75 results about "Nomogram Chart" patented technology

The invention relates to the technical field of medical data analysis and clinical risk prediction, provides a construction method of a glucocorticoid induced diabetes risk prediction model based on an LASSO algorithm, and belongs to the technical scheme of data processing executed on computing equipment. Clinical data of inpatients receiving systemic glucocorticoid treatment are collected, key variables are screened through LASSO regression, and a Logistic regression model is constructed to achieve risk prediction. The model is composed of four conventional clinical indexes, has good distinction degree and calibration degree, and finally realizes individualized risk assessment through column diagram form output. The method is simple, practical and accurate, and is suitable for SDM prediction and intervention management of clinical high-risk groups.

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 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 invention provides a risk prediction model for the death rate of senile sepsis patients in 28 days after the senile sepsis patients are transferred into ICU and a construction method of the risk prediction model, independent risk factors related to death occurrence in 28 days are determined through single-factor and multi-factor logistic regression analysis, a column chart is made, the prediction model of the patent is constructed, and the risk prediction model of the senile sepsis patients in 28 days can be used for predicting the death rate of the senile sepsis patients. A comparison result with a standard APACHE II score and a decline index (FI-lab) in recent years or an AUC of an ROC curve of other multi-index models shows that the prediction model provided by the invention is better in efficiency.

The present invention provides a postoperative risk assessment system, device, storage medium and program product, relating to the field of computer technology. The system includes: an extraction module for extracting independent risk factors for early saphenous vein graft failure from the postoperative data of a patient; a score determination module for determining the scores of each factor in the independent risk factors based on the scoring scale in the nomogram model, where the nomogram model is constructed based on the independent risk factors; and a prediction module for predicting the risk information of early saphenous vein graft failure after coronary artery bypass grafting based on the risk coefficient corresponding to the total score of each factor in the independent risk factors. Based on the results of Lasso regression analysis and Logistic regression analysis, the present invention constructs a prediction model for early saphenous vein graft failure with risk factors such as QFR > 0.80, PI > 3.0, complications, and non-left main stenosis as the main body, thereby improving the accuracy of the risk assessment of early saphenous vein graft failure in postoperative patients.

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

The 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.

The invention relates to the technical field of medical treatment, in particular to a prediction device and a prediction model for predicting the probability of complications after pulmonary lobectomy or sub-pulmonary lobectomy and application of the prediction device and the prediction model. Aiming at prediction errors of an AS method and a QCT traditional mode at present, the invention finds that FEV1QCT (postoperative FEV1 value estimated based on QCT) is an independent prediction factor of short-term pulmonary complications after lung cancer lobectomy or sub-lobectomy through research, and a model based on QCT has relatively high prediction performance; the specific technical scheme of the prediction device for predicting the probability of the complications after the pulmonary lobe or sub-pulmonary lobe resection, the prediction model comprising the column diagram and used for predicting the probability of the complications after the pulmonary lobe or sub-pulmonary lobe resection and the application of the prediction model are obtained and provided, and the prediction accuracy of the PC after the pulmonary segmental resection is improved. Personalized selection of appropriate operation schemes and postoperative management schemes is facilitated, and the living quality and prognosis of patients are improved.

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 belongs to the technical field of tumor prognosis prediction, and relates to a colorectal cancer allotropic liver metastasis prediction method and system.The method comprises the steps that colorectal adenocarcinoma cases are collected, the volume of an intra-tumor region-of-interest is delineated and automatically expanded to generate the volume of a peritumor region-of-interest, image omics characteristics are extracted through a pyradiomics packet, core omics characteristics are screened out, and the colorectal cancer allotropic liver metastasis prediction result is obtained. The method comprises the following steps: respectively constructing an intratumoral model and a peritumoral model, analyzing and integrating intratumoral and peritumoral core omics characteristics through logistic regression to form a combined radiomics model, integrating the combined radiomics model and clinical risk factors, establishing a column diagram for predicting the non-hepatic metastasis lifetime, and dividing patients into a low-risk group and a high-risk group according to a column diagram score threshold value; according to the method, LMFS prediction results of 1-5 years can be quickly output, and 0.6911 is set as a standardized risk stratification cut-off value so as to support risk stratification and personalized treatment decision of a patient.

The application provides a clinical feature-machine learning nomogram method for predicting prostate cancer risk stratification, relates to data processing, data analysis, machine learning and nomogram, and constructs a clinical feature-machine learning nomogram which is easy to use, good in interpretability and powerful in function by combining machine learning and nomogram technology, supports visualization of model prediction results, and realizes the function of constructing a clinical feature-machine learning nomogram for predicting prostate cancer risk stratification. Considering the simplicity and interpretability of the nomogram and the high efficiency and robustness of the machine learning model, the clinical feature-machine learning nomogram provided by the application can be used as an auxiliary tool for preoperative evaluation of prostate cancer risk stratification, and provides necessary information for individual diagnosis and treatment of prostate cancer patients.

