136 results about "Outcome data" patented technology

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance, and discloses a credit evaluation method and device for multi-source data co-processing and related equipment. The method comprises the steps of receiving credit evaluation data of at least two types of preset data sources, and performing cleaning, time scale unification and numerical standardization processing on the credit evaluation data to form standardized multi-source feature data; constructing credit portrait data based on the standardized multi-source feature data; performing scoring reasoning on the credit portrait data through a pre-trained credit extension evaluation model, and generating credit extension evaluation result data through a limit mapping rule; receiving the use behavior data and the performance behavior data, performing time sequence analysis and deviation identification, and generating credit use behavior analysis data; and updating credit extension evaluation model parameters based on the credit use behavior analysis data, loading the credit extension evaluation model parameters to a model operation state, outputting credit extension evaluation result data, and recording model parameter updating information. According to the method, the dynamic adaptive capacity of multi-source credit assessment is improved.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

The invention discloses a rock burst intelligent early warning and decision making system and method based on large language model driving. The system is characterized in that a data acquisition module, a data preprocessing module, a domain self-adaption large model module and a multi-agent early warning and decision making module are connected in sequence; the multi-agent early-warning and decision-making module comprises five agents including a seismic source mechanism intelligent analysis early-warning agent, an impact danger early-warning analysis daily report, a rock burst knowledge question and answer agent, a rock burst risk special event analysis agent and a rock burst early-warning and prevention and control auxiliary decision-making agent, and a knowledge database; and the user input module is connected with the rock burst knowledge question-answer agent. The method comprises the following steps: collecting multi-source monitoring data; extracting feature data; carrying out deep semantic understanding and professional reasoning by utilizing a domain self-adaptive large model module; and based on the input data, the multi-agent early warning and decision module outputs result data through a multi-agent communication and negotiation mechanism. According to the invention, accurate early warning and intelligent decision-making of the rock burst risk can be realized.

The invention relates to an intelligent control method and system based on multi-protocol task scheduling and a medium. The intelligent control method comprises the steps that original instruction data input by a user is received and subjected to semantic analysis, instruction keywords are extracted, and a structured instruction data packet is generated; converting the structured instruction data packet into a multi-protocol connection service calling parameter, and sending the multi-protocol connection service calling parameter to a multi-protocol connection service processing core through an internal communication interface; the multi-protocol connection service processing core schedules a corresponding execution unit to execute the operation instruction set according to the target system identifier, and generates operation result data corresponding to the task type identifier; performing state analysis on the operation result data to generate decision instruction data containing a decision type identifier; and executing task refinement processing, abnormal alternative scheme generation or task completion feedback processing based on the decision type identifier, and converting a processing result into natural language feedback output. According to the invention, unified coordination and autonomous operation of the heterogeneous intelligent system in the home environment are realized.

One or more instruments generate test result data. The test result data include patient data and QC data. The test result data is provided to a QC data flow system via a local network, which filters the test result data (e.g., using a set of rules) to extract the QC data. The QC data is provided to a cloud-based QC data management platform via an external network. The cloud-based QC data management platform analyzes the QC data and provides a result back to the QC data flow system. The QC data flow system forwards the result to middleware or the instrument, which triggers a corrective action based on the result as appropriate.WO

The application discloses a digital thread driven intelligent management system for a clinical laboratory, which comprises a system body, and the system body is operated through the following method, and the specific method comprises the following steps: obtaining patient historical records and same batch sample data from a pre-established database by collecting current test result data, and obtaining multimodal information containing original images, detection curves and trend deviations; determining potential abnormal patterns by processing the original images and the detection curves through an abnormality detection algorithm according to the multimodal information; if the abnormal patterns are inconsistent with historical comparison results, comparing previous results of the patient through a trend analysis algorithm to judge the trend deviation degree; obtaining critical project specific threshold values according to the trend deviation degree to obtain key clue recognition results; and the application aims to solve the problem that in the prior art, when facing critical value projects, comprehensive verification and rapid release cannot be balanced, resulting in poor quality control and efficiency of the clinical laboratory.

