105 results about "Outcome prediction" patented technology

The embodiment of the invention provides a legal affair information processing method and system fusing a knowledge graph and a large language model, and belongs to the technical field of knowledge graphs. The method comprises the steps that existing information related to the legal field is collected, and data preprocessing is conducted on the existing information; entity recognition and relation extraction are carried out based on the preprocessed existing information, and a knowledge graph is constructed based on the relation between the entities; collecting a legal affair information processing instruction of a user, and performing user intention recognition based on the legal affair information processing instruction; and based on the identification intention and the legal affair information processing instruction, generating result prediction information through the knowledge graph, and pushing the result prediction information to a user side. According to the scheme, deep analysis and intelligent processing of legal problems can be achieved, the accuracy of information retrieval and the reliability of legal suggestions are improved, and therefore decision making of legal practitioners and legal problem solving of users are better supported.

The invention relates to the technical field of computers, and discloses a legal case strategy analysis method and system, electronic equipment and a storage medium, and the method comprises the steps: analyzing a to-be-analyzed legal case, extracting key information, and constructing a dynamic affair graph based on the key information; matching with a historical case database by adopting a multi-dimensional class case retrieval method on the basis of the dynamic event atlas, and retrieving similar historical cases; based on the judgment result information of the similar historical cases, performing analysis in combination with key information or a dynamic affair map of the to-be-analyzed legal case, and generating judgment result prediction of the to-be-analyzed legal case; and fusing the dynamic affair atlas, similar historical cases and judgment result prediction, calling a large language model adjusted by a law field instruction through a retrieval enhancement generation architecture, and generating a litigation policy analysis report with a preset structure part. According to the method, the automation degree and depth of case information processing are improved, and closed-loop generation from analysis to strategy is realized.

Various systems and methods for generating a health outcome prediction for a patient are disclosed herein. The embodiments described involve operating a processor to receive a health condition of interest, receive a health dataset including patient data associated with the patient that includes demographic information about the patient, and clinical information about the patient. At least a portion of the health dataset is unstructured data and the health dataset originates from distinct data sources. The described embodiments then involve operating the processor to process the unstructured data to obtain structured data, extracting from the processed health dataset, features determined to be relevant to the health condition of interest, determine a risk score for the patient for the health condition of interest using a trained health prediction engine, based on the features extracted and generate and display a graphical output based on the risk score.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to predicting an optimized result for a graph neural network (GNN). A system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components comprise: a fusion component that that models non-linear modality correlations within and across entities through Hirschfeld-Gebelein-Re'nyi maximal correlation (MaxCorr) embeddings that generates a multi-graph that preserves identities of modalities and entities; and a multi-graph neural network (MGNN) component for task-informed reasoning in multi-graphs, that learns parameters defining entity-modality graph connectivity and message passing in an end-to-end fashion.

A method (100) for recommending a patient treatment comprising: (i) receiving (120) information about the patient, wherein the information comprises a plurality of patient outcome prediction features; (ii) extracting (130) the plurality of patient outcome prediction features from the received information; (iii) analyzing (140), using a trained time-varying treatment effect model, the plurality of patient outcome prediction features to predict a plurality of different outcomes for the patient, wherein each of the plurality of different outcomes is associated with a patient treatment leading to said respective outcome; (iv) identifying (150) at least one of the plurality of different outcomes and treatment as a recommended outcome and associated treatment for the patient, wherein identifying comprises identification of an outcome and associated treatment that maximizes favorable results for two or more organ systems for the patient; and (v) providing (160) the recommended outcome and associated treatment for the patient to a user.

The invention relates to a body weight dynamic-based nutrition state outcome prediction model construction method and system, and belongs to the field of medical informatization. The method comprises the following steps: firstly, establishing a crowd data set containing individual weight information and nutrition state data, and carrying out data preprocessing, including calculating a weight loss percentage and defining an end label; then, the data set is divided into a training set and a test set, and key features related to nutrition state outcome are extracted from the training set through feature screening; and based on the screened features and the training set, constructing a plurality of machine learning classifiers, and selecting an optimal model according to performance indexes of the classifiers. And finally, predicting the test set by using the optimal model, and evaluating the performance of the model. According to the method, the weight dynamic information is utilized, other information assistance is not needed, high-performance prediction of common bad nutrition state outcome can be achieved, and the method has high practical value.

