30 results about "Nomogram" patented technology

The invention relates to a cerebral hemorrhage hematoma enlargement prediction method, system and device and a medium. The method comprises the following steps: acquiring baseline NCCT and CTA images of a patient; hematoma segmentation and three-dimensional reconstruction are carried out, and a key quantitative feature surface regularity index (SRI) and a density variation coefficient (DCV) are calculated; and inputting the SRI, the DCV and the clinical variables into the prediction model, and outputting the hematoma expansion risk probability. The prediction model is subjected to variable screening and strict verification, and risk visualization is realized through a column graph. The system comprises an image processing module, a feature calculation module, a prediction analysis module and a visualization module. According to the method, SRI and DCV are innovatively introduced to accurately quantify the hematoma morphology and density characteristics, and clinical variables are combined, so that the prediction accuracy and stability are remarkably improved; the efficiency is improved through AI-assisted segmentation; and the column graph enhances the model interpretability and the clinical decision support capability.

An analysis device according to the embodiment includes a creation unit and an output control unit. The creation unit arranges a line segment indicating a point that is a product of an attribute value of a regression model obtained by regression analysis by secure computation and a regression coefficient in a direction corresponding to a sign of the corresponding regression coefficient and creates a nomogram in which the point is plotted at a position corresponding to a target point on the line segment. The output control unit outputs the created nomogram.

The application provides a model construction and verification method and nomogram for predicting the clinical prognosis of SFTS patients complicated with AP. By collecting the clinical data of SFTS patients, the independent risk factors related to adverse prognosis are screened out by using LASSO regression analysis, a multi-factor Logistic regression model is constructed, and the model is visualized by nomogram for predicting the adverse prognosis of SFTS patients complicated with AP. The model has high discrimination and calibration, and can provide an effective prediction tool for clinicians to optimize clinical decision-making.

Provided in the present invention is a grading method for evaluating the severity of the clinical symptoms of hypersplenism. In the present invention, a model for evaluating the severity of the clinical symptoms of hypersplenism is constructed by means of performing logistic regression analysis on PLT, WBC and RBC to establish a nomogram, converting regression coefficients into a visual scoring system, separately performing score assignment on the basis of primary and secondary factors, and finally constructing a total-score-based hypersplenism grading prediction model. The model in the present invention is used to evaluate the severity of the clinical symptoms of hypersplenism in patients. By means of correlation analysis of peripheral blood cell testing results of patients with hypersplenism, hypersplenism grades of the patients are evaluated, such that personalized treatment plans can be provided to the patients in a timely manner, thereby avoiding delays in treatment.

The application discloses a type 2 diabetes heart failure risk prediction system and method, and belongs to the technical field of medical diagnosis and risk assessment. The prediction system comprises the following modules: a data and feature engineering module, which is responsible for standardization processing of data and screening of key prediction factors, and obtains a core feature set for machine learning; a model construction and selection module, which uses the core feature set and trains multiple machine learning algorithms in parallel; through cross-validation and comprehensive performance evaluation, the best model is selected as a prediction model; and a model deployment and application module, which converts output results of the prediction model into a clinically usable static nomogram or online tool, and performs visual output. The application predicts by integrating clinical variables and adopting a machine learning algorithm, and provides a static nomogram and a dynamic Web application, realizes heart failure risk assessment without relying on NT-proBNP detection, and can improve the prevention and management efficiency of cardiovascular diseases.

A group of marker sets capable of effectively predicting acute pain of a patient after cesarean section is screened out, and the marker sets comprise whether the patient has preoperative anxiety or not, whether the patient is gestational diabetes mellitus or not, the age of the patient, the Pittsburgh sleep quality index (PSQI) of the patient, the gestational week age of the patient and the abdominal circumference of the patient. On the basis, a visual and quantifiable column diagram prediction model is constructed, the method can be used for evaluating the risk probability of acute pain occurring after a cesarean section patient is operated clinically before an operation, targeted and individualized pain management is provided for the patient subsequently, the pregnancy safety of the patient is improved, and physical and psychological pain of the patient after the operation is relieved.

