218 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.

Systems and methods implement algorithms for initializing medical devices, including by programming initial medical device settings. Further, systems and methods implement adaptive algorithms for changing medical device settings. Settings initialization algorithms can calculate the settings most likely to be applicable for the patient at-issue using databases of positive clinical outcomes and respective therapy settings for existing users. Settings adaptation algorithm are configured to update medical device therapy settings at one or more intervals including based on databases of positive clinical outcomes and respective therapy settings for existing users.

The invention discloses a die life prediction and maintenance decision system based on digital twinning. The system comprises a data acquisition module, a data fusion module, a digital twinning body construction and updating module, a residual life prediction module, a maintenance decision and optimization module and a closed-loop execution and feedback module. The system collects working condition data and production parameters of a physical mold in real time, generates a comprehensive health state index after fusion processing, constructs a dynamic digital twin, simulates a future production plan based on the digital twin, predicts the remaining service life of the mold, and combines a production schedule, a resource inventory and a cost model. And generating, issuing and executing an optimal maintenance decision scheme. The system continuously updates and optimizes the digital twin by using the maintained result data through a closed-loop feedback mechanism, so that the health state evaluation, the life prediction and the dynamic optimization of the maintenance strategy of the mold are realized, the mold management level is effectively improved, and the maintenance cost and the production shutdown risk are reduced.

The invention relates to the field of traditional Chinese medicine diagnosis, and discloses a traditional Chinese medicine intelligent diagnosis system based on multi-source data fusion and an AI technology, and the system comprises a tongue picture collection and classification unit which is used for collecting tongue picture data of a patient through imaging equipment, carrying out the feature extraction of the tongue picture data, and obtaining a tongue picture feature data set; performing tongue picture pathological mode classification on the tongue picture feature data set based on a deep convolutional neural network to obtain tongue diagnosis identification result data; and the face diagnosis feature correlation analysis unit is used for inputting the tongue diagnosis identification result data into a multi-modal data fusion engine and carrying out face diagnosis pathological feature correlation analysis based on a graph attention network to obtain face diagnosis identification result data. The deep semantic understanding is realized by introducing a plurality of data acquisition modes such as three-dimensional imaging, thermal imaging, micro-expression recognition, spectral information and pulse condition harmonic analysis and combining advanced models such as a graph neural network, U-Net and Bi-LSTM.

The invention belongs to the technical field of operation and maintenance of edge computing equipment in an industrial scene, and particularly relates to an edge computing terminal information query method based on LLM and MCP protocols and an intelligent agent. According to the method, a free question and answer mode and a timed query mode are supported at the same time, the free question and answer mode meets the temporary and interactive information acquisition requirement of a user, and the timed query mode is suitable for periodic and automatic index acquisition and monitoring, so that two scenes of real-time query and long-term operation and maintenance are considered, semantic analysis and context modeling are performed on a natural language request through LLM, and the query efficiency is improved. According to the method, the multi-target requirements and implicit intentions of the user can be understood, the mood, specialty and context consistency can be adjusted when the natural language answer is generated, the user experience is remarkably improved, bidirectional authentication and communication encryption of the client side and the server side are achieved by combining the MCP with the encryption token, and the safety of the query process and the reliability of result data are ensured.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

The invention relates to the technical field of medical information, and discloses a dosage regulation and control data storage method and system based on multi-modal diagnosis and treatment data, and the method comprises the steps: obtaining historical diagnosis and treatment data and historical dosage data of a patient; matching the diagnosis result data with health diagnosis result data in the health diagnosis and treatment data, and matching the wearable device data with health wearable device data in the health diagnosis and treatment data; determining whether to adjust the historical dose data to obtain the current dose, and outputting a judgment result needing to be adjusted; and obtaining the adjusted dose data, and storing the diagnosis result data, the diagnosis result similarity, the wearable device data, the wearable data similarity, the historical dose data and the adjusted dose data into a dose adjustment unit in a patient database. According to the invention, multi-modal diagnosis and treatment data can be integrated and applied to dose regulation and control, and a complete medication decision data chain is constructed.

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 provides a data access and use control method and system for a trusted data space. The method is applied to a data user terminal. A data user connector, a safety control area management module and a result output area management module which are connected pairwise are integrated in the data user terminal; the security control area management module supports deployment of a data processing application program in a security control area; through setting of the security control area, the result output area and the corresponding management module and the corresponding processing flow, the data user application program can directly access the original plaintext data of the specified data, but cannot take the plaintext data out of the security control area; the result data processed by the application program can only be written into the result output area, the data is prevented from being leaked from the channel through a mechanism of firstly forcibly encrypting the result data and then auditing the result data, and the safety problems of data leakage and the like during cross-mechanism, cross-body and cross-area sharing and circulation of the data of the trusted data space are thoroughly solved.

