44 results about "Clinical variables" patented technology

The invention discloses an intraoperative patient dynamic adverse event real-time early warning system and method, and relates to the technical field of intelligent diagnosis and treatment data processing, and the real-time early warning system comprises an intraoperative data unified preprocessing module which is used for preprocessing clinical variables and outputting structured triples; the variable structure modeling module is used for carrying out feature extraction, and the variable graph feature sequence output time sequence modeling module is used for modeling dynamic information of historical and future contexts at the same time and outputting a context feature sequence H = {ht}; the feature fusion and risk prediction module is used for fusing gt and ht. The risk score sequence prediction interpretability module for outputting continuous time steps is used for carrying out feature attribution analysis on prediction output. The dynamic adverse event real-time early warning system and method are innovative in structure, accurate in prediction, high in interpretability and suitable for deployment, can adapt to complex data characteristics in an operation, and have high real-time performance, high robustness and clinical interpretability.

The invention relates to a kidney lump benign and malignant analysis method and device based on a laparoscopic ultrasound image, and belongs to the technical field of medical image processing.The method comprises the steps that radiomics characteristics of the laparoscopic ultrasound image are obtained, and radiomics scores of lump malignant risks are calculated according to the radiomics characteristics; determining an independent risk factor corresponding to the patient clinical variable, and constructing a clinical prediction model according to a mapping relationship between the patient clinical variable and the independent risk factor; and according to the radiomics score and the clinical prediction model, constructing a kidney lump benign and malignant analysis model. The technical problem that in the prior art, massive quantitative image features cannot be efficiently and accurately mined from medical images, and the most valuable iconography features cannot be screened out for analyzing clinical information is solved.

A multimodal deep learning framework which is used to determine the likelihood of a particular treatment method effectively treating a patient with ovarian / kidney cancer with the goal of increasing patient survival. The framework takes into account not only large histopathology images (whole slide images), but also clinical variables to increase the scope of the data. The results demonstrate that the proposed models achieve high prediction accuracy and interpretability and can also be transferred to other cancer datasets without significant loss of performance. One of the key innovations here is the combination of pathology and clinical variables in a deep learning model to provide recommendations in therapy areas with limited information.

The invention discloses a colorectal cancer diagnostic marker group based on expiration volatile organic compounds and application of the colorectal cancer diagnostic marker group. The colorectal cancer diagnostic marker group consists of six expiration volatile organic compounds, namely furfural, 2-methyl butane, valeric acid, methyl allyl sulfide, hexane and octanal. According to the method, expiration samples of 182 subjects are analyzed through TD-GC-MS / MS, machine learning is combined, the six expiration volatile organic compounds are screened out, the diagnosis AUC of the combination in a verification queue reaches 0.907, the accuracy rate is 0.806, and the combination is superior to a serum marker combination (AUC = 0.861). The invention further provides the BRS and a colorectal cancer noninvasive diagnosis and risk stratification system, and the BRS and the colorectal cancer noninvasive diagnosis and risk stratification system are used for achieving efficient prediction and risk stratification of the colorectal cancer. Compared with a traditional risk scoring model depending on clinical variables such as age, family history and lifestyle, the BRS provides unique metabonomics information, and the prediction accuracy is remarkably improved.

The invention discloses a lung cancer patient prognosis evaluation method and system combining a pathological image and clinical variables, and the method comprises the steps: obtaining the pathological image of a target patient, carrying out the preprocessing and feature extraction, and integrating a plurality of clinical variables according to the identity information and daily habit information of the target patient; constructing a clinical feature network and an image feature network, and analyzing the extracted features through the image feature network to obtain a first analysis result; performing standardization and classification processing on the multiple clinical variables, and analyzing the processed multiple clinical variables through a clinical feature network to obtain a second analysis result; according to the first analysis result and the second analysis result, the survival month of the target patient is predicted through a multi-layer perception model. The lifetime prediction can be comprehensively carried out by considering the cooperative influence of clinical variables such as the tumor microenvironment and the patient smoking history, and the prediction precision and reliability can be ensured.