Provided are techniques including receiving patient data; generating, based on the patient data, a patient risk stratification including: generating stratification scoring based on the patient data; and determining, based on the stratification scoring, a binary classification; generating, based on the patient data, a patient risk level assignment including: generating risk level scoring based on the stratification scoring and the binary classification; and determining, based on the risk level scoring, a risk category; generating a set of patient next best actions including: determining, based on the patient data, a patient outcome prediction; and generating, based on the predictions of patient outcomes, the set of patient next best actions; generating a patient disease state transition prediction including: determining, based on the patient data, a set of transition probabilities; generating, a patient unknown identification prediction including: determining, based on the patient data, a disease propensity score; and generating a corresponding patient diagnosis report.

The invention relates to a fusion prediction and interpretable report generation system for multi-modal medical data, and the system comprises a data access and preprocessing module which is used for processing at least two kinds of heterogeneous modal data from a target object; the modal feature encoding module comprises a plurality of encoders corresponding to modal types and is used for extracting feature representation of each modal data; the outcome prediction module is used for outputting an outcome prediction result of each mode; the missing modal fusion module is used for dynamically adjusting a fusion weight based on a modal existence vector when at least one modal data is missing so as to realize robust prediction; and the multi-agent interpretation module comprises a plurality of interpretation sub-modules corresponding to the modals and a language model convergence device, and is used for generating an interpretable natural language report based on the multi-modal evidence. According to the method, the problems that an existing prediction technology is unavailable under the condition of lack of modals, the fusion capability is insufficient and clinical interpretability is lacked are solved, and a stable and interpretable auxiliary decision-making tool is provided.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be used to generate citations for the outcome predictions to provide references to previous cases (e.g., historic data) from which the outcome predictions are based.

Provided are techniques including receiving patient data; generating, based on the patient data, a patient risk stratification including: generating stratification scoring based on the patient data; and determining, based on the stratification scoring, a binary classification; generating, based on the patient data, a patient risk level assignment including: generating risk level scoring based on the stratification scoring and the binary classification; and determining, based on the risk level scoring, a risk category; generating a set of patient next best actions including: determining, based on the patient data, a patient outcome prediction; and generating, based on the predictions of patient outcomes, the set of patient next best actions; generating a patient disease state transition prediction including: determining, based on the patient data, a set of transition probabilities; generating, a patient unknown identification prediction including: determining, based on the patient data, a disease propensity score; and generating a corresponding patient diagnosis report.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

A method of determining the risk of developing a known disease or condition or of identifying the presence of the known disease or condition in a subject includes obtaining subject data that includes results of blood tests. The blood tests include a basic metabolic panel (BMP) and a complete blood count (CBC) panel. The method further includes classifying the subject data with respect to the risk of the subject having or developing the known disease or condition by using the subject data in a machine learning classification system. The classification system includes a machine learning model previously trained on BMP and CBC data from a positive group of training subjects who received a diagnosis of the disease or condition and from a negative group of training subjects who were not diagnosed to have the disease or condition.

Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.

The invention relates to an individual future health state prediction method and device based on proteomics, and a medium. The method comprises the following steps: obtaining and preprocessing proteomics data; constructing a shared network: constructing an individual past and future common disease condition prediction neural network based on a twin network framework; backbone network construction: constructing a health specific outcome prediction neural network based on a multilayer perceptron method; and the health assessment network synthesizes the shared network and the backbone network architecture, carries out fusion and fine tuning on the extracted features in a potential space, and outputs the future risk assessment probability of various health specific outcomes through further training, updating and fine tuning of network parameters. Compared with the prior art, the method focuses on a proteomics data processing and modeling method, takes past and future health estimation as prior information, and realizes individualized health condition evaluation with multiple disease species and death as the outcome.

A system and method for determining a health outcome, comprising: receiving first and second images or videos of a wound of a patient; comparing the images or videos to detect a characteristic of the wound, the characteristic including an identification of a change in the wound; receiving at least one non-image or non-video data input that includes data about the patient; executing a machine learning algorithm comprising a dataset of images or videos to analyze the identified change in the wound and to correlate at least one first image or video and at least one second image or video with the at least one non-image or non-video data input and to train the machine learning algorithm with the identification of a change in the wound; and generating a medical outcome prediction regarding a status and recovery of the patient in response to correlating the at least one additional input with the first and second images or videos.

The invention relates to a depression disease management system based on targeted metabonomics and a machine learning model, and the system is characterized in that the system comprises an intelligent diagnosis model module, a differential diagnosis model module, a grading diagnosis model module, an accompanying diagnosis model module, a treatment endpoint outcome prediction model module, a recurrence prediction model module, and a comprehensive judgment module. Through the system provided by the invention, the problems in the fields of current clinical depression diagnosis, treatment outcome, relapse and the like can be solved, accurate diagnosis and timely treatment are realized, and the mental health level of the whole people is improved.