The invention provides a clinical data-oriented stepped asynchronous acquisition system and method, a medium, a program product and a terminal. The system comprises a task acquisition module, a multi-stage analysis module and a data acquisition module, the method comprises the following steps: acquiring main index information of a current patient, and generating a doctor seeing acquisition task based on the main index information; obtaining corresponding doctor-seeing information based on the doctor-seeing collection task, generating a doctor's advice collection task based on the doctor-seeing information and the main index information, obtaining corresponding doctor's advice information based on the doctor's advice collection task, and generating a data collection task based on the doctor's advice information; and generating a corresponding time window parameter for the data acquisition task based on a preset time window rule, and executing the data acquisition task based on the time window parameter to obtain clinical result data of the current patient. And a set of stepped asynchronous incremental acquisition mechanism taking a time window and state driving as a core is constructed, so that the continuous pressure on a source system is reduced.

This invention provides an integrated method and system for data storage and management, belonging to the field of data processing and storage technology. The method includes: a semantic awareness module intercepting the write data stream during the data writing phase, obtaining the business semantic features of the data and encapsulating them into persistent tags; writing the persistent tags and the data ontology together into a programmable storage engine, associating them in storage units, and calculating the semantic association entropy; the programmable storage engine parsing the persistent tags and generating a storage unit management model when performing internal maintenance tasks; automatically executing management operations using this model and recording the result data; performing predictive management based on the semantic dynamic features of the storage units when executing management instructions, executing instructions through a unified metadata ring, and recording the execution result data; and feeding back the operation result data and execution result data to the semantic awareness module to verify and optimize the storage unit management model. This invention improves system response speed and resource utilization using the above method.

The present disclosure encompasses methods for molecularly subtyping formalin-fixed paraffin-embedded tumor samples. The disclosure works particularly well for old and degraded (archival) samples for which standard methods are unfeasible. Further, the methods disclosed allow for the correlation of patient outcome data with the molecular subtype of the tumor and provides a wealth of information which will guide treatment decisions and / or selection of therapeutic agents.

The invention discloses a medical record intelligent logic quality control method based on an AI large model, and relates to the technical field of medical information, the disclosed medical record intelligent logic quality control method based on the AI large model comprises the following steps: obtaining original text data of an electronic medical record and carrying out structured extraction, generating clinical focus problem data by using a first processing module of the AI large model; and driving a second processing module of the AI large model to perform deep reasoning in combination with a chain reasoning instruction, generating logic consistency verification result data, and integrating the result data into a quality control report, so that the problem that the deep logic vulnerabilities of medical records cannot be processed in the prior art is solved, a clinical expert thinking model can be simulated, end-to-end deep logic reasoning and consistency verification are realized, and the quality control report quality is improved. And the deep logic inconsistency in the medical record is identified, so that the medical risk is reduced and the medical record quality is improved.

The application relates to a medicine transportation management method, system and device and a storage medium, and belongs to the technical field of medicine management. The method comprises the following steps: determining the risk level of medicines; matching a corresponding detection period according to the risk level; in the detection period, an evaluation model is used to process image data and sensing data of the medicines to obtain result data, the image data is used to reflect the appearance shape of the medicines, and the sensing data is used to reflect the physical properties of the medicines and environmental parameters of the medicines. The application has the effect of improving the detection efficiency of toxic and narcotic medicines.

The invention belongs to the technical field of software testing, and particularly relates to a distributed testing platform system and method based on a micro-service architecture, and the system comprises a plug-in layer, a micro-service layer and an infrastructure layer. The plug-in layer is used for acquiring original data in a user operation process, packaging the original data according to a standardized interface format, and generating request data; the micro-service layer is used for receiving the request data, extracting key content in the request data after executing authority verification and route matching, analyzing the key content and generating a resource scheduling instruction; and the infrastructure layer is used for performing classified storage on the analysis data, asynchronously sending a resource scheduling instruction to a distributed test cluster, triggering a target node to execute a test task, monitoring key test indexes according to real-time log data generated in a test process, and feeding back result data to the plug-in layer through the micro-service layer after the test is completed. According to the scheme, the flexibility and reliability of a test link are improved.

