61 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.

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.

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.

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.

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.

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.

The present invention provides a method for constructing an extubation outcome prediction model based on respiratory variability and machine learning. By introducing comprehensive respiratory indicators and complex, comprehensive statistical methods for physiological variability, a number of respiratory variability indicators are developed. Using feature engineering, multiple respiratory variability indicators are selected to train a machine learning model for extubation outcome prediction. The prediction system of the present invention is not limited to extubation prediction but has general applicability to other types of clinical decision-making support analysis. Compared with existing technologies, the prediction system proposed in the present invention utilizes respiratory variability indicators in a machine learning model, achieving a higher accuracy rate for extubation outcome prediction, and the method is highly interpretable.

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 an improved ResNet encoder, and the method comprises the following steps: collecting a chest CT image of an acute-stage lung disease; an improved ResNet encoder module used for extracting high-dimensional image features in the chest CT image is established through an input convolution layer, four residual blocks, an average pooling layer and a linear transformation layer; and outputting short-term, medium-term and long-term outcome probabilities based on the high-dimensional image features output by the improved ResNet encoder module by using a pre-established multi-stage outcome prediction model. According to the method, a plurality of classification heads are designed to be fused in a cascade mode to realize guidance of time sequence information, an improved cross-period fusion loss function based on Focal Loss is introduced, continuous constraint among prediction results in different periods is realized, and a multi-classification-head cascade structure and a feature extraction encoder structure of a loss cross-period fusion combination are combined, so that the prediction accuracy is improved. And therefore, the performance of the multi-period prediction model can be played to the optimum.

The invention relates to the technical field of medical treatment, and discloses a transfer learning method for bad outcome prediction of small-sample early-stage cancer patients. According to the method, the neural network risk function is introduced and the transfer learning framework is combined, so that the nonlinear interaction of high-dimensional features can be better fitted, and the prediction accuracy of the small-probability bad outcome of the early-stage tumor is improved. Although the existing RSF can overcome linear hypothesis to a certain extent, the existing RSF is susceptible to noise interference in a small sample queue, and the prediction stability is insufficient. According to the migration survival forest method provided by the invention, by training and migrating structure priori on a large-scale database, a stable tree structure can be kept under a small sample condition, and a split threshold is re-estimated in combination with target data, so that global structure knowledge and local adaptability are taken into consideration, and the reliability of the system is improved. Therefore, the robustness and generalization ability of the model are remarkably improved.

The invention relates to the technical field of artificial intelligence and medical image processing, in particular to a multi-stage regression prediction method based on convolutional neural network multi-classification head cascading, which comprises the following steps: setting a first classification head for predicting a short-term regression probability; setting a second classification head used for predicting a medium-term outcome probability; and setting a third classification head used for predicting the long-term outcome probability. According to the method, three classification heads corresponding to short-term, middle-term and long-term prediction tasks are designed, and classification intermediate features of a previous period are fused in a cascade mode to serve as auxiliary input of a next period, so that guidance of time sequence information is realized, and the constructed convolutional neural network can be used for realizing accurate prediction of a CT image in a single acute period through a CT image in a single acute period. And high-precision rotation probability of three time points can be output.

The invention discloses a blood fat-clinical outcome correlation analysis system and decision-making method oriented to an assisted reproduction and pregnancy period, and belongs to the field of medical information technology and intelligent diagnosis and treatment, and the method comprises the following steps: obtaining blood fat data and clinical baseline characteristics of a patient on an ovulation promotion starting day and a trigger day; adjusting a preset standard metabolic response curve by taking the clinical baseline characteristics as adjusting parameters based on blood fat data, and performing constraint fitting on the adjusted curve through parameter inversion to generate a personalized metabolic response curve and extract dynamic response characteristics; inputting the dynamic response features and the clinical baseline features into a risk-outcome prediction model to obtain a prediction result; and generating a clinical decision suggestion according to the prediction result and the decision rule. According to the method, discrete blood fat data are constructed into a continuous personalized metabolic response curve by adopting a method based on parameter inversion and constraint fitting, and dynamic response characteristics are extracted from the continuous personalized metabolic response curve for outcome prediction, so that the clinical outcome can be predicted more accurately, and a reliable basis is provided for personalized clinical decision making.