The invention provides a grading method for evaluating the clinical symptoms of hypersplenism, and the method comprises the steps: constructing a model for evaluating the clinical symptoms of hypersplenism, carrying out the Logistic regression analysis of the model through PLT, WBC and RBC, building a column diagram, converting a regression coefficient into a visual grading system, carrying out the score assignment according to primary and secondary factors, and carrying out the evaluation of the clinical symptoms of hypersplenism. And finally, constructing a splenohyperactivity grading prediction model according to the total score. The severity of the clinical symptom of the hypersplenidism of a patient is evaluated by adopting the model, the splenidism grading of the patient is evaluated by utilizing the correlation analysis of the peripheral blood cell detection result of the hypersplenidism patient, a personalized treatment scheme can be given to the patient in time, and the illness state is prevented from being delayed.

The invention relates to the technical field of medical prognosis evaluation, in particular to a sex-specific advanced young non-small cell lung cancer prognosis model construction method, which collects clinical data of young metastatic non-small cell lung cancer patients, including sex, age, body mass index, D-2 polymer level, gene mutation state, metastasis site, tumor marker and hematology index. Data are divided into male and female queues, male specific prognostic factors (body mass index, gene mutation state and liver metastasis state) and female specific prognostic factors (D-2 polymer level and gene mutation state) are identified through Cox proportional risk regression analysis, and male and female column map prognostic models are respectively constructed based on the factors. All prognosis factors are converted into risk scores, the corresponding relation between the total score and the survival probability is established, finally, the model can output individualized survival probability prediction results of patients of different genders, and important reference is provided for clinical treatment and prognosis evaluation.

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 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 relates to the technical field of respiratory medicine diagnosis, in particular to a respiratory system disease prediction method, system and device and a storage medium. According to the method, multiple types of patient data are obtained, the data are used in a combined mode, the illness probability and the illness degree of the patient are obtained through the column graph model based on the score corresponding to the data, and illness can be comprehensively explained through the patient data.

The invention relates to the field of health behavior prediction and rehabilitation management, and discloses construction and application of a stroke patient home functional exercise compliance risk prediction model. The method comprises the following steps: collecting demographic and clinical data and scale evaluation data of a stroke patient, and carrying out missing value processing and preprocessing; identifying a compliance potential category by adopting potential profile analysis, and determining an optimal cutoff value of a total score of a scale through subject working characteristic curve analysis to form a dichotomy result; key predictive factors are screened in a training set by applying LASSO regression, and a'non-good compliance 'predictive model is established by incorporating the key predictive factors into multivariate Logistic regression; an online dynamic column graph webpage calculator is developed based on the Shiny technology, and a user is supported to input variables in real time and output a prediction probability and a confidence interval; and the discrimination degree, the calibration degree and the clinical net income are analyzed and evaluated through five-fold cross validation, Hosmer-Lemeshop test and decision curve analysis. According to the invention, rapid identification and hierarchical management of high-risk patients can be realized, and a basis is provided for follow-up visit and individualized intervention.

This invention relates to the field of biomedical technology and proposes the use of TM9SF1 in the preparation of a reagent for monitoring the course of sepsis. Specifically, it proposes the use of a marker in the preparation of a reagent for monitoring the course of sepsis, wherein the marker comprises the TM9SF1 gene or the mRNA corresponding to the TM9SF1 gene. In the technical solution of this invention, a nomogram model for predicting the severity of sepsis can be constructed by using the TM9SF1 gene as a marker. Receiver-operating characteristic (ROC) curve validation demonstrated an AUC of 0.883, a sensitivity of 91.5%, and a specificity of 78.4%. For predicting mortality in patients with septic shock, the nomogram model achieved a C-index of 0.931.

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 secondary cesarean delivery postpartum hemorrhage risk early warning model, and relates to the technical field of gynaecology and obstetrics medical treatment. The construction method of the early warning model comprises the following steps: data collection: collecting clinical data of a puerpera in secondary cesarean section, including basic and historical information, information of the gestation period, information of the delivery period of the gestation period and an outcome variable; single factor analysis: screening significant factors of an assisted reproductive technology, a pre-pregnancy body mass index, a pre-antenatal body mass index, pregnancy and birth times, previous cesarean birth times, the number of fetuses, gestational week termination, combined hypertension, combined hysteromyoma, a placenta and uterus relationship, fetal prolapse and uterus height. According to the method, based on large sample data and Logistic regression analysis, the gestational week of gestation termination is screened out, three independent risk factors of hysteromyoma and the relationship between the placenta and the uterus are combined, and model construction has statistical significance; risk scores are quantified through column diagram design, abstract risks are converted into visual values, and the clinical decision-making efficiency is improved.

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 discloses a prediction method and device for complete remission of an endometrial cancer patient, a medium and a product, and relates to the technical field of biologication.The method comprises the steps that feature extraction is conducted on an obtained data set, and a plurality of radiomics features are obtained; establishing an MR scoring model according to the plurality of radiomics features; performing prediction effectiveness evaluation on the MR scoring model to obtain an MR score; performing normal distribution test on the plurality of radiomics characteristics to obtain a plurality of radiomics characteristics converted into normal distribution; carrying out variance homogeneity analysis on the plurality of radiomics characteristics converted into normal distribution, and determining the difference of the plurality of radiomics characteristics converted into normal distribution and prediction factors related to complete remission of the patient; and constructing a column graph model according to the MR score and the prediction factor. According to the method, young EC patients suitable for retaining fertility can be accurately identified, the accuracy of a clinical decision-making process is improved, and unnecessary uterine cavity invasive operation is reduced.

