out; and image omics feature screening and model construction: extracting image omics features from each original CT image. According to the method, CT image features and clinical prediction factors are combined, a clinical-image omics model is developed and verified, a repeatable, non-invasive, non-invasive and safe preoperative prediction method is provided, the calculus removal success rate of a kidney stone patient after flexible ureteroscope lithotripsy can be accurately evaluated, and the calculus removal success rate of the kidney stone patient after flexible ureteroscope lithotripsy is improved. And a clinician can be assisted to make a clinical decision and implement precise medical treatment.

The invention relates to a CVD risk assessment tool based on wearable device data and a machine learning algorithm, and the tool comprises a data collection module which obtains cardiovascular traditional risk factors through a cardiovascular risk assessment scale, and obtains monitoring data through a wearable device; the feature screening module is used for determining a candidate predictive factor range, and then performing feature extraction and predictive factor screening on the crowd wearing the wearable equipment by jointly using a minimum absolute contraction and selection operator (LASSO), a random forest (RF) and a Logistic regression model; the model building module is used for building a model based on an XGBoost machine learning algorithm; and the risk diagnosis module is used for outputting and evaluating the probability that the CVD risk of the individual is high within 10 years based on the characteristic data obtained by the wearable equipment data in real time. The cardiovascular health condition of an individual can be dynamically evaluated, personalized and timely risk early warning is provided, the method is suitable for daily health management, remote monitoring and other scenes, and a new solution is provided for early intervention and personalized medical treatment of cardiovascular diseases.

The invention relates to a method and a system for discriminating years of raw Pu'er tea in different storage aging periods. The method comprises the following steps: S1, preparing a sample; s2, analyzing components to determine the content of non-volatile compounds; s31, dividing the sample into three storage aging periods; s32, screening the number and the type of the non-volatile compounds by adopting LASSO regression to obtain a key predictive factor; s33, taking the key predictive factor as an input variable, and combining linear discriminant analysis to construct a year discriminant model; s4, outputting a result; the method has the advantages that based on LASSO regression of non-volatile compounds, linear discriminant analysis and other frontier machine learning technologies, an accurate model for Pu'er raw tea year discrimination is constructed, a matched visual module is combined, an efficient and visual tool is provided for Pu'er raw tea year discrimination, and the Pu'er raw tea year discrimination method is suitable for popularization and application. And scientific technical support is provided for quality evaluation and market transaction of annual Pu'er raw tea.

The invention discloses a hereditary ophthalmic disease intervention therapy prediction method. The method comprises the following steps: obtaining regulatory omics data of a hereditary ophthalmic disease, defining a core gene based on genetic evidence, defining a peripheral gene in combination with network evidence, performing calculation to obtain a gene-predictive factor matrix, and performing priority ranking on the genes; on the basis of the gene priority ranking list, a KEGG path is adopted to merge the network, and an intervention target network is identified and obtained; performing advanced analysis on the intervention target network, including lesion inhibition gene analysis, disturbance removal analysis and / or cross-disease priority atlas analysis, and identifying an intervention target with disease specificity; and predicting an intervention therapy for the hereditary ophthalmic disease according to the disease-specific intervention target.

The invention provides a solid tumor treatment target prediction method and system based on multi-modal omics data. The solid tumor treatment target prediction method comprises the following steps: inputting multi-modal omics data of a solid tumor, calculating an affinity score, and constructing a gene-predictive factor matrix; performing priority ranking on all the input genes, and performing function enrichment analysis on the preferentially ranked genes by using a KEGG pathway set; constructing a pathway intersection network related to solid tumor progression, and identifying a sub-network enriched with high-score nodes in the pathway intersection network by adopting a restart random walk algorithm; and based on drug research and development database information, drug reutilization analysis and disturbance removal analysis are carried out on the identified sub-network enriched with the high-score nodes. The invention discloses a method for realizing multi-modal omics data integration, analysis and identification of solid tumor treatment targets and drug reutilization combination, and aims to determine treatment candidate drugs including treatment targets and reutilization drugs.