The present disclosure relates to a method. The method may be performed by accessing data derived from one or more routine clinical medical imaging scans including a lesion in which the lesion and associated vasculature are segmented in a three-dimensional segmentation. At least two features are extracted from the three-dimensional segmentation of the associated vasculature. The at least two features include at least one feature indicative of a morphology of the associated vasculature or a portion thereof, and at least one feature indicative of a function of the associated vasculature or a portion thereof. The at least two features, and / or one or more statistics of the at least two features, are provided to a machine learning model trained to make a prediction concerning the lesion. The prediction concerning the lesion is received from the machine learning model.

The invention discloses a Crohn disease postoperative bad outcome prediction method combining knowledge base construction and an experience-driven self-asking mechanism, and solves the problems of insufficient interpretability, insufficient clinical data utilization, weak generalization ability and the like of an existing prediction method. The method comprises the following steps: constructing a Crohn disease special medical knowledge base, extracting disease progress related factors from medical literatures and real medical records, and carrying out expert score weighting processing to form searchable knowledge entries; retrieving related knowledge injection context from a knowledge base based on a medical record input by a user, and generating preliminary prediction by using a large language model; structured reasoning is guided through a multi-layer prompt engine, diagnosis tasks are decomposed through the thinking chain technology, and self-check before reasoning is achieved by driving a self-asking mechanism through experience. And generating an introspection problem chain according to historical error cases, iteratively optimizing the reasoning process, and finally outputting a bad outcome prediction result and a detailed analysis report. According to the method, both traceable interpretability and prediction accuracy are considered, and reliable assistance is provided for postoperative clinical management of Crohn's disease.

The invention relates to the technical field of medical data analysis, in particular to a hepatic fibrosis outcome prediction and intervention recommendation method based on dynamic data, which comprises the following steps: collecting biological samples and clinical index data from a target individual at a plurality of continuous time points, and constructing a multi-modal time sequence data set; processing the data set to extract dynamic features reflecting a change process, wherein the dynamic features comprise trend features and event features; inputting the baseline features and the dynamic features of the target individual into a pre-trained hepatic fibrosis outcome prediction model, and outputting hepatic fibrosis index prediction values at a plurality of time points in the future to form a prediction outcome trajectory; when the trajectory meets an early warning condition, matching candidate donors from a pre-constructed donor feature library and generating an intervention recommendation list based on the current state and trajectory features of the target individual; and when new time point data is obtained, updating the data set and repeating the process. According to the method, dynamic and quantitative prediction of the hepatic fibrosis outcome trajectory is realized, and data-driven personalized intervention recommendation is provided.

A system and method for generation and use of radiation outcome prediction (response & side effects) score for patients undergoing radiotherapy for various medical conditions wherein the score is a personalized score, provided by analyzing multiple parameters including the tumor specific, patient specific, gene specific and treatment planning specific parameter(s), during and post therapy.

The present application relates to a kind of individual future health state prediction method, device and medium based on proteomics, wherein the method comprises the following steps: obtaining proteomics data and preprocessing;Shared network construction: construct individual past and future comorbidity condition prediction neural network based on twin network framework;Trunk network construction: construct health-specific outcome prediction neural network based on multilayer perceptron method;Health assessment network integrates the shared network and trunk network architecture, and extracts features thereof to fuse and fine-tune in latent space, updates fine-tuning network parameters by further training, and outputs the risk assessment probability of multiple health-specific outcomes in the future.Compared with prior art, the present application focuses on proteomics data processing and modeling method, uses past and future health estimates as prior information, and realizes individual health condition assessment with multiple diseases and death as outcome.

The present invention relates to a depression disease management system based on targeted metabolomics and machine learning models. The system comprises an intelligent diagnostic model module, a differential diagnostic model module, a hierarchical diagnostic model module, a companion diagnostic model module, a treatment endpoint outcome prediction model module, a relapse prediction model module, and a comprehensive judgment module. The system provided by the present invention can address current clinical challenges in depression diagnosis, treatment outcomes, and relapse, enabling accurate diagnosis, timely treatment, and improving the mental health of the general population.

The present application relates to the medical technical field, specifically relates to a kind of early postoperative adverse outcome prediction method for craniopharyngioma patient, comprising: extracting preoperative data and label data of patient, store in database;The data in database is preprocessed, is randomly divided into training set and verification set according to preset proportion, and the training set is handled using the minority class oversampling technique, obtain the training set after processing;From the training set after processing, the most predictive value feature subset is identified and screened;Predictive model is constructed;The predictive model is evaluated and compared, and the best model is selected as the final deployment model;Receive new preoperative data of patient, input final deployment model, output prediction result.The present application fills the blank of existing prediction craniopharyngioma postoperative early overall adverse outcome model, overcomes the single data processing method in existing prediction technology, feature selection is not accurate enough, prediction accuracy is not high and lacks clinical usability and other problems.