Sleep apnea prediction using electrocardiograms and machine learning is described. In one or more implementations, a wearable monitoring device produces electrical potential measurements of a heart of a user during an observation period spanning multiple days. A sleep apnea classification of the user is predicted by providing the electrical potential measurements to one or more machine learning models as input. The one or more machine learning models are trained based on historical electrical potential measurements and historical outcome data of a user population to correlate patterns in electrical potential measurements to sleep apnea classifications. The sleep apnea classification may then be output, such as in a health report, via a user interface, as notification on a computing device, and so forth.

The invention discloses a large-model intelligent question setting system and method based on fuzzy mathematics fine tuning, and belongs to the technical field of intelligent question setting. Comprising a fuzzification processing module, a multi-dimensional reward feedback module, a rule-driven attribute reasoning module, an attribute accurate quantification module, a self-adaptive membership degree adjustment module, a strategy optimization and intra-cluster standardization module and a question generation and structured output module. According to objective fields such as post specifications, post levels, technical stack depth and team scales, hard indexes are converted into soft boundary semantics through five membership functions, and then quantitative attributes of surface test questions on difficulty, openness, prejudice and distinction are reasoned by using N rules without subjective wording. And the business result data is used for driving rewards to be updated online, and finally, daily automatic fine tuning is realized on the 32B large model, so that each subjective surface test question generated by the AI not only meets the objective requirements of posts, but also has explainable, auditory and iterable closed-loop capabilities.

To support the monitoring of fraudulent accounting by switching account items.SOLUTION: A cluster generation unit assigns a plurality of pieces of recorded data, which are recorded for each task and include at least an account item and a recorded amount, to a cluster that is a plurality of groups, according to the similarity between pieces of the recorded data. A detection unit detects, for each cluster, rare cost items, which are account items whose amount composition ratio that is the ratio of the recorded amount of each recorded data to the total amount of the recorded amounts of the recorded data for the entire cluster, is below a cost determination ratio threshold. An abnormality determination unit determines, as an abnormality, the recorded amount for the rare cost item that is outside a predetermined amount range. A display control unit then controls the display of the abnormality determination result data, including the total amount determined to be abnormal, the task, and the cluster to which the work belongs, on a display unit. Thus, the monitoring of fraudulent accounting by switching account items can be supported.SELECTED DRAWING: Figure 1

A system may include a neurostimulator and a processing system configured to provide a stimulation effects map by testing stimulation parameter sets from a plurality of available stimulation parameter sets, acquiring clinical effect data indicative of a patient response to electrical energy delivered to the tissue using at least a first subset of the tested stimulation parameter sets, acquiring sensed data indicative of a sensed response to the electrical energy delivered to the tissue using at least a second subset of the tested stimulation parameter sets, and evaluating parameter sets from the plurality of untested stimulation parameter sets including for each of the evaluated parameter sets, determining an estimated response by estimating at least one of the patient response or the sensed response to the electrical energy using the acquired clinical effect data and the acquired sensed data. A stimulation parameter set may be chosen based on the map.

The invention discloses a medical aid decision-making method and system, a clinical decision-making support instrument and a storage medium. The method comprises the following steps: acquiring medical problem data; performing retrieval enhancement labeling on the medical problem data to obtain problem knowledge fragments; performing semantic fusion analysis on the medical problem data and the problem knowledge fragments according to a fully trained large language model to obtain auxiliary decision data of the medical problem data; wherein the auxiliary decision-making data comprises medical result data and question knowledge fragments; according to the method, professional knowledge fragments related to medical problems are obtained by retrieving enhanced labels, reliable knowledge support is provided for a large language model, illusion or errors possibly generated due to the fact that the large model only depends on training data of the large model are avoided, and medical result data obtained after semantic fusion analysis better conform to medical professional logic; as the auxiliary decision-making data further comprises question knowledge fragments, the output auxiliary decision-making data is associated with the corresponding professional basis, so that the source of the auxiliary decision-making data can be understood conveniently.

The invention discloses a depressive emotion intelligent identification and intervention system. The system comprises a data acquisition module used for acquiring original data of a user in real time; the data preprocessing module is used for performing layered preprocessing on original data; the feature extraction module is used for extracting each modal feature; the multi-modal fusion module realizes multi-modal dynamic fusion based on a bidirectional long short-term memory network and a cross attention mechanism, and supports multi-task output of depression dichotomy, PHQ-9 scale regression prediction and the like through a full-connection network; the user interaction module is used for providing task guidance, data acquisition control and intervention suggestion visualization for a user; the system integrates a self-developed VR (Virtual Reality) mind game and an AI (Artificial Intelligence) psychological big model, and provides intervention tools such as mind training, dialogue intervention and PC (Personal Computer) terminal severe games for different depression levels. The data management module is used for storing user information, original data, a depression emotion recognition process and evaluation result data, and a feasible technical means is provided for early recognition and auxiliary evaluation of depression risks.