This invention relates to the field of power engineering management and control and virtual power plant operation collaboration technology, specifically to a big data-based intelligent management and control system and method for power engineering. It collects and evaluates the quality of multi-source heterogeneous data from power engineering sites to obtain reliable data for engineering risk prediction, while simultaneously extracting feature data related to virtual power plant operation. Furthermore, it identifies and aggregates dispatchable load resources at the engineering site to form standardized virtual load packages. Combining the operational demand signals of the virtual power plant with the engineering risk status, it generates dispatch decision schemes through collaborative optimization calculations. Blockchain technology is used to ensure the reliable storage of dispatch instructions, execution, and result data, and based on this, automatically calculates collaborative benefits. This effectively solves the problem in existing technologies where power engineering management and control data are disconnected from virtual power plant operation, preventing a shift from passive risk warning to proactive value mining.

The invention specifically discloses a medication safety assessment method and system for hypertensive patients, and the method comprises the following steps: extracting one-complaint five-history, medication records, inspection indexes and outcome data of the hypertensive patients from an electronic health record system (EHR), carrying out the preprocessing, extracting key entities, and carrying out the verification of the key entities; constructing a comprehensive data set of'co-disease-medication-examination-symptom 'of the patient, identifying a co-disease combination strongest associated with hypertension, and mining specific adverse reaction signals under the co-disease combination; key factors causing adverse reactions or unreasonable medication are attributed through a multi-factor conditional logic regression or decision tree model; and based on common disease combination and multi-factor logistic regression analysis of one complaint and five histories, performing pairing t inspection on laboratory indexes, and generating a medication safety report of the hypertension common disease patient. By adopting the technical scheme, through multi-dimensional data analysis, specific influence factors causing adverse drug reaction or unreasonable drug use are attributed, and accurate drug use safety guidance is provided for clinicians.

The invention provides an inspection data processing method, device and equipment, belongs to the technical field of information processing, and solves the problems that the inspection data processing efficiency is low and the execution process is difficult to control. The method comprises the following steps: acquiring inspection target data, wherein the inspection target data comprises inspection time data, inspection execution equipment data and inspection object data; according to the inspection target data, performing traversal filling processing on the inspection form database to obtain inspection work order data; sending the inspection work order data to the client; receiving a plurality of inspection feedback data sent by the client, wherein the inspection feedback data is obtained by the client by generating an operation instruction according to the inspection work order data and executing the operation instruction; and performing statistical classification processing on the plurality of inspection result data to obtain inspection report data. According to the scheme, automatic triggering and accurate execution of the inspection task are realized.

The invention discloses a data verification method and device for a supervision data set, and relates to the technical field of supervision businesses, and the method comprises the steps: carrying out the data quality verification of a submission result data record in the supervision data set through a preset data quality verification rule library; determining a submission result data record corresponding to the data quality verification rule for triggering the alarm as a problem data record; acquiring data consanguinity information of a problem data record corresponding to the data quality verification rule, wherein the data consanguinity information comprises source data and a data processing path on which the problem data record is generated; according to the data consanguinity information of the problem data record corresponding to the data quality verification rule, a problem analysis report of the data quality verification rule is generated, and the problem analysis report comprises tracing analysis of the problem data record. According to the invention, data abnormity can be accurately positioned, and timely identification and effective rectification of data quality problems are supported.

The invention provides a financing process real-time monitoring and early warning method and device based on a knowledge graph, and the method comprises the steps: carrying out the real-time monitoring and early warning of a financing process according to multi-source target financial data which are collected in real time and / or at regular time and are generated in the financing process; dynamically updating a knowledge graph used for storing each node representing the type and attribute of the transaction entity and an association relationship between the nodes; abnormal index monitoring and transaction track data tracking are carried out on the knowledge graph in real time; determining an index financial risk early warning level corresponding to the abnormal index monitoring result data based on the customized financial risk rule data; and inputting the transaction trajectory data into a transaction anomaly detection model to obtain a trajectory financial risk score, and determining a trajectory financial risk level corresponding to the transaction trajectory data according to the trajectory financial risk score. According to the invention, the problems of difficulty in discovering hidden risks and poor adaptability caused by information fragmentation, insufficient monitoring and early warning real-time performance and weak association analysis capability of a traditional financing process monitoring mode can be solved.

