74 results about "Clinical variables" patented technology

The invention discloses a Nomogram clinical prediction method for postoperative skin flap vascular crisis of an oral and maxillofacial tumor patient, and the method comprises the following steps: analyzing an influence factor Meta, and forming a data collection table; matching logistic regression analysis, and adjusting a data collection table: collecting clinical data, and collecting related information of a patient; processing the collected information, and screening variables by adopting single factor analysis; a logistic regression algorithm is adopted to analyze the multi-factor field; constructing a risk prediction model, and performing internal and external verification; carrying out analysis modeling on the training group, and carrying out visual presentation through a Nomogram graph; a Bootstrp 1000-time re-sampling method is adopted to carry out replacement extraction on the model data, and internal verification is carried out on the development model; and drawing a clinical decision curve. The objective of the invention is to explore risk factors of vascular crisis after free tissue valve repair operation of oral and maxillofacial tumor patients from finer and simplified clinical variables and establish an efficient and rapid model to predict the risk of vascular crisis after operation.

The invention discloses a method for predicting progression-free lifetime of middle and advanced HCC patients after triple immunotherapy. The method comprises the following steps: constructing a personalized tumor variation map for sequencing data of tumor tissues and paired white blood cells of the middle and advanced HCC patients; calculating a gene risk score by using a first model based on tumor mutation information in the personalized tumor mutation map; calculating a total risk score by using a second model by taking the gene risk score and the clinical variable as independent variables; and carrying out risk stratification according to the cutoff value of the total risk score, and predicting the probability that the patient accepts the PFS at different times of triple immunotherapy. The method can effectively predict progression-free survival conditions of middle and advanced hepatocellular carcinoma patients after triple immunotherapy, so that potential beneficial patients are screened out.

Systems and methodologies for determining the likely cause of death of an individual from the individual's clinical history are disclosed. In particular, embodiments of the present disclosure relate to the processing of medical information through one or more algorithms, which can assess clinical and non-clinical variables to determine the probability that one or more medical or clinical factors in an individual's history is the precipitating cause of death, along with optionally identifying secondary potential causes of death and the assignment of a confidence variable, score and / or level indicating the calculated reliability of the assignment. The output data may be used, for example, in or with systems and methods with improved actuarial risk predictions and more accurate drug effectiveness and survival curves, among other applications.

Provided herein are methods, systems, and devices for integrated monitoring of patients, such as infants, within incubators. In an embodiment, an integrated monitoring system may include an integrated monitoring device communicatively coupled to a plurality of sensors. The integrated monitoring device may include a processor that executes machine-readable instructions to receive an environmental condition of an incubator and a clinical variable of the infant from the plurality of sensors. The processor may generate a predicted outcome based on application of the environmental condition and the clinical variable to a model configured to associate sensor data with clinical outcomes. In response to the predicted outcome being outside a threshold, the processor may output a notification to a user device communicatively coupled to the integrated monitoring device.

System, methods, and non-transitory computer readable medium (CRM) are provided that are directed to a biological age model utilizing metabolomic and proteomic data. Additional clinical variables and DNA methylation data may also be delivered to the methods, systems, and CRM. The biological age model determines a morbidity parameter based on a comparison between a patient's determined biological age and their chronological age to determine a time until death and possible treatment strategies.

The invention relates to a prognosis prediction method based on multi-modal intermediate fusion, and the method comprises the steps: extracting pathological features from a pathological image through a deep convolutional neural network and predefined omics, and carrying out the aggregation of the features through multi-instance learning, so as to form a pathological representation, the method comprises the following steps: extracting radioactive features by using a deep convolutional neural network and predefined omics to form a radiology representation, and then integrating a pathological representation, the radiology representation and clinical variables by using a deep survival network to generate a multi-modal prognosis prediction score. Compared with the prior art, information of three modals of pathology, radiology and clinic is integrated through the deep survival network, and traditional prognosis prediction indexes and prediction performance of a single-modal model are remarkably improved.