The invention belongs to the technical field of medical diagnosis, and particularly relates to a visual column diagram construction method for identifying and diagnosing suppurative and tuberculous lumbar major muscle abscesses and interactive application. According to the invention, it is found that six indexes including serum uric acid (UA), thrombin time (TT), neutrophil absolute value (NEUT #), age (Age), fever (Fever) and erythrocyte sedimentation rate (ESR) are related to the type of the lumbar major muscle abscess, and a visual column diagram construction method for identifying and diagnosing suppurative and tuberculous lumbar major muscle abscess and interactive application are invented. And a first visual and quantifiable column diagram (Nomogram) prediction model for differential diagnosis of lumbar major muscle abscess is provided for the field. The technical problem that an abstract mathematical prediction model is converted into a visual graphic diagnosis tool which is intuitive in form and convenient to operate is solved, so that the visual graphic diagnosis tool can be separated from dependence on specific computing equipment, and clinical medical staff can quickly and accurately apply the visual graphic diagnosis tool on a diagnosis and treatment site conveniently.

The invention belongs to the technical field of medical artificial intelligence, discloses an aMCI risk automatic identification system based on a polysleep electroencephalogram, and aims at solving the problems that an existing aMCI identification technology is high in subjectivity, high in cost and difficult to popularize. The system comprises a data input module, a PSG parameter automatic extraction module, an aMCI risk prediction module and a result visualization output module which are in communication connection in sequence. The data input module receives PSG original data and preprocesses the PSG original data; a PSG parameter module calls YASA, Spindler Toolbox and K complex wave detection methods, and nine core parameters are automatically extracted; the risk prediction module outputs aMCI risk probability through LASSO regression and column diagram models; and the result module generates a PDF report, is in butt joint with a medical platform and stores historical data. The system realizes full-process automation, predicts AUC to be more than 0.95, adapts to multiple scenes, and provides an accurate and economical tool for early recognition of aMCI.

The application discloses a kidney function risk information processing method based on OCTA features and clinical data, and relates to the technical field of artificial intelligence and intelligent medical treatment. Quantitative features such as blood vessel density, perfusion area, retinal thickness and choroidal vascular volume are extracted from multi-scale fundus OCTA images of a subject, and total feature data sets are constructed in combination with clinical biochemical indexes, so as to estimate glomerular filtration rate as an outcome index for binary classification labeling. Elastic network regression is adopted to screen key modeling features, and various machine learning models such as logistic regression, random forest and gradient boosting are simultaneously trained on a training set. After 5-fold cross-validation optimization, the optimal model is selected by comprehensively considering the receiver operating characteristic curve, the calibration curve and the decision curve. A nomogram is constructed based on the optimal model, and individual early kidney function decline risk probability numerical values and auxiliary reference information of the subject are outputted for reference of clinical doctors.

The invention discloses a model for early prediction of acute kidney injury risk after geriatric sepsis patients are transferred into ICU based on clinical variables and immune inflammation indexes, 627 geriatric sepsis patients are included in the research, and the clinical variables and immune inflammation indexes of the geriatric sepsis patients in the ICU within 24 hours are collected; clinical variables of the first 6 ranked and immune inflammation indexes of the first 4 ranked related to acute kidney injury are screened out, prediction models are constructed through four machine learning algorithms respectively, the model prediction performance of the XGBoost algorithm is the best, the model prediction performance of the logistic regression algorithm is the second, and the model prediction performance of the XGBoost algorithm is the best. And a visual column diagram is made according to a logistic regression algorithm, and an early prediction model with good accuracy is established.

The invention provides a machine learning analysis method and system based on prostate tumor multi-dimensional disease data and electronic equipment. The method comprises the following steps: S1, acquiring original image data, original clinical data and pathological results of prostate dominant focus tissues; s2, screening to obtain target radiomics characteristics, and establishing a radiomics model according to the target radiomics characteristics and in combination with the pathological result; establishing a deep learning model according to the original image data in combination with a pathological result; screening to obtain target clinical features, and establishing a clinical prediction model according to the target clinical features and in combination with the pathological result; s3, carrying out fusion analysis on the image omics model, the deep learning model and the clinical prediction model aiming at the prediction result of the prostate tumor multi-dimensional disease data, screening to obtain a target fusion feature, and establishing a multi-modal fusion model according to the target fusion feature in combination with a pathological result; and S4, establishing a two-dimensional column diagram based on the multi-modal fusion model.