The invention discloses an AKI, CKD and AKI-CKD progress general prediction model based on plasma proteomics and a construction method thereof. The construction method comprises the following steps: selecting a patient which has plasma proteomics data when a baseline which meets the inclusion standard of a research group, has eGFR greater than or equal to 60mL / min / 1.73 m < 2 > when the baseline exists, and has no AKI or CKD medical history; collecting clinical prediction factors and blood proteomics data of the modeling crowd; and randomly dividing the selected modeling crowd data into a training set and a verification set, screening out protein prediction markers such as protein WFDC2 and protein GDF15 shared with the development of the AKI, the CKD and the AKI-CKD, and establishing a general prediction model for the development of the AKI, the CKD and the AKI-CKD. Proteomics is used for predicting AKI-CKD progress for the first time, three renal function outcomes can be predicted at the same time, prediction indexes are simple and easy to obtain, and the model is a universal model with high prediction capacity.

The invention provides a rubber graphene composite sealing element performance prediction method, which relates to the technical field of performance prediction, and realizes complete mapping from a bottom layer structure to a surface layer contact behavior by establishing a fine-grained structural unit, extracting node-level stress response data and coding the data into a contact response coding set; on the basis, joint matrix construction and regional response difference analysis are adopted, local response abnormal regions are effectively recognized, and the problem that interface microcosmic changes cannot be accurately captured through a traditional method is solved. Furthermore, a predictive factor sequence is formed through weighted combination of the response volatility and the contact strength, and a historical track is backtracked in a linkage manner, so that accurate early warning of a high-risk unit is realized.

The invention provides a construction method and a construction system of a chronic lymphocytic leukemia prediction model based on machine learning, electronic equipment and a storage medium, and relates to the field of chronic lymphocytic leukemia prognosis research. The construction method comprises the steps of obtaining original sample data, and performing preliminary screening; performing data cleaning on the screened sample data; determining a prediction factor from the plurality of features of the cleaned sample data; dividing the cleaned sample data corresponding to the prediction factor and the target variable into a training set and a test set; inputting the training set after unbalance processing into a LightGBM model for training to obtain a prediction model; inputting the test set into a prediction model, performing hyper-parameter optimization on the prediction model, and evaluating the performance of the prediction model; calibrating the prediction model to obtain a calibrated prediction model; the method has the beneficial effects that the method can be realized only by depending on conventionally available predictive factors, and the patient screening can be realized before the CLL clinical symptoms appear.

The present invention relates to a system and a method for monitoring mental health and generating personalized reports. According to one embodiment of the present invention, the method for monitoring mental health and generating personalized reports comprises: receiving a user's response data to a self-report questionnaire, wherein the self-report questionnaire comprises questionnaires corresponding to the respective mental health scales; and generating a report on the user's mental state based on the response data to the self-report questionnaire. The mental state report includes a personalized mental health graph that visualizes daily scores for each mental health scale in the form of a bar chart.The bar charts included in the personalized mental health graphic can be displayed in colors that are predetermined according to criteria for the respective mental health scale. According to the present invention, it is possible to capture psychological, biological, and social digital phenotypes of groups with non-suicidal self-harm via the self-report questionnaire and thereby investigate predictive factors related to these phenotypes.

The invention provides an extreme weather power load prediction method and system, and the method comprises the steps: obtaining historical data, and determining a basic load in extreme weather according to the historical data; constructing a total load decomposition model, stripping initial estimation of a basic load and a random load in the total load according to the total load decomposition model, and obtaining a meteorological load mid-value through multiple regression fitting; according to the meteorological load median, screening core meteorological factors through improved grey correlation analysis, and combining stepwise regression screening and extreme value adaptation correction to obtain an accurate meteorological load; constructing a comprehensive predictive factor set according to the precise meteorological load; and inputting each prediction factor of the comprehensive prediction factor set into the trained improved BP neural network model, and outputting a power load prediction value in extreme weather, thereby effectively improving meteorological load separation precision and extreme weather load prediction precision.

