48 results about "Nomogram" patented technology

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

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.

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.

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.

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.

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 invention discloses a fracture risk prediction method and system, and relates to the technical field of health management. According to the prediction method, serious sarcopenia is selected to diagnose and assess the sarcopenia state, the ratio of monocytes to lymphocytes reflects the immune inflammation level, the prediction model is constructed in combination with the femoral neck bone mineral density T value, and the fracture risk influence factors are comprehensively covered; and the risk identification capability on special high-risk groups with sarcopenia, immune dysfunction and the like is effectively improved. And secondly, only three target independent variables are selected, and the three target independent variables are indexes which can be directly obtained or simply calculated in clinical routine examination, so that the data collection difficulty is greatly reduced. Meanwhile, the prediction model is visualized through a column diagram, complex calculation is not needed, a clinician can quickly inquire the fracture risk score through the actual value of the target subject, and the method is suitable for being efficiently applied to conventional outpatient service, hospitalization and other scenes.

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.

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.

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 discloses a collagen feature-based lymph node state judgment method and system, a terminal and a storage medium, and the method comprises the steps: obtaining a pancreatic duct gland data sample, and carrying out the collection of a multi-photon image, and obtaining a target multi-photon image; obtaining a macroscopic collagen feature in the target multi-photon image, and calculating a first score of the macroscopic collagen feature; obtaining a microscopic collagen feature in the target multi-photon image, and calculating a second score of the microscopic collagen feature; constructing a column graph model according to the first score and the second score; and acquiring current pancreatic duct gland data of the patient, and inputting the data into the column graph model to obtain a lymph node metastasis result. According to the method, the macroscopic collagen features and the microcosmic collagen features in the pancreatic duct gland data sample are extracted and combined to obtain the column graph model, and accurate judgment of lymph node metastasis can be achieved.

The invention relates to a bromhidrosis postoperative wound infection risk prediction method and a bromhidrosis postoperative wound infection risk prediction system, aims to solve the problems that an existing clinical evaluation method depends on subjective experience and lacks a standardized tool, and performs quantitative prediction on the individual infection risk of a patient by utilizing a column graph model constructed based on multi-factor analysis. The method comprises the following steps: systematically collecting clinical index data of a patient, wherein the clinical index data comprises general data and operation-related indexes; inputting the data into a pre-constructed column graph model, and obtaining a comprehensive risk score by matching a corresponding score for each index and performing accumulation; and finally, the risk level is divided into a low-risk class and a high-risk class according to a preset threshold value, and an intuitive basis is provided for clinical decision making. According to the method, the operation process is standardized, the result output is visual, the method can be effectively integrated into the clinical working process, and a practical auxiliary tool is provided for medical staff to identify high-risk patients and implement targeted prevention and nursing measures, so that the quality and efficiency of postoperative management are expected to be improved.

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 invention provides a united station process parameter optimization method and system based on a Nomogram technology, and the method comprises the steps: determining to-be-analyzed equipment related to energy consumption, and carrying out the thermodynamic calculation to obtain a single-equipment sub-energy-flow model; splicing the sub-energy flow models based on the process flow of the united station to obtain an overall energy flow model of the united station system; performing sensitivity analysis based on the overall energy flow model to determine target analysis parameters; performing variable-working-condition energy consumption simulation operation by taking the target analysis parameter as an adjustment parameter, and drawing a corresponding Nomoh map based on the operation parameter and an energy consumption result; and obtaining process parameter curve distribution of actual operation of the united station, and comparing and analyzing the process parameter curve distribution with the curve distribution of the nomogram to realize abnormal condition identification, abnormal energy consumption prediction and improvement measure formulation. By adopting the scheme, the defects of complex calculation process and insufficient intuition in the prior art can be overcome, and rapid and accurate judgment and adjustment of parameters can be conveniently realized on site.