The application discloses a clinical prediction model for predicting the risk of purpura nephritis and a construction method of a nomogram thereof. Four independent risk factors are obtained through mathematical statistical methods, the risk independent factors include age, rash duration, D-dimer and IgG, four corresponding clinical prediction models are established according to the risk independent factors, and the optimal prediction model containing three prediction factors is obtained through mathematical analysis methods, the three prediction factors are the age of the child, D-dimer and IgG, and the occurrence risk of purpura nephritis can be predicted through the established nomogram. The application provides a convenient and effective tool for clinicians to evaluate the risk of HSPN in children with HSP, and the determination of the IgG and D-dimer levels in blood can be realized in community clinics.

The present application relates to a kind of vertebral body fracture postoperative secondary fracture risk prediction model, construction method and prediction system, including the multidimensional data of collection research object;The multidimensional data of the research object meeting the requirements is standardized pre-processing;Fractured vertebral body positioning sub-network is constructed, and the output ROI of injured vertebra is input into image parameter measurement sub-network, and the output result is converted to obtain standardized image parameter set;Standardized image parameter set and clinical data are merged, and independent predictive factor is screened;Based on independent predictive factor, the risk prediction model of vertebral body fracture postoperative secondary fracture based on nomogram is constructed.The present application realizes the automatic, standardized extraction of multimodal image parameters by cascading CNN network, integrates multi-center patient baseline data, surgery-related data and postoperative nursing data, and can be used for accurately predicting the risk of secondary OVCF within 2 years after operation by feature screening and model construction;Solve the problems of poor generalization, strong subjectivity and insufficient patient compliance in the prior art.

The invention relates to a computer-implemented method (200) for training an artificial intelligence-based model (M) and to a computer-implemented method (600) for supporting an operation on a patient's eye, comprising such a model (M), which is used to calculate and / or provide refractive output data (933) on the basis of input data (931), and in particular to create nomograms. Nomograms from the prior art may either have an accuracy in need of improvement or, if the accuracy is improved, may generate implausible profiles of the nomogram. According to the invention, more accurate nomograms based on the output data (933) are made possible without implausible states. This is made possible by a cost function (915) which comprises a non-linear output value (917), representing a measure of the prediction error, of a univariate linear regression function and at least one non-linear penalty term (919). The invention further relates to a trained model (M) or a weighting matrix (923) of such a model (M), to a computer program product, to a data processing device (800), to a computer-readable medium, and to a laser therapy device.

The invention provides a gastric cancer CT image T stage intelligent decision-making method and system based on deep learning, and the method comprises the steps: obtaining CT image data, carrying out the preprocessing of the CT image data, and obtaining a standardized venous phase CT image; the standardized vein phase CT image is input into an improved residual network for feature extraction, feature maps of different levels are obtained, and a gastric cancer T-stage prediction result is obtained through the feature maps; obtaining a visual thermodynamic diagram; constructing a radiomics score through the regression model, and generating a dynamic column line graph according to the radiomics score in combination with the clinical parameters; and outputting a gastric cancer T stage prediction result, a visual thermodynamic diagram and a dynamic column diagram. The method gets rid of the dependence of manual marking, automatically locates the tumor area and extracts the features directly based on the original CT image, and avoids the deviation caused by the difference of operators.

A computer-implemented method (200) for training an artificial intelligence-based model (M) and a computer-implemented method (600) for supporting surgery on a patient's eye are provided, comprising such a model (M) which serves to calculate and / or provide refractive output data (933) based on input data (931), and in particular to generate nomograms. Prior art nomograms may either have insufficient accuracy or, if the accuracy is improved, generate implausible nomogram progressions. According to the invention, more accurate nomograms based on the output data (933) are enabled without implausible states.This is made possible by a cost function (915) comprising a nonlinear output value (917) of a univariate linear regression function representing a measure of the prediction error and at least one nonlinear penalty term (919). Furthermore, a trained model (M) or a weighting matrix (923) of such a model (M), as well as a computer program product, a data processing device (800), a computer-readable medium, and a laser therapy device are described.