Computer-implemented methods of providing a clinical predictor tool are described, comprising: obtaining training data comprising, for each of a plurality of patients, values for a plurality of clinical variables comprising a variable indicative of a diagnosis or prognosis and one or more further clinical variables; and training a clinical predictor model to predict the variable indicative of a diagnosis or prognosis using said training data, wherein obtaining the training data comprises obtaining synthetic clinical data comprising values for a plurality of clinical variables for one or more patients by obtaining a directed acyclic graph (DAG) edges corresponding to conditional dependence relationships inferred from real clinical data comprising values for the plurality of clinical variables for a plurality of patients, and obtaining values for each node of the DAG using a machine learning model and multivariate conditional probability table. Computer-implemented methods of obtaining synthetic clinical data are also described.

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 relates to a new crown growing typing method and device, a molecular subtype and clinical variable association method and device and electronic equipment, and relates to the technical field of new crown growing typing. The typing method comprises the following steps: acquiring a gene expression matrix of a to-be-detected sample; and processing the gene expression matrix of a to-be-detected sample by adopting the new crown growing typing model to obtain a typing result of the to-be-detected sample. The association method comprises the following steps: acquiring a gene expression matrix and a typing result of an experimental sample and a gene expression matrix of a control sample; respectively carrying out differential gene analysis on the experimental samples of each molecular subtype and a control sample to obtain significant differential genes; performing functional pathway enrichment analysis, and calculating a gene set expression average value of the functional pathways with significant difference; and carrying out correlation analysis after normalizing the gene set expression average value and clinical variable data to obtain clinical variables associated with each molecular subtype.

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.

Provided herein are methods for predicting chemotherapy benefit. The invention predicts chemotherapy benefit based on the expression analysis of biomarkers, e.g., RNA biomarker transcription analysis, taken from a tumor sample. The biomarker expression data can be combined with clinical variables, e.g., tumor size and nodal status, to generate a profile that predicts the benefit of including chemotherapy as a treatment decision.

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.

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.

Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

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.

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 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.

To provide diagnostic and prognostic methods for cardiovascular diseases and events.SOLUTION: Compositions and methods are provided for diagnosis and / or prognosis of cardiovascular diseases or events in a subject. In some embodiments, the method includes measuring and comparing the levels of particular proteins to other proteins. In other embodiments, the method includes comparison with clinical variable information. Diagnosis or prognosis provided by the methods of the present disclosure are particularly important in defining and determining a therapeutic path forward for a patient receiving a positive diagnosis and / or positive prognosis of coronary artery disease, including cardiovascular death, myocardial infarct (MI), stroke, all cause death, or a composite thereof.SELECTED DRAWING: Figure 1

It is provided a method of determining the risk of developing Alzheimer's disease dementia in a subject, comprising: (a) determining in a sample of the subject comprising mitochondrial DNA, the methylation pattern in the D-loop region, and / or in the ND1 gene of the mitochondrial DNA; and (b) combining the methylation pattern of one or more sites determined in step (a), with at least one clinical variable of the subject, wherein said combining is performed using a classification model for determining a risk score which correlates to the risk of developing Alzheimer's disease dementia in the subject. A classification model, oligonucleotides, and kits to perform the method, are also provided.

Provided herein are methods of training and building algorithms to predict stroke etiology. The method of training an algorithm to predict stroke etiology includes processing electronic health records (EHRs) in a derivation dataset, the processing including extracting clinical variables from the EHRs by detecting concept unique identifiers (CUIs) through natural language processing (NLP), and extracting other covariates from the EHRs through regular expression, wherein the clinical variables and other covariates form a training dataset; training two or more base models with the training set to form two or more trained base models, the base models selected from the group including Random Forests (RF), XGBoost (XGB), support vector classifier (SVC), and logistic regression (LR); refining the two or more trained base models using hyperparameters to form two or more refined base models; and building an ensemble model with the two or more refined base models. Also provided herein are articles and methods of predicting ischemic stroke etiology using the trained algorithm.

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.