85 results about "Outcome prediction" patented technology

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.

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.

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.

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.

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 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.

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.

The application provides a kind of legal judgment result prediction intelligent feedback error correction method and electronic equipment, including the secretary intelligent agent obtained by the training of large language model, the prosecution intelligent agent, the defense intelligent agent and the judge intelligent agent of sentencing, method includes: through secretary intelligent agent, event-level disassembly and extraction are carried out to case fact text, and fact atom set is obtained;Through the prosecution intelligent agent, the charge set is generated based on fact atom set, and through the defense intelligent agent, the refutation set is generated based on fact atom set;Through the judge intelligent agent of sentencing, the charge set and the refutation set are carried out to confrontation type collaborative reasoning, and initial sentencing result is obtained;The initial sentencing result is checked, if there is doubt charge, then the prosecution intelligent agent and the defense intelligent agent are used to generate correction content for doubt charge;Through the judge intelligent agent of sentencing, the final sentencing result is generated according to the correction content.Can assist judicial organ to realize more stable, interpretable and efficient legal judgment prediction.

A mechanical ventilation device comprising at least one electronic controller is configured to receive images of lungs of a patient undergoing mechanical ventilation therapy with a mechanical ventilator, the images being acquired over time and having timestamps; process the images to generate timeline images at corresponding discrete time points; and display a timeline of the timeline images on a display device.

The present invention relates to the technical field of legal judgment, and specifically to an encoder training method, judgment prediction method, and device for legal judgment. The present invention constructs a sample case triple, wherein the sample dissimilar cases in the sample case triple that are dissimilar to the sample original case are high-frequency cases. The encoder is trained by the sample case triple. Since the sample dissimilar cases are high-frequency cases, during the encoder training process, the intra-class distinction of high-frequency cases and the inter-class distinction of low-frequency cases can be constrained, thereby weakening the influence of high-frequency cases on the encoder, thereby improving the accuracy of the encoder in encoding the case, and on the basis of accurate encoding, further improving the accuracy of the prediction of the case judgment result. In addition, since the present invention constructs the case triple based on the legal provisions and the crime, the legal provisions and the crime are introduced into the case triple, which realizes the refinement of the encoder, thereby improving the accuracy of the prediction of the case judgment result.

The embodiments of this specification disclose a method, device, storage medium, and electronic device for training a result prediction model. The method first trains a fusion model based on first-sample training data, then obtains counterfactual results based on the trained fusion model, constructs unbiased augmented signals based on the first-sample training data and the outputs of the trained fusion model for each decision item, and then trains the result prediction model based on the unbiased augmented signals. Thus, by combining a regression model and a propensity score model for multi-task representation learning and fusing multiple prediction models to share parameters to reduce variance, it can effectively solve the data bias problem and improve the training efficiency and prediction accuracy.

System and methods for determining high-impact, non-informative features in datasets can include obtaining a first dataset including a first set of features and first set of tags, determining a second dataset including a second set of features and the first set of tags, the second set of features transformed from the first set of features, and each of the second set of features including a binary value, predicting, by a model, a third dataset including a third set of features and second set of tags based on the second dataset, comparing the third dataset to the second dataset to determine a correlation therebetween, based on the comparison, filtering at least one feature having missing feature values from the first dataset, and determining a training dataset with the remaining features in the first dataset, wherein the model being trained using the training dataset improves the outcome prediction accuracy of the model.

The invention relates to a law decision result prediction method and system, and the method comprises the steps: constructing an intellectual property case keyword dictionary, and obtaining a law text of an intellectual property related case from a first public database through the intellectual property case keyword dictionary; obtaining enterprise economic data corresponding to each legal text from a second public database; constructing a structured topic extraction model, and training the structured topic extraction model by using the legal text and the enterprise economic data; constructing a legal judgment result prediction model, and training the legal judgment result prediction model by using a topic distribution result output by the trained structured topic extraction model; and obtaining a legal text and enterprise economic data of a to-be-predicted object, inputting the legal text and the enterprise economic data into the trained structured topic extraction model to obtain a topic distribution result, and inputting the topic distribution result into the trained legal judgment result prediction model to perform legal judgment result prediction. According to the method, the prediction accuracy and the interpretability of the legal judgment result of the legal text of the intellectual property case can be remarkably improved.

The invention relates to the technical field of machine learning and disease prediction, in particular to a KAN-based necrotizing enterocolitis outcome prediction model construction method. According to the method, firstly, the key variables related to the necrotizing enterocolitis outcome are screened, the necrotizing enterocolitis outcome can be predicted through the key variables, and the modeling complexity is reduced; secondly, a KAN is used for constructing an end prediction model, complex relations in data can be captured, and good performance and beforehand interpretability are achieved;

An online system receives orders from users and dispatches pickers to fulfill the orders by obtaining ordered items at a retailer. If an ordered item cannot be found by a picker, the picker may refund the item or attempt to find a replacement item. While obtaining a replacement item may increase revenue to the online system, it can also cause a bad outcome for user experience (e.g., an unacceptable replacement item, a refund request of the replacement item, etc.). To balance these interests, the online system trains a model to predict an outcome metric comprising a likelihood of a bad outcome from replacing an item or an expected amount of profit to the online system from a replacement item. The online system compares the outcome metric to a threshold to determine whether to promote or dissuade the picker from replacing a not-found item.

A disease prognosis model is provided that can be trained to determine a clinical outcome of a disease based on longitudinal data including health records for each point in time in a sequence of points in time. The disease prognosis model may include a recurrent neural network trained to extract, from each health record, a set of features representing local dependencies present within the health record. The disease prognosis model may include spatio-temporal attention trained to determine an importance of each feature in the set of features at each point in time in the sequence of points in time. The disease prognosis model may include a feed-forward neural network trained to determine the clinical outcome of the disease based on the importance of each feature in the set of features at each point in time in the sequence of points in time. A trained disease prognosis model may be applied to determine the clinical outcome of the disease for one or more patients.