The application discloses a kind of long-term prognosis prediction system and method of liver disease patient, comprising the following steps, step S1, the clinical data of cirrhosis acute decompensation and acute-on-chronic liver failure patient are collected, including anthropometry data, vital signs, laboratory data and the albumin binding capacity obtained by specific detection;Step S2, the competitive risk model for predicting the death of cirrhosis acute decompensation and acute-on-chronic liver failure patient after 1 year of admission is constructed, the prediction model is constructed using competitive risk model, and dynamic nomogram visualization prediction model is made into convenient prediction tool;Step S3, the prediction performance of model is evaluated by receiver operating characteristic curve.The albumin binding capacity and the long-term prognosis of decompensated cirrhosis and acute-on-chronic liver failure patient establish index relationship with great clinical significance, which can further optimize clinical management and guide drug treatment.

The invention discloses an epidural delivery analgesic lower head dystocia risk prediction method and system. The method comprises the following steps: retrospectively collecting clinical feature data of a parturient to be parturified under epidural delivery analgesia; the method comprises the following steps: determining classification tangency points of continuous variables according to clinical medicine consensus, literature review or statistical distribution, converting the continuous variables into ordered or disordered classification variables, performing code conversion on the collected classification variables, and constructing a structured data set; carrying out preliminary screening on all feature data by using single-factor logistic regression analysis and carrying out multi-collinearity diagnosis; the screened features are incorporated into a multi-factor logistic regression model for optimization to determine key prediction variables, and a logistic regression prediction model is constructed to derive a logistic regression equation; and converting the logistic regression model into a Nomogram column graph model, and outputting a prediction result of the occurrence risk probability of the head dystocia. According to the scheme, early risk prediction of epidural delivery analgesia lower head dystocia is realized, and an earlier decision window is provided for clinic.

The invention provides a method and a model for predicting the risk of early renal function decline after renal partial resection of a renal cell carcinoma patient. According to the method, clinical parameters of a patient are obtained through a preoperative nomogram or preoperative-intraoperative combined nomogram prediction model, risk integrals of all the parameters are distributed, a total integral is calculated, and the postoperative early renal function decline prediction probability is determined and visually output. The parameters of the preoperative nomogram prediction model comprise age, diabetes history, preoperative estimated glomerular filtration rate, kidney score and radiomics score; the joint model newly increases the intra-operative ischemia time. Model construction comprises the steps of CT image standardization, region segmentation, radiology feature extraction and preprocessing and the like, and can assist in clinical accurate assessment of risks and optimization of treatment decisions.

This invention discloses a method and apparatus for predicting the risk of urinary tract infection (UTI) in children with urinary tract stones based on nomograms. The method includes: acquiring and preprocessing clinical data of pediatric patients to obtain a raw dataset; performing univariate analysis on the training set based on clinical indicators to obtain candidate variables significantly associated with UTI; performing multivariate logistic regression analysis on the candidate variables to obtain independent predictors; constructing a nomogram model based on the independent predictors; calculating a corresponding score for each independent predictor in the nomogram model; calculating the total score of the independent predictors and obtaining the prediction probability; and performing quantitative evaluation and curve analysis on the prediction probabilities to obtain the UTI risk assessment results. This invention is the first to organically integrate four indicators: gender, mean CT value, U-LEU, and NIT, greatly improving the efficiency of clinical decision-making.

The invention relates to the technical field of machine learning and oral medical treatment, in particular to a risk assessment method and system for the influence of malformation on posterior tooth adjacent face caries disease, and the method comprises the steps: obtaining the feature data of a to-be-assessed target object; performing variable assignment on the feature data of the to-be-evaluated target object to obtain predicted variable data; screening the predictive variable data based on a risk assessment model to obtain risk factor data; drawing a column graph according to the screened risk factor data; and according to the risk factor data and the column diagram, obtaining the illness probability of the posterior tooth adjacent face caries of the to-be-evaluated target object. On the basis of data including ANB angle data, Wits value data, APDI angle data, Angle classification data, dentition crowding degree data and the like related to error deformity, the invention provides the model for predicting the posterior tooth adjacent face caries and the method for predicting the risk of the posterior tooth adjacent face caries on the basis of the model, and a new method for predicting the risk of the posterior tooth adjacent face caries in the prior art is supplemented.