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 relates to the technical field of crossing of medical artificial intelligence and precision medical treatment, and discloses a head and neck squamous cell carcinoma prognosis prediction method based on an immunohistochemical marker, which comprises the following steps: acquiring clinical variable data and immunohistochemical marker data of a patient, and preprocessing the clinical variable data and the immunohistochemical marker data; screening prognosis prediction factors by adopting regularization regression analysis, constructing a column graph model based on a screening result, and generating a visual prognosis prediction tool; the performance of the visual tool is verified through multiple verification indexes, and the prediction accuracy is guaranteed; on the basis of the verified tool, an online prognosis prediction calculator is developed, and individualized risk stratification and prognosis prediction of the patient are achieved. By integrating multi-dimensional clinical and molecular pathology data and optimizing the model construction and verification process, the accuracy of head and neck squamous cell carcinoma prognosis prediction is improved, and the developed online prognosis tool is convenient to operate, can directly serve clinical diagnosis and treatment decisions, and has high clinical practical value.

Embodiments include an automated non-invasive method of assessment of an examined subject utilizing an electrocardiogram (ECG) system including: an electronic unit configured to connect to the examined subject, a memory unit configured to contain a database of Z-score-based nomograms of a first set of ECG variables from historic data of healthy individuals, a computer interface system, an adaptive confirmatory enhancement (ACE) module connected to various machine learning algorithms, an oversAIght module with artificial intelligence determining which algorithm to use, and a report generator, the method comprising: determining, by the computer interface system of the ECG system, a disease diagnosis of the examined subject based on a determination that the digital ECG values of the second set of ECG variables of the examined subject are abnormal.

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 invention relates to the technical field of medical image processing. The invention discloses a habitat imaging-based gastric cancer neoadjuvant chemotherapy curative effect prediction method and a habitat imaging-based gastric cancer neoadjuvant chemotherapy curative effect prediction system, and the method comprises the following steps: S1, obtaining pre-treatment CT and clinical pathology information, and marking the maximum tumor area and upper and lower layers; s2, dividing the marked region into habitat sub-regions with different biological characteristics through habitat imaging; and S3, performing prognosis analysis on the habitat subregion, screening independent prognosis features related to survival, and generating a habitat subregion image. And S4, constructing a joint attention model, inputting images of the marked area and the habitat subarea, predicting the curative effect and the total lifetime, and obtaining a survival score. And S5, integrating clinical pathological information and survival scores, performing prognosis analysis, constructing a column graph, and evaluating correlation. After a CT image label before treatment is obtained, habitat imaging is used for dividing subareas, related images are generated, the model is input in a combined mode, tumor heterogeneity is revealed, and the curative effect is predicted.

In order to solve the problems that an existing viral pneumonia related prediction model lacks accurate distinguishing of disease grading, model construction depends on laboratories and iconography data, and interpretability is insufficient, the invention provides an instant interpretable viral pneumonia disease grading distinguishing model construction method based on symptoms and signs. Comprising the following steps: acquiring clinical information of a subject, and constructing a sample data set; determining N preliminary clinical manifestation variables, and screening out M variables as clinical symptom sign feature vectors; respectively constructing a plurality of machine learning models, and determining an optimal model; the M clinical symptom sign feature vectors are incorporated into an optimal model, and a viral pneumonia condition grading discrimination model is established; carrying out SHAP interpretability analysis on the optimal model; and constructing an ordered logistic regression model, drawing a normogram, and displaying scores corresponding to different clinical symptom sign feature vectors and illness state grading discrimination probabilities according to the normogram, so as to realize accurate identification of early illness states and clinical convenience operability.

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 discloses a nomogram pancreatic cystic disease diagnosis model construction method, which comprises the following steps of S1, acquiring enhanced MRI (Magnetic Resonance Imaging) images and clinical pathological data, and completing data set division; s2, acquiring a focus region of interest for analyzing region calibration; s3, preprocessing the focus region of interest, extracting radiomics features, completing repeatability evaluation and feature screening, and constructing a candidate feature set; s4, an improved quadratic discriminant analysis algorithm is adopted to construct a radiomics model, hyper-parameters are adjusted, indexes such as AUC are evaluated, and radiomics scores are output; s5, screening key clinical variables based on regression analysis, and generating a clinical input feature set; s6, combining radiomics scores and clinical features to construct a combined diagnosis model; and S7, outputting a Nomoh map, performing three-data-set performance evaluation, and verifying model discrimination capability, calibration consistency and clinical effectiveness. The intelligent, accurate and visual pancreatic cystic lesion diagnosis system realizes intelligent, accurate and visual pancreatic cystic lesion diagnosis.

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.