The invention relates to the technical field of data prediction, and discloses a colorectal progression stage adenoma data prediction method and system based on machine learning, a terminal and a storage medium, and the method comprises the steps: screening out a corresponding candidate prediction factor according to the colorectal progression stage adenoma data of each target sample; and dividing the candidate prediction factors into a training set and a verification set, constructing a target machine learning model by using the training set, inputting the verification set into a plurality of decision trees of the target machine learning model, outputting an original prediction probability, and converting the original prediction probability into a classification probability to obtain a prediction result. According to the method, an efficient, explainable and visual machine learning early warning model is utilized, a visual decision path is generated, conventional non-invasive indexes can be effectively integrated, and individualized prediction of colorectal progression stage adenoma data is achieved.

The invention discloses an ACS patient prognosis prediction method, which is characterized in that ACS patient prognosis is predicted through a trained and verified ACS patient prognosis prediction model, and prediction factors of the prediction model comprise patient age, heart rate, heart function grading, urea nitrogen and glycosylated hemoglobin. The predictive factors are screened by adopting multi-factor Cox regression analysis. The prediction model comprises a column chart model, and a column chart scale comprises a scale score range of 0-100, a patient age range of 25-95, a heart rate range of 40-140, a heart function grading range of I-IV, a urea nitrogen range of 0-35, a glycosylated hemoglobin range of 4-15, a total score range of 0-240 and a survival rate range of 0.99-0.01.

The invention relates to the technical field of silver powder production control, and discloses a production control method and system for silver powder and a storage medium. The method comprises the following steps: mounting a sensor array on a chemical reduction reaction kettle to collect data to form a process parameter set; filtering the parameter set to obtain a quality prediction factor; performing sectional temperature regulation and control based on the predictive factor, and dynamically controlling the reaction to obtain silver powder suspension; and inputting the suspension into a self-adaptive centrifugal system, and adjusting process parameters according to the sedimentation efficiency. The problem of multi-parameter collaborative optimization in a traditional silver powder production control method is solved, the problems that in the chemical reduction method preparation process, the concentration of silver ions fluctuates, the adding rate of a reducing agent is unstable, and the reaction temperature is difficult to accurately control are solved, and self-adaptive centrifugal separation and intelligent washing control in the silver powder post-treatment process are achieved.

This invention discloses a machine learning-based system for predicting mild cognitive impairment (MCI) and its progression to dementia. The machine learning-based MCI prediction system includes: selecting predictive factors from a population known to have mild cognitive impairment by screening candidate factors; using this population as a dataset and dividing it into a training set and a test set; inputting the training set into all machine learning models to train them, resulting in a pre-trained machine learning model; inputting the test set into the pre-trained machine learning model to test it, and using the machine learning model whose test index value exceeds a set threshold as the final trained MCI machine learning model; and inputting several predictive factors of the subject to be tested into the final trained MCI machine learning model to obtain a prediction result indicating whether the subject has mild cognitive impairment.

This invention relates to the field of intrinsic ability prediction technology and discloses a method for predicting the intrinsic ability characteristics of the elderly based on machine learning. First, intrinsic ability score data of the elderly in five dimensions, including cognition and psychology, are acquired, and heterogeneity classification is performed using latent profile analysis. Using the classification results as labels, random forest and LASSO regression are used in parallel to screen key predictive factors to form a feature subset. A classification prediction model is built based on XGBoost, and hyperparameters are optimized through cross-validation and grid search. The SHAP and LIME algorithms are integrated to achieve both global and local interpretability. Finally, the model is deployed on an online interactive platform, where inputting individual characteristics outputs the IC propensity classification, probability, and feature contribution explanation. This invention achieves accurate classification and interpretable prediction of the intrinsic abilities of the elderly, reduces the dependence of assessment on professionals and the environment, and is suitable for large-scale application in grassroots elderly care scenarios.

