45 results about "Outcome analysis" patented technology

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 discloses a natural resource environment quality comprehensive evaluation method and system based on spatial distribution, and relates to the technical field of environment evaluation. The evaluation method comprises the steps of S1, determining an evaluation target and an evaluation range, S2, collecting and processing data, S3, constructing an evaluation index system, S4, implementing comprehensive evaluation, S5, establishing a dynamic weight adjustment mechanism, and S6, analyzing a result and formulating a strategy. The method is technically characterized in that a related mechanism of weight adjustment triggered by abnormal indexes is established, on one hand, the recognition and feedback capability of key problems is enhanced, and on the other hand, conversion of an evaluation system from static evaluation to dynamic response is promoted, so that the problems that traditional evaluation results are lagged and ecological environment changes cannot be reflected in time are solved, and the evaluation efficiency is improved. According to the method, the closed-loop regulation and control capability of dynamic adjustment and optimization is embodied, after a primary weight correction action is carried out, when a certain index deviates from a normal threshold value, a secondary weight correction program is automatically started, and the system weight of a criterion layer where the index is located is improved.

The invention relates to the technical field of medical data analysis, and discloses a clinical examination result analysis method and system based on a medical knowledge graph. The method comprises the following steps: discretizing continuous test data of a patient into data segments with change modes, mapping a time sequence mode of the data segments to corresponding entity nodes of a knowledge graph, and constructing knowledge graph segments carrying dynamic information; and performing heuristic extension by identifying key mode paths in the analysis paths to generate a candidate analysis path set. And performing synchronous iteration comparison on the core analysis path and real-time inspection data, dynamically correcting the confidence coefficient weight of each path, and finally outputting a structured report according to the optimized main analysis link. According to the method, fusion of inspection data time sequence features and the knowledge graph is realized, and the depth of result analysis and the adaptive capacity to illness state evolution are improved through dynamic interaction optimization