The invention relates to the technical field of biology, in particular to 5-hydroxymethylcytosine and application of a prognosis model of 5-hydroxymethylcytosine in nasopharynx cancer prognosis evaluation. According to the method, the prognosis scoring model capable of layering the outcome of the patient is constructed, and the model shows strong distinguishing performance in a training set and a verification set. When it is integrated into a prognostic model together with tumor staging and EBV status, the 5hmC score results in higher calibration accuracy and net clinical benefit in decision curve analysis. According to the findings, a 5hmC prognosis model is determined as a noninvasive marker rich in biological information, can be used for prognosis risk layering of NPC patients, and has potential significance for individualized management and improvement of outcome prediction.

A system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis driven simulations of alternate candidate business actions has been devised and reduced to practice. This business operating system may be used predict the outcome of enacting candidate business decisions based upon past and current business data retrieved from both within the corporation and from a plurality of external sources pre-programmed into the system. Both single parameter set and multiple parameter set analyses are supported. Risk to value estimates of candidate decisions are also calculated.

The invention relates to an illness condition outcome prediction method and device based on a gating adaptive mechanism. The method comprises the following steps: acquiring chronic disease related data, performing feature correlation analysis according to the chronic disease related data, generating graph data, and performing feature enhancement on the graph data; based on the graph data, utilizing a multi-layer GAT hybrid model to carry out prediction, and obtaining a time sequence of a transition state; wherein the multi-layer GAT hybrid model comprises a plurality of GATConv layers, a plurality of LSTMAggregation layers and a feature fusion layer based on gating self-adaption; and outputting the time sequence of the conversion state as a conversion matrix by using a Grubm angle field. According to the method, graph-level global feature fusion is carried out through a gating mechanism and an attention mechanism, so that the multi-layer GAT hybrid model has a strong graph feature extraction capability, and the precision and accuracy of power prediction can be effectively improved. Meanwhile, the multi-layer GAT hybrid model is used for carrying out outcome prediction on each data point of each patient, and then a Gream angle field is adopted to generate an outcome matrix, so that further outcome analysis is facilitated.

Techniques for intelligently managing service requests using a service request outcome prediction and a dynamically determined probability threshold are disclosed. In one embodiment, a computer-implemented method is disclosed comprising receiving a request for service directed to an online service provider, determining a feature vector for the received service request, the feature vector determination comprising identifying information associated with the request and a response of the service provider, the feature vector being based on the identified information, analyzing the received request using a trained outcome prediction model and the feature vector, and determining a win probability based on the analysis, the win probability indicating a likelihood of a predefined outcome in connection with the service request and the service provider's response, making a request throttling determination based on the win probability and a threshold probability, and managing the service request based on the request throttling determination.

The invention relates to the technical field of artificial intelligence and medical image processing, in particular to a multi-stage outcome prediction method based on multi-classification head cascading and inter-stage fusion loss, which comprises the following steps of: establishing a multi-stage outcome prediction model consisting of an encoder and a multi-classification head cascading module, a cross-period fusion loss function is designed based on Focal loss and is used for constraining the consistency of multi-period prediction; performing end-to-end training on the multi-period outcome prediction model by adopting an Adam optimizer to obtain an optimal multi-period outcome prediction model; and outputting short-term, middle-term and long-term outcome probabilities of the patient by using the optimal multi-stage outcome prediction model. According to the method, the multi-classification head and the cross-period fusion loss are combined, and the classification intermediate features of the previous period are fused in a cascade mode to serve as the auxiliary input of the next period, so that the constructed multi-period outcome prediction model can output the high-precision outcome probabilities of three time points through the single acute-period CT image.

The invention relates to the technical field of artificial intelligence and psychological health assessment, and particularly discloses a psychological health longitudinal tracking and effect prediction method based on a time sequence, which reflects the change process of the psychological state of an individual by utilizing longitudinal time sequence data in data acquisition and processing, and predicts the psychological state of the individual. In result prediction, future psychological health trend prediction is realized through a time sequence model, accuracy of diagnosis and intervention evaluation is improved by fusing subjectivity such as scale, inquiry and objectivity such as cognitive test, physiological signal data and past examination reports, and the method is suitable for various application scenes such as clinical follow-up visit, school psychological health management and online psychological counseling platforms.