The application discloses a classification method and device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: segmenting tumor masks from enhanced CT images; for each tumor mask, generating a first peritumoral region and a second peritumoral region; dividing the tumor into multiple habitats for each tumor mask; extracting 3D radiomics features, multiple peritumoral microenvironment features, multiple tumor habitat features, multiple local pathological features and multiple global context features; selecting multiple target features for each enhanced CT image and calculating a radiomics score; inputting the radiomics score and clinical variables into a logistic regression model for training to obtain a trained logistic regression model; and using the trained logistic regression model to classify the enhanced CT images to be classified. The application can quickly and accurately realize G-grade classification of pancreatic neuroendocrine tumors, and provides a reliable non-invasive evaluation tool for clinical decision-making.

Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.

The invention discloses a model for early prediction of acute kidney injury risk after a sepsis patient is transferred into ICU based on clinical variables, 1551 sepsis patients are included in the research, clinical variables of the patients in the ICU within 24 hours are collected, five most important clinical variables related to acute kidney injury are screened out, XGBoost is adopted to construct a prediction model, and the risk of acute kidney injury is predicted. The prediction model is good in performance, an SHAP method is introduced to generate a feature importance map and an individual prediction interpretation map, the contribution degree of each index to risk prediction of a specific patient can be visually displayed, and the cause of SA-AKI of the patient can be explained, so that a doctor can take targeted measures preventively.

The application provides a liver cancer risk stratification and treatment recommendation information generation method and device, the method comprises the following steps: obtaining liver cancer related structured clinical data of a target object; based on a pre-set electronic medical record narrative template, converting the structured clinical data into an electronic medical record style narrative text containing clinical semantics, embedding key clinical variable markers corresponding to the structured clinical data into the electronic medical record style narrative text, and constructing a model input sequence; inputting the model input sequence into a pre-trained large language model for inference operation, outputting a structured text stream, and the large language model is obtained through decision tree constraint based on a liver cancer diagnosis and treatment guideline and multi-objective reinforcement learning strategy training; and extracting comprehensive decision assistance information from the structured text stream by using a pre-set analysis rule, realizing high-credibility clinical assistance decision, and significantly improving the accuracy and logical consistency of the treatment recommendation information.

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.

The invention discloses a premature infant retinopathy risk prediction method and system based on a Bayesian network. The method comprises the following steps: S1, data acquisition and grouping; s2, performing quality control and standardization processing on clinical variables, and preprocessing microbiome data; s3, based on a structure learning algorithm, constructing a directed acyclic graph Bayesian network model which jointly reflects a dependency relationship between clinical variables and microbiome variables, optimizing the structure in combination with clinical prior medical knowledge, generating a conditional probability table of each variable, and realizing multi-factor joint risk reasoning and interpretation; s4, model training and performance evaluation; and S5, inputting data, analyzing, outputting the risk probability of occurrence of ROP of the child patient, and visually displaying the risk probability. By integrating multi-dimensional clinical variables and intestinal microbiome characteristics, a Bayesian network model which is reasonable in structure, high in interpretability and high in prediction accuracy is constructed, and early layered prediction and individualized intervention suggestions on the retinopathy risk of the premature infant are achieved.

The application discloses a kind of multi-disease prediction systems based on binocular fundus image, including fundus feature extraction module, similarity-difference feature fusion module, clinical feature extraction module, multi-modal feature fusion module and multi-disease classification prediction module.System uses unified backbone network to extract left and right fundus image features, by similarity-difference feature fusion module, the features of two-way are added to each element to obtain preliminary fusion features, and the absolute value is obtained by subtracting each element to obtain different features, after normalization and 1 complement operation generates dissimilarity weight and similarity weight, separate out dissimilarity information and similarity information after weighting fusion according to hyperparameter α Fusion fundus feature is obtained;Multi-dimensional clinical variable features are extracted by two fully connected layers, after fundus features and clinical features are spliced, input multi-disease classification prediction module, and output disease prevalence probability.The application makes full use of the correlation and complementarity of binocular fundus image, and realizes high-precision, low-cost multi-disease screening combined with clinical indicators.

The invention discloses an AI sarcopenia long-term risk prediction system and method based on proteomics data. According to the method, a multi-step feature selection strategy is adopted, finally seven proteins such as IGFBP2, ACP5, LEP, FOLR3, OXT, PTPRZ1 and NEFL are locked to serve as core prediction factors, clinical variables (such as age, gender and BMI) are fused, and an individualized 10-year sarcopenia risk prediction model is trained and generated through machine learning methods such as integrated learning and cross validation. The model and the prediction system based on the model not only have high prediction accuracy and good model calibration degree, but also realize model interpretability through a Shapley value (SHAP) interpretation algorithm, can reveal association strength and action mechanism of each protein and sarcopenia risk, and provide scientific basis for pathogenesis research and accurate intervention of diseases.

Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.

The invention discloses a method for predicting heart failure based on left ventricular ejection fraction, which develops and trains a primitive model to automatically provide help and evaluate the level of clinical decision based on LVEF heart failure. The training model is prevented from using any or biased clinical variables, and the following two steps are ensured: investigating the statistical significance of each variable to distinguish the three categories; secondly, a novel dimension reduction technology is adopted to visually observe the characterization of the optimal variable in the radial direction, and each LVEF-based HF category is separated; and on the basis, the performance of the developed model is trained, the importance of the most important clinical variables is discussed, and the significance of the clinical variables is explained in detail based on the application of LVEF-based deep learning in HF analysis, so that patient data of three heart failure types are distinguished.

This invention discloses a method and system for pontine infarct segmentation and END prediction based on multimodal joint learning. The method includes acquiring and preprocessing multimodal data; constructing a wavelet transform-based feature encoding network to perform multi-scale decomposition and detail preservation of features; constructing a dual-task guided fusion module to align the deep semantics of clinical variables and imaging features and generate task-specific representations; constructing a Mamba-based global feature aggregation module to model sequence dependencies using a state-space model; constructing a multimodal second-order fusion classifier to enhance the clinical-image interaction modeling using second-order statistics; and employing a two-stage joint training strategy for training and prediction, and outputting the prediction results. This invention utilizes the DWT / IWT mechanism to significantly improve the accuracy of capturing small pontine infarct lesions; it achieves explicit interaction between segmentation evidence and prediction signals, significantly improving the segmentation accuracy of small lesions and the reliability of stroke risk assessment.

The invention belongs to the technical field of biological information processing, and particularly relates to a method and system for constructing a risk prediction model from Kawasaki disease to huge coronary artery tumor. The invention provides a Kawasaki disease MGCAA risk prediction model construction method and system, and the method comprises the steps: building a prediction model based on six conventional clinical variables through employing a random forest (RF ranger) algorithm; an SHAP explanation mechanism is introduced, so that the contribution of each variable to a prediction result can be visually displayed in global and individual levels, and the transparency and clinical interpretability of the model are improved; through external verification and intercept-only recalibration, the reliability and the applicability of the model in different groups of people can be ensured; according to the method, an online webpage tool (Shiny App) is deployed, and a doctor can input clinical data of a patient in real time and immediately obtain individualized risk prediction and explanation, so that clinical early recognition of an MGCAA high-risk child patient is realized, and formulation of an individualized treatment scheme and early intervention measures is assisted.

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.

This invention discloses an early ovarian cancer auxiliary diagnostic system based on multimodal fusion, belonging to the field of early ovarian cancer auxiliary diagnostic technology. It includes a data acquisition module, an image feature extraction module, a non-image feature extraction module, a multimodal fusion module, and a diagnostic output module. The data acquisition module acquires raw ultrasound images, serum biomarker test values, and clinical variable data; the image feature extraction module extracts image depth features; the non-image feature extraction module extracts non-image features; the multimodal fusion module dynamically calculates the attention weights of the two modalities and generates fused features; and the diagnostic output module outputs risk probability values. This invention overcomes the limitations of single-modal diagnosis by dynamically and adaptively fusing multimodal data such as ultrasound images, serum biomarkers, and clinical variables, significantly improving the accuracy of early ovarian cancer diagnosis, while also possessing interpretability and clinical deployment feasibility.

Compositions and methods are provided for diagnosis and / or prognosis of acute kidney injury risk following medical procedures in a subject. In some embodiments, the method includes measuring and comparing the level of particular proteins to other proteins. In other embodiments, the method includes measuring proteins levels with clinical variable information and comparing this composite with the composite of other protein levels with clinical variable information.