The present disclosure proposes an artificial intelligence (AI) based System (100) for preoperative assessment and surgical outcome prediction. The AI-based system (100) comprises one or more client modules collect data from electronic medical records (EMRs), Data repositories and Databases via a data collection module (122), then standardize and pre-process it with a standardization & pre-processing module (124) for integrity and compatibility. The pre-processed data is transmitted to a server (104) through a network(102) using an API module (126) and thereafter forwarded to an input module (112). The input module (112) authenticates and validates data of at least one patient. The data is subsequently sent to a processing module (114), and a prediction module (116) for predicting surgical duration and blood loss, and the extreme Gradient Boost model for postoperative patient placement predictions.

An intelligent system for optimizing radiotherapy for personalized radiotherapy, consisting of: • a genome data acquisition module configured to acquire and process a patient's genomic, molecular, and biomarker information; • a multimodal medical imaging module configured to acquire medical image data from one or more imaging modalities; • an image processing and automatic segmentation module configured to preprocess the acquired medical images, register multimodal images, segment tumors and organs at risk, and extract quantitative image features; • an artificial intelligence and data integration module configured to integrate genome data, imaging data, radiomic features, and clinical information to generate a patient-specific predictive model;• A treatment planning and dose optimization module configured to automatically generate and optimize a personalized treatment plan based on the integrated patient-specific model; • A digital twin and adaptive therapy module configured to simulate the patient-specific treatment response and continuously adjust the treatment plan during therapy; • A treatment monitoring and outcome prediction module configured to predict treatment response, disease progression, and radiation-induced toxicity based on longitudinal patient data; • A clinical decision support module configured to generate personalized treatment recommendations and support clinical decision-making;• A communication and data management module configured for the secure exchange, synchronization, and storage of clinical, imaging, genomic, and treatment-related information; and • An autonomous learning and systems management module configured to continuously improve predictive models and treatment optimization algorithms based on collected treatment outcomes, with the modules working together to generate, optimize, monitor, and continuously adapt personalized radiotherapy based on integrated genomic and multimodal medical imaging information.

The application provides a gastric low-grade intraepithelial neoplasia outcome evaluation method based on deep learning imageomics features, and relates to the field of medical information technology. The method comprises the following steps: acquiring a gastric medical image, extracting a lesion region of interest and expanding outward to construct an extended background region containing a surrounding mucosa microenvironment; extracting a high-dimensional imageomics feature set from the lesion region of interest; inputting the above two regions into a multi-branch convolutional neural network to extract deep semantic feature maps, and calculating the spatial feature difference to obtain an edge weight prior matrix; then constructing a spatial heterogeneity graph, and obtaining deep imageomics fusion features through collaborative updating of a graph attention network; finally, inputting the deep imageomics fusion features into an outcome prediction classifier to obtain a final outcome evaluation result. The application realizes deep fusion modeling of macro-microenvironment differences and micro-tissue heterogeneity, and significantly improves the accuracy of outcome prediction.

A Computer Aided Diagnosis, CADx, system and method for predicting an outcome score for a patient is described. The system comprising: an input circuit configured to receive input data comprising at least one input medical image for a patient; an outcome prediction circuit operably coupled to the input circuit configured to receive input data from the input circuit for outcome prediction analysis; wherein the outcome prediction circuit is further configured to receive details of a suggested future intervention for the patient; and the input data to the outcome prediction circuit is analysed to generate the outcome score for the patient accounting for the details of the future intervention.

The invention belongs to the field of legal artificial intelligence application, and particularly relates to a dispute result prediction system based on legal specification and fact matching, which is suitable for intelligent pre-judgment of judgment results and mediation schemes of litigation cases. A system architecture and a working process comprise small-premise structured input, large-premise intelligent matching, conclusion logic deduction, class case comparative analysis and document intelligent generation, and the system strictly follows a legal applicable logic structure. By introducing a similarity algorithm (such as a BERT legal semantic model), the fact-law-conclusion accurate mapping is realized, and it is ensured that different fact inputs inevitably correspond to differentiated prediction conclusions.