The present invention belongs to the field of biomedicine, and relates to an obstetric surgery risk prediction system, and specifically to a prediction system for massive bleeding during artificial abortion of unexpected pregnancy after cesarean section and a construction method thereof. The prediction system for massive bleeding during artificial abortion of unexpected pregnancy after cesarean section includes a variable input module and a nomogram model prediction module; the variable input module is used to input variables of the maximum diameter of the gestational sac, the number of cesarean sections, the medical history of uterine incision pregnancy, the distance between the gestational sac and the uterine incision scar, and the variable of the yolk sac visible by ultrasound; by substituting the values ​​of each variable into the nomogram model prediction module, the probability value of the risk of surgical bleeding can be obtained, which provides a reliable and effective reference for the configuration of obstetric surgery resources, and increases the investment in physical therapy resources for patients with higher bleeding risks. This scheme can solve the technical problem that the existing technology lacks a prediction system for the risk of massive bleeding during artificial abortion of unexpected pregnancy after cesarean section, and has an ideal prospect for application and promotion.

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.

The invention discloses a primary metastatic breast cancer postoperative BCSS column diagram prediction model and a construction method thereof, and belongs to the technical field of biomedicine. The construction method comprises the following steps: collecting clinical pathological characteristics of a patient after a preliminary diagnosis metastatic breast cancer operation; a Cox regression model is adopted to carry out regression analysis on clinical pathological features of a patient without radiotherapy, and independent BCSS predictive variables are screened out; and determining an index for establishing a column graph prediction model from the BCSS prediction variable, and establishing the prediction model. The prediction efficiency and clinical practicability of the model are evaluated through the C index, the ROC curve, the calibration curve and the clinical decision curve, the patients are divided into low-risk groups, medium-risk groups and high-risk groups according to the total score of the column diagram, and finally the benefit conditions of each risk group receiving postoperative radiotherapy are observed. The column graph model constructed by the method has good distinction degree and calibration degree, provides a basis for a clinician to formulate an individualized postoperative radiotherapy strategy, and has important clinical significance for improving long-term survival of a patient with metastatic breast cancer initially diagnosed.

The invention discloses a kit for detecting plasma IgG glycosylation level and a detection method thereof, and relates to the technical field of biological detection.Plasma IgG is specifically captured through protein A, four biotinylation lectins are combined, sialylation, galactosylation, fucosylation and mannosylation levels of IgG can be synchronously and quantitatively detected, a standardized ELISA system is established, and the kit is used for detecting the plasma IgG glycosylation level. Operation is easy and convenient, repeatability is good, and the problem that multi-dimensional simultaneous detection cannot be achieved in the prior art is solved. The kit is low in cost, does not need complex instruments, is suitable for large-scale clinical popularization, and fills the market blank of commercial IgG glycosylation ELISA kits. Clinical samples of colorectal cancer prove that patients and healthy people can be effectively distinguished, in-vitro risk assessment can be realized by combining a column diagram model, the detection efficiency is excellent, and the method can be expanded to be applied to assessment and monitoring of various tumor and inflammation related diseases, and has high clinical value.

The application belongs to the technical field of medical decision support, and discloses a knee joint postoperative distal deep vein thrombosis prediction system; by acquiring multi-dimensional parameters such as coagulation function, metabolic function, inflammation ratio index and intra-articular injection history, the sodium hyaluronate injection history is converted into an articular lesion severity score, an inflammation-coagulation coupling characteristic vector and a metabolic disturbance correction model are innovatively constructed, and the synergistic effect among multiple systems is captured. A penalty regression model with cross-validation is used for sparse prediction factor screening, redundant variables are screened out through a regularization path shrinkage technology, and key prediction factors are reserved. Finally, an individualized thrombosis risk probability nomogram is constructed, and the prediction accuracy is verified through resampling calibration. The application realizes the multi-system integration of inflammation, coagulation, metabolism and joint degeneration factors, converts the complex statistical model into a clinically friendly tool, and optimizes the postoperative thrombosis prevention and control strategy.

The invention discloses a prediction model, a construction method and a prediction system for liver cancer TACE postoperative active lesion risk degree stratification, and belongs to the technical field of molecular biology research and bioinformatics. The method comprises the following steps: firstly, acquiring basic data established by a model, then taking the survival degree of the active lesion as an outcome index in a training set, analyzing and screening independent prediction factors of the survival degree of the active lesion by utilizing single-factor and multi-factor logistic regression, establishing a column graph model, acquiring corresponding coefficients of the independent prediction factors, and finally, calculating the survival degree of the active lesion according to the corresponding coefficients. And constructing an active lesion risk degree total score RSV score of the liver cancer TACE postoperative active lesion risk degree layering prediction model. According to the model constructed by the invention, the survival degree of the liver cancer TACE postoperative active focus can be evaluated, and multi-dimensional verification proves that the model has relatively high reliability.