This invention discloses a machine learning model integrating oral microbiome and clinical features and its application in predicting sarcopenia at high altitudes. The method targets high-altitude populations residing at altitudes >3500 meters, collecting oral microbiome 16S rRNA sequencing data, clinical features, and lifestyle data. Core predictive factors are selected through adaptive preprocessing and elastic network regularized regression, and a classification model is constructed using logistic regression. To address the sample imbalance problem in high-altitude areas, this scheme introduces a synthetic balanced sampling strategy based on target distribution doubling; simultaneously, Platt Scaling is used for two-layer probability calibration, combined with a dynamic threshold optimization mechanism based on F1-Score to improve discrimination accuracy. This invention is the first to integrate oral microbiome shaped by the high-altitude environment (such as…)… Selenomonas , Veillonella (etc.) are incorporated into the predictive model. Experiments show that the model achieves an AUC of 0.868 on the independent test set and has advantages such as being completely non-invasive, low-cost, and highly interpretable. This invention provides an efficient early screening program for sarcopenia in resource-scarce high-altitude areas.

The invention provides a dilated cardiomyopathy child death risk prediction method, system and device, and relates to the technical field of medical prediction models.The method comprises the steps that clinical data of a target dilated cardiomyopathy child patient are obtained and input into a pre-constructed nomogram prediction model, and death risk prediction results of the target dilated cardiomyopathy child patient at multiple time points in the future are output; the nomogram prediction model is constructed through independent prediction factors, the amino-terminal brain natriuretic peptide precursor level, the gender and the digoxin use condition which are determined through single-factor and multi-factor Cox regression analysis. According to the method, the nomogram prediction model is constructed by integrating the key clinical variables, accurate prediction and individualized treatment guidance of the dilated cardiomyopathy death risk of children are achieved, and the accuracy of clinical decision and the prognosis management effect are effectively improved.

The invention discloses a marker for predicting an insulin fortified treatment effect and application of the marker, and relates to the technical field of biomarkers. In order to solve the problem that in the prior art, there is no research on systematic exploration of the effect of microRNA in SIIT curative effect prediction, it is found through systematic research that hsa-miR-502-3p is an independent predictive factor for SIIT long-term remission, a novel combined prediction model for SIIT long-term remission based on hsa-miR-502-3p and ISSI-2 is constructed, and the model is used for predicting the effect of microRNA on SIIT long-term remission. Compared with a single hsa-miR-502-3p prediction model and a single ISSI-2 prediction model, the prediction model has the advantages that the recognition capability of the model is further improved, and better balance between prediction efficiency and clinical interpretability is realized; in addition, the invention also clarifies the regulation and control relationship between hsa-miR-502-3p and SUR1, reveals a partial molecular biological mechanism of SIIT treatment heterogeneity, and provides a new view angle and basis for T2DM research and SIIT clinical practice.

The invention relates to a construction method of an acute mushroom poisoning death prediction model, which comprises the following steps: collecting detection data of a plurality of acute mushroom poisoning persons, and constructing a training set and a verification set; performing single-factor analysis based on the training set to obtain acute mushroom poisoning death risk related indexes; performing multi-factor Logistic regression analysis based on the acute mushroom poisoning death risk related indexes to obtain acute mushroom poisoning death independent risk factors; taking the independent risk factors of the acute mushroom poisoning death as predictive factors, and establishing a line diagram predictive model of the acute mushroom poisoning death; and carrying out distinction degree evaluation, calibration degree evaluation, decision curve evaluation and rationality analysis on the constructed column graph prediction model based on the verification set, and optimizing the column graph prediction model based on an analysis result to obtain a final column graph prediction model. The method can guide the standardized early recognition of the acute lethal mushroom poisoning process and treatment strategy, and improves the prognosis of the acute mushroom poisoning patient.

The invention relates to the technical field of medical artificial intelligence, and particularly discloses a method for establishing a disease prediction model of GCK-MODY in gestational diabetes population based on machine learning. The model takes five clinical characteristics as input variables: a body mass index before pregnancy, fasting blood glucose and glycosylated hemoglobin levels during GDM diagnosis, continuous multi-generation diabetes family history and a newly discovered predictive factor, namely GDM diagnosis week of pregnancy. A classification model is constructed through a support vector machine algorithm, and a SHapley additive interpretation method is adopted to provide global and local interpretation of a prediction result, so that model transparency is enhanced. The area of the model under a curve in a test is obviously superior to that of an existing screening standard. The invention also provides an electronic device, a storage medium and a prediction system comprising the model, which can assist clinicians in early recognition of GCK-MODY high-risk patients, provide decision support for targeted gene detection, avoid unnecessary enhanced hypoglycemic treatment of pregnant women, reduce the risk of maternal and infant complications, and promote precise medical development of gestational diabetes mellitus.