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.

The invention provides a dot product operation device, which comprises a configuration controller, a dot product unit, an arbiter and an accumulation unit, and is characterized in that the dot product unit comprises a plurality of dot product operation modules, each dot product operation module is used for processing a plurality of input data with different data precisions, and the data precisions of the input data processed by different dot product operation modules are at least partially different; the configuration controller determines a target dot product operation module from the plurality of dot product operation modules according to the data precision of the received input data, and sends the plurality of input data to the target dot product operation module; the target dot product operation module performs dot product operation on the multiple input data to obtain intermediate result data; the arbiter determines current to-be-processed data from the received intermediate result data according to a set scheduling mode, and sends the current to-be-processed data to an accumulation unit; and the accumulation unit is used for performing accumulation operation on the current to-be-processed data and the matched offset data to obtain target result data and outputting the target result data.

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 invention discloses an AI-based medical information anomaly detection analysis method and analysis system, and relates to the technical field of medical information analysis, and the method comprises the steps: collecting medical detection data and corresponding medical diagnosis result data for each patient; based on the medical detection data and the medical detection item normal data, analyzing whether the medical detection data is normal or not, and if yes, generating normal detection data; according to the AI-based medical information anomaly detection analysis method and analysis system, the diagnosis opinions given by doctors based on the detection data of the detected items are collected, the anomaly types corresponding to the diagnosis opinions and the confidence coefficients and confidence intervals corresponding to the detection data of the detected items are analyzed, and whether the diagnosis opinions of the doctors are abnormal or not is judged; and a prompt is given when an abnormality occurs, so that the misdiagnosis probability of a doctor is reduced.

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 embodiment of the invention provides a patient data real-time quality control method and device, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring patient data, wherein the patient data comprises patient information and test result data of a patient on a target test item; inputting the patient information into a target regression model corresponding to the target inspection item to obtain prediction result data; determining a residual sequence according to the test result data, the prediction result data and a historical residual result; a quality control range is determined by smoothing the residual error sequence; and if the inspection result data exceed the quality control range, triggering an alarm indication. Therefore, by introducing the target regression model, the complex relationship between the patient information of the patient and the inspection result data can be automatically learned, multiple quality control schemes do not need to be formulated for different patient groups, the quality control process is remarkably simplified, and the complexity of quality control steps is reduced.

According to various embodiments, a server for predicting determination on a diabetic foot on the basis of a plurality of pieces of data on the basis of a prediction model, comprises: a communication interface; a memory; and a processor, wherein the processor is configured to: obtain at least one piece of image data and at least one piece of first sensing data from an external server of an external user through the communication interface; obtain first ulcer output data by inputting the at least one piece of image data and the at least one piece of first sensing data to the prediction model; when it is determined that the first ulcer output data exceeds a preset first threshold value on the basis of a result of comparing the first ulcer output data with the preset first threshold value, output that a foot ulcer of the external user is likely to be diabetic foot ulcer; when it is determined that the first ulcer output data exceeds a second threshold value set to be greater than the first threshold value on the basis of a result of comparing the first ulcer output data with the second threshold value, output that the foot ulcer of the external user is highly likely to be the diabetic foot ulcer; and transmit, through the communication interface, result data for the first ulcer output data determined to exceed the first threshold value and / or the second threshold value, and guideline data presenting at least one guideline on the basis of the result data, wherein the prediction model is trained on the basis of a plurality of pieces of image data, a plurality of pieces of first sensing data, and a plurality of pieces of second sensing data, which are obtained from a plurality of users, a plurality of pieces of result data output from the plurality of users, and at least one piece of professional opinion data obtained by observing the plurality of users.

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 embodiment of the invention discloses a target model training method and device for individualized curative effect evaluation, and the method comprises the steps: obtaining sample observation data, fitting the relation between expected outcome data and target body feature data, and obtaining an individual outcome baseline prediction value; fitting a relation between the treatment state data and the target body feature data to obtain an individual treatment probability prediction value; determining a first difference value between the individual outcome baseline predicted value and the actual outcome of the individual; determining a second difference between the individual treatment probability prediction value and the actual treatment state of the individual; estimating an individualized treatment effect; constructing a target equivalent relational expression; training and learning the initial model to enable the target loss function to be the value of the individualized treatment effect to be the minimum, and obtaining a target model. According to the scheme, based on a causal inference theory and a machine learning modeling strategy, an existing causal machine learning method is improved so as to adapt to challenges brought by sample imbalance data, and scientificity and practicability of individualized intervention decision are improved.