The invention relates to and discloses a radiation enteritis prevention method based on an AI model and image fusion, which aims at the difficulty in prediction and intervention of radiation enteritis after radiotherapy of abdominal and pelvic tumors, predicts to be incorporated into 100 radiotherapy patients through historical retrospective and prospective prediction, systematically collects multi-modal data, carries out image fusion on the multi-modal data, and carries out image fusion on the multi-modal data. Utilizing interpretable machine learning to fuse radiomics parameters, flora markers and clinical variables, constructing a radiation enteritis dynamic risk prediction model, and verifying the universality of the model through 200 historical queues; key flora / metabolic targets are further screened, a coprophilous fungi transplantation intervention scheme is designed, the regulation and control effect of coprophilous fungi is verified in 50 high-risk patients, an AI auxiliary decision-making system and a microecological preparation are finally developed, a full-chain management strategy of'image-omics prediction-targeted intervention-clinical transformation 'is formed, and an innovative solution is provided for precise prevention and control of radiotherapy complications.

The application discloses an adnexal mass benign and malignant risk assessment method based on exosome OCS and O-RADS combination. The biomarker combination provided by the application comprises ovarian cancer OCS score and imaging grading score, and the ovarian cancer OCS score is obtained based on the concentration of CA125 protein, HE4 protein and C5a protein in exosomes. The application first uses the OCS score in combination with the O-RADS imaging score, and by comprehensively combining the biological information and the imaging characteristics, the accurate diagnosis capability for the O-RADS 3 / 4 gray area patients is significantly improved, and unnecessary excessive medical treatment and missed diagnosis are reduced. The application also includes various clinical variables such as age, menopausal status and ultrasonic characteristics, and constructs a multi-dimensional combined diagnosis model, and further improves the diagnosis performance.

The present application relates to a kind of prognosis prediction method based on multi-modal intermediate fusion, which extracts pathological features from pathological images by deep convolutional neural network and pre-defined omics, and applies multi-instance learning to aggregate these features to form pathological representation, while using deep convolutional neural network and pre-defined omics to extract radiological features to form radiological representation, then using deep survival network to integrate pathological representation, radiological representation and clinical variables to generate multi-modal prognosis prediction score.Compared with prior art, the present application integrates the information of three modalities of pathology, radiology and clinic through deep survival network, significantly improves the prediction performance of traditional prognosis prediction indicators and single-modal model.

The invention relates to the technical field of medical information, and discloses a non-small cell lung cancer immunotherapy prognosis risk assessment method and system and a medium, and the method comprises the steps: obtaining clinical data including clinical variables, progression-free lifetime (PFS) and total lifetime (OS); taking each clinical variable as a hierarchical index to divide patient subgroups, and screening out hierarchical indexes with significant differences; then, aiming at each subgroup divided by the screened hierarchical indexes, quantifying prognosis influence of other clinical variables on the corresponding subgroup by adopting a risk regression model; and finally, screening out determinants with prognosis values according to a quantification result, dividing the determinants into different prognosis recommendation levels, and constructing a risk assessment matrix. According to the method, through a two-stage screening strategy of firstly identifying the effective hierarchical indexes and then analyzing the prognosis factors, evaluation deviation caused by generalization of local effective factors or neglecting of global non-significant factors is avoided, so that the accuracy and clinical practicability of prognosis evaluation are remarkably improved.

Embodiments include systems and methods for determining cardiovascular information for a patient. A method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data; and creating a computational model of a blood flow characteristic based on the anatomic model. The method also includes identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry; modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry; determining a blood flow characteristic within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and calculating, based on the probability model and the determined blood flow characteristic, a sensitivity of the determined fractional flow reserve to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

This invention discloses a risk stratification assessment model, device, and construction method for RUNX1::RUNX1T1-positive children with acute myeloid leukemia. The construction method includes: acquiring sample clinical data; assessing the correlation between clinical predictors and overall survival (OS) and event-free survival (ORS); for continuous variables, determining the optimal risk cutoff value for predicting poor prognosis; converting the continuous variables into binary variables; performing univariate Cox proportional hazards regression analysis to screen for factors significantly associated with OS and ORS; and incorporating these factors into a multivariate Cox proportional hazards regression model to confirm independent prognostic factors. This invention systematically integrates the clinical variable MRD1 and the percentage of peripheral blood blasts at diagnosis as core predictors, constructing a model capable of accurately assessing ORS. + A prognostic model for pAML risk was developed and rigorously validated. This model demonstrated superior predictive performance, effectively and accurately identifying patients with a high actual risk of relapse and death from the traditionally low-risk patient population.