The application discloses a kind of question and answer recommendation method, system, equipment, storage medium and program product, the method includes: real-time acquisition first information, first information contains first inquiry information and multiple second inquiry information, the inquiry time corresponding to second inquiry information is earlier than the inquiry time corresponding to first inquiry information;First information is carried out semantic analysis, determine first demand feature, first demand feature is used to describe first information in from at least one of the following characterization dimensions: first characterization dimension, second characterization dimension and third characterization dimension;First demand feature is searched in first vector knowledge base based on, determine multiple response result data for first inquiry information;Wherein, the similarity between response result data and first demand feature is greater than similarity threshold, and first vector knowledge base is formed by the response vector data corresponding to the response data of multiple different modalities.The present scheme can realize automatic and fast accurate question and answer recommendation.

The present disclosure provides a method of automated clinical study simulation. Further, in some embodiments, the method simulates clinical studies via conversational interactions with digital twin patients. Further, the method includes receiving profile data from one or more client devices, generating a digital twin data based on the profile data, generating one or more user inputs for an AI model using the digital twin data, and obtaining one or more response data from the AI model. Further, the AI model may be configured to generate the one or more response data for the one or more user inputs. Further, the method includes analyzing the one or more user inputs and the one or more response data, generating one or more result data based on the analyzing, and transmitting the one or more result data to the one or more client devices.

This invention discloses a multi-level and self-learning analysis method and medium for testing data. The method includes: acquiring and parsing product testing result data, and creating a general intelligent agent for the parsed data; determining the type of product problem based on the general intelligent agent, creating a corresponding specialized intelligent agent according to the problem type, and assigning the product problem to at least one corresponding specialized intelligent agent; establishing at least one attribution model based on the specialized intelligent agents to perform in-depth causal analysis of the product testing data and outputting the product reasoning chain; and performing hierarchical self-learning analysis of the product problem based on the reasoning chain and an accumulated knowledge base to obtain key factors affecting the product and the correlation between these factors. This application improves the depth and accuracy of product problem analysis by assigning data to general and specialized intelligent agents, with the general intelligent agent identifying the problem type and the specialized intelligent agents conducting in-depth analysis and mining to output the product reasoning chain.

An intelligent dosing platform incorporating comprehensive predictive analytics and forecasting capabilities for injectable medication administration. The platform includes predictive analytics modules configured to generate forecasts for financial costs, medication demand patterns, and adverse event probabilities using machine learning algorithms and statistical modeling techniques. The system features financial modeling modules that analyze cost patterns associated with medication procurement, administration infrastructure, and insurance reimbursements, while demand forecasting modules predict medication needs based on seasonal trends, patient population changes, and treatment protocol modifications. Adverse event prediction modules identify risk factors using patient risk profiles and clinical outcome histories. The platform incorporates machine learning capabilities that continuously improve prediction accuracy using patient outcome data and administration patterns. Comprehensive reporting modules generate customized predictive reports for healthcare administrators, pharmaceutical suppliers, and insurance providers, while optimization modules recommend actions for cost reduction and patient safety improvement based on predictive insights.

The invention discloses an attribution analysis method and device, equipment, a storage medium and a computer program product, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the causal structure learning of a target data set, obtaining a target causal relationship in the target data set, and constructing a causal knowledge graph based on the target causal relationship; in response to a selection instruction of a main analysis index of a target user, querying a node set having causal association with the main analysis index from the causal knowledge graph, and determining a recommended association node based on causal strength; performing intervention effect calculation on the main analysis index based on the main analysis index and the recommended association node to generate anti-fact deduction result data; and performing multi-dimensional confidence assessment on the anti-fact deduction result data to obtain an attribution path credibility score. And through causal structure learning and multi-dimensional confidence assessment, the problem that attribution analysis depends on experience hypothesis is solved, and the analysis reliability is improved.

This application discloses a remote logistics risk early warning method, device, and medium, relating to the field of intelligent logistics technology. The method includes: collecting multi-source data from various data sources during the logistics transportation process and preprocessing the multi-source data to generate a standardized multi-source dataset; analyzing the standardized multi-source dataset using a pre-trained large language model to identify predefined risk types present in the logistics transportation process; if present, generating risk identification data containing risk descriptions and risk locations, and calculating the probability of occurrence and impact range of the identified risk based on the risk identification data using a preset risk assessment model; weighting the probability of occurrence and impact range according to preset weighting factors to comprehensively assess the risk level of the identified risk and generate risk assessment result data; and automatically determining the early warning level and the early warning recipient based on the risk assessment result data.