The invention discloses a neonatal asphyxia risk prediction model construction method and device, which are applied to the field of computer models for disease prediction, and are used for acquiring clinical feature data of a neonatal asphyxia group and a healthy neonatal group, including pregnant mother information, fetus information and other information; performing single-factor analysis on clinical risk factors between the two groups of data, and screening out single-factor predictive variables; and by taking the screened variables as independent variables and taking whether suffocation occurs as dependent variables, carrying out binary Logistic regression analysis by adopting a forward stepwise method, and establishing a risk prediction model. A visual column diagram is drawn according to the prediction model, and the prediction model has good distinction degree and calibration degree through evaluation. According to the method, key independent risk factors are screened from multiple factors through a scientific statistical analysis method, a prediction model beneficial to early clinical recognition of neonatal suffocation high-risk groups is constructed, a basis is provided for timely making individual intervention measures, and the method is of great significance in preventing neonatal suffocation and improving the survival rate of neonates.

This invention discloses a method for constructing a risk prediction model for blinding diabetic retinopathy (STDR), belonging to the field of diabetic lesion detection. It addresses the problem that STDR screening at the grassroots level relies on specialized resources and that existing models have poor adaptability. The method includes: screening eligible type 2 diabetic patients and organizing clinical data; detecting 15 core laboratory indicators and calculating derived indicators as candidate variables; dividing patients into non-STDR and STDR groups according to DR classification and DME diagnosis results; selecting variables using a dual-dimensional strategy of "statistical significance + clinical relevance" through binary logistic regression, multicollinearity test, and incorporating six indicators including age, duration of diabetes, and MLR to construct a mathematical prediction model; and then building a nomogram visualization prediction model based on the model formula. All indicators included in this model are routinely available in clinical practice. The model has been validated with an AUC of 0.825, sensitivity of 87.2%, specificity of 67.8%, good fit, and good clinical applicability. It can quickly assess the risk of STDR and is suitable for grassroots medical scenarios.

The invention belongs to the technical field of child severe risk prediction, and discloses a child severe mycoplasmal pneumonia risk prediction method based on a dynamic column graph, and the method comprises the following specific steps: 1, receiving user input, and receiving multiple clinical indexes of a child patient input by a user through a Web interface; 2, sending data, and automatically sending the input data to a server without refreshing a page; and step 3, risk prediction model calculation: the server calls a pre-stored risk prediction model to carry out processing calculation so as to obtain a risk probability value. The quantitative prediction model pre-trained in the background and based on the multi-dimensional clinical data is called for calculation, a subjective judgment mode depending on the personal experience of a doctor is abandoned, the model is obtained based on large-scale clinical data training, the weight and the structure of the model are fixed and objective, the system outputs an accurately quantified risk probability value for each child patient, and the accuracy of the risk probability value is improved. And missed judgment or misjudged judgment caused by experience difference of doctors is greatly reduced.

The present application relates to the technical field of diabetes complication health management, in particular to a diabetes complication management method and system based on multi-modal data fusion and reinforcement learning, the method acquires continuous glucose monitoring high-frequency data, low-frequency discrete clinical indicators and survival data; uses a sliding window and a time sequence alignment algorithm to perform feature extraction and interpolation, and constructs a time-varying covariant state vector; uses a Cox proportional risk model containing an L1 regularization term to solve the optimal regression coefficient and calculate the instantaneous risk ratio, and then constructs a dynamic nomogram model to map the disease-free survival probability; the state vector, survival probability and risk ratio are input into the policy network of a reinforcement learning intelligent agent as the environment state, and the optimal intervention strategy is output in the preset action space; finally, the risk improvement indicators of the last moment and the current moment are used as reward signals to update the network parameters in a closed loop. The present application solves the problem of heterogeneous data fusion and realizes safe and personalized dynamic closed-loop health management.