The invention discloses a pregnancy individualized medication dosage prediction factor screening method, a prediction model construction method and a prediction system, and relates to the technical field of medicine. Factors which are obviously related to blood concentration and have no obvious multicollinearity among variables are screened out from factors influencing pregnancy drug dosage to serve as drug dosage prediction factors, and a corresponding prediction model and system are constructed. According to the screening method, the prediction model construction method and the prediction system provided by the invention, auxiliary decision making can be provided for individualized drug dosage during pregnancy.

The invention belongs to the technical field of medical risk assessment, and provides a fibrosis metabolism related steatohepatitis risk assessment system. Comprising a data acquisition module configured to determine candidate prediction indexes; the primary screening module is configured to perform single-factor logistic regression analysis on the candidate prediction indexes to determine key prediction indexes of fibrosis metabolism related steatohepatitis; the secondary screening module is configured to screen out four independent predictive factors from key predictive indexes of fibrosis metabolism related steatohepatitis through stepwise multiple regression analysis; and the model training module is configured to train the constructed multi-factor logistic regression model based on the screened four independent predictive factors. According to the method, four indexes of aspartate transaminase, alanine transaminase, triglyceride and high-density lipoprotein cholesterol are integrated, a fibrosis MASH evaluation and prediction model for obese people is constructed, and the evaluation performance is better.

The invention provides a method, equipment, medium and program product for predicting the osteoporosis risk of postmenopausal diabetes people, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring one or more predictive factors of an age, a menopausal age, a diabetes disease course, PINP, CTX and an FF score of a subject; the subject is a postmenopausal diabetes patient; calculating a risk score based on the predictive factor, and outputting an auxiliary predictive result of the risk probability of osteoporosis of the subject according to the risk score; when the risk score is greater than a first threshold value, outputting an auxiliary prediction result that the probability of occurrence of osteoporosis risk of the subject is large; and when the risk score is smaller than a first threshold value, outputting an auxiliary prediction result with a small probability of occurrence of osteoporosis risk of the subject. The osteoporosis risk of postmenopausal diabetes people is predicted specifically aiming at the postmenopausal diabetes people.

This invention discloses a method and related equipment for predicting and intervening in the risk of stillbirth related to umbilical cord torsion. The method acquires multimodal clinical data, including the pregnant woman's complaints and prenatal ultrasound examination information, and standardizes the data to extract core predictive factors such as changes in fetal movement, signs of umbilical cord root torsion, and fetal growth restriction, as well as auxiliary predictive factors such as abnormal amniotic fluid volume and abnormal placental-umbilical cord insertion, constructing a feature vector that the model can process. Based on a pre-trained risk prediction model, the risk of stillbirth related to umbilical cord torsion is quantitatively assessed, a comprehensive risk score is output, and risk levels are classified. Based on the risk level, a pre-set clinical intervention strategy is automatically matched to form standardized treatment recommendations, which are then output to the clinical terminal. This method overcomes the limitations of traditional methods that rely on low sensitivity of single ultrasound diagnosis, achieving early risk identification and tiered management.

This invention discloses a method for predicting the glycemic index (GI) of oat products in vitro, belonging to the field of food testing technology. This invention systematically integrates multi-dimensional data on the nutritional components, structural characteristics, and in vitro digestion kinetics of oat products. Using machine learning algorithms such as Bayesian ridge regression, it screens out five key predictive factors: β-glucan content, damaged starch content, median particle size, aleurone layer thickness, and 180-minute hydrolysis index. Combined with in vivo GI measurements, a high-precision prediction model is constructed through nonlinear regression fitting. This invention transforms the complex GI formation mechanism into a clear and measurable quantitative relationship, providing an efficient and low-cost technical solution for the rapid development, process optimization, and quality control of low-GI oat products, and has clear industrial application value.