Systems, methods, and computer-readable media for providing low-latency markets for wagering on sporting events are disclosed. In some embodiments, an outcome matrix may be generated by a plurality of contest simulations utilizing statistical data of a history of sporting events. Outcome data of the outcome matrix may be indicative of probabilities of events occurring during an upcoming contest. The probabilities may be calculated, and markets may be priced based on the calculated probabilities. The priced markets may be provided to users by a graphical user interface by user computing device. Furthermore, users may request user-requested markets by inputting different markets into the GUI. The user-requested markets may then be priced using the outcome data of the outcome matrix and stored calculations thus, providing new markets based on the user-requested markets from the outcome matrix without generating new simulations.

Systems, methods, and computer-readable media for providing low-latency markets for wagering on sporting events are disclosed. In some embodiments, an outcome matrix may be generated by a plurality of contest simulations utilizing statistical data of a history of sporting events. Outcome data of the outcome matrix may be indicative of probabilities of events occurring during an upcoming contest. The probabilities may be calculated, and markets may be priced based on the calculated probabilities. The priced markets may be provided to users by a graphical user interface by user computing device. Furthermore, users may request user-requested markets by inputting different markets into the GUI. The user-requested markets may then be priced using the outcome data of the outcome matrix and stored calculations thus, providing new markets based on the user-requested markets from the outcome matrix without generating new simulations.

The invention discloses a low-code test method and device, a storage medium, equipment and a program product, and the method comprises the steps: executing a multi-stage smoking test on low-code assets generated by a target model, and when verification of any stage fails, terminating a subsequent test process and recording an error type; if the low-code assets pass the smoking test, loading the low-code assets into an integrated development environment for function test; if verification fails in the smoking test or the function test, generating a repair strategy based on the identified error type and the item context information, calling the target model to perform automatic repair, and re-executing the test process after repair; based on the result data of the test process, the multi-dimensional evaluation index corresponding to the low-code assets is calculated, the multi-dimensional evaluation index is processed, and the standardized evaluation report is generated, so that full-link, automatic and intelligent benchmark test for the low-code assets is realized, and the low-code generation quality is ensured.

A method for optimizing at least one exercise for a first user. The method includes receiving first user data including attribute data associated with the first user and outcome data associated with the exercise. The method includes generating, based on the first user data, initial target data. The initial target data is associated with at least one of the first user, the exercise apparatus, and the exercise. The method includes receiving measurement data associated with at least one of the first user, the exercise apparatus, and the exercise. The measurement data is associated with one or more sensors. The method includes determining differential data based on one or more differences between the initial target data and the measurement data. The method includes generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate optimized target data.

This application relates to the field of computer science and discloses a method, device, medium, and product for simulating clinical trials based on molecular perturbation spectra. The method includes: determining the molecular perturbation spectrum of a target drug; determining a predictive model from genomics to high-dimensional omics based on genomic and high-dimensional omics data in a first population dataset; determining the predicted omics data for an individual based on the predictive model and the genomic data of an individual in a second population dataset; wherein the second population dataset also includes clinical outcome data; determining a grouping scheme for the simulated clinical trial by optimizing an individual allocation model based on the molecular perturbation spectrum and the predicted omics data of all individuals, maximizing the similarity between the predicted omics differences and the molecular perturbation spectrum between the simulated experimental group and the simulated control group; constructing a simulated clinical trial based on the grouping scheme; and determining an estimated value of the intervention effect of the target drug based on the differences in clinical outcomes between the simulated experimental group and the simulated control group in the simulated clinical trial.

Provided are a method, device, and computer program for providing medical guidance by using a generative artificial intelligence-based medical staff model. According to various embodiments of the present disclosure, provided is a method, performed by a computing device, for providing medical guidance by using a generative artificial intelligence-based medical staff model. The method comprises the steps of: acquiring clinical data generated in a process in which a medical staff member treats a patient; selecting one or more medical staff models from among a plurality of medical staff models on the basis of the acquired clinical data; deriving result data according to analyzing the acquired clinical data by means of the selected one or more medical staff models; and providing medical guidance information to the medical staff by using the derived result data.