The invention relates to the technical field of medicine, in particular to a column diagram model for predicting renal function prognosis of a CKD patient and an establishment method. The invention provides a column diagram model for predicting the probability that a CKD patient does not suffer from renal failure in 1, 2 and 3 years and an establishment method of the column diagram model, five risk factors including hypertension, mesangial hyperplasia degree, renal tubule atrophy degree, blood potassium and glomerular filtration rate are screened out from the column diagram model, and the five risk factors are used for establishing the column diagram model. In the training queue, the C index is 0.898, and the AUC values of the first year, the second year and the third year are 0.983 (95% CI 0.966-1.000), 0.955 (95% CI 0.924-0.986) and 0.945 (95% CI 0.918-0.972) respectively. In the external verification queue, the C index is 0.918, and the corresponding AUC values of the first year, the second year and the third year are 0.919 (0.853 to 0.986), 0.902 (0.822 to 0.982) and 0.915 (0.846 to 0.985). The calibration curve and the DCA curve show that the accuracy of the model is in a fitting range, which shows that the column graph model has good prediction capability.

This invention relates to the field of medical data processing technology, specifically to a method and system for constructing a cervical metastasis prediction model for T1 stage oral squamous cell carcinoma. Addressing the problems of existing technologies, such as the difficulty in accurately assessing the risk of occult lymph node metastasis in T1 stage patients preoperatively, the decreased discrimination and calibration of existing models due to mixed staging, and the lack of intuitive and usable tools, this application constructs a cervical metastasis prediction model for T1 stage oral squamous cell carcinoma. The specific construction method is as follows: Obtaining the subject's characteristic data; using the occurrence of cervical lymph node metastasis as the dependent variable, performing univariate logistic regression analysis on the independent variables of the characteristic data to determine candidate risk factors; performing multivariate logistic regression analysis on the obtained candidate risk factors to screen independent predictive factors; constructing a nomogram based on the independent predictive factors, and predicting the risk of cervical lymph node metastasis in T1 stage oral squamous cell carcinoma patients based on the nomogram.

The application discloses a kind of small stroke patient family caregiver burden risk prediction method and system, comprising: constructing research cohort and collecting the baseline clinical data of patient acute phase and the baseline information of family caregiver;Through statistical analysis, the independent correlation of the prediction variable of caregiver medium-severe burden is screened out;Based on the variable and its regression coefficient screened out, a nomogram prediction model is constructed, the score of each variable value is converted, the individual risk probability is predicted by accumulating total score;And ROC curve and Bootstrap resampling method are used to verify the model.The system includes data input, prediction calculation and result output module, and can be integrated into clinical information system.The application can realize individualization, quantification early warning of future burden risk of family caregiver of small stroke patient in acute phase, provide key decision support for implementing precise nursing intervention, effectively improve nursing resource efficiency and family care quality.

This invention discloses a method, system, and equipment for determining the safe distance of air shock waves during tunnel blasting excavation, relating to the field of tunnel blasting excavation. The method includes: determining the overpressure safety control standard ΔP0 for air shock waves; calculating the overpressure ΔP1 of the air shock wave propagating along the tunnel and the overpressure ΔP2 of the air shock wave at the tunnel entrance based on blasting design parameters and tunnel characteristic parameters; calculating the overpressure ΔP3 of the air shock wave at any distance along the tunnel axis in an open-air environment at the tunnel entrance based on ΔP2; drawing a nomogram of the safe distance of the air shock wave action within the tunnel based on ΔP0 and ΔP1, and determining the first safe distance based on the nomogram; drawing an isopleth map of the air shock wave overpressure based on ΔP3, and determining the second safe distance based on ΔP0 and the isopleth map. This invention eliminates the need for extensive blasting tests, providing a new approach for the safety protection of blasting air shock waves near the tunnel face and entrance.

The invention belongs to the technical field of biological medicines, and relates to a construction method of a chronic hepatitis B and hepatic fibrosis combined prediction model based on serum CACs and M30. The construction method comprises the following steps: acquiring liver histological analysis results and peripheral blood data of a plurality of chronic hepatitis B patients; screening candidate influence factors used for constructing the chronic hepatitis B and hepatic fibrosis combined prediction model; independent influence factors are determined through Logistic regression analysis; using a forward stepwise method based on maximum likelihood estimation to bring the independent influence factors into regression analysis, and constructing a column graph joint prediction model; and evaluating the prediction performance of the chronic hepatitis B and hepatic fibrosis combined prediction model. The diagnosis efficiency of the constructed model on chronic hepatitis B hepatic fibrosis is obviously better than that of APRI, FIB-4, RPR and other traditional prediction models, the sensitivity, specificity and accuracy are obviously improved, and higher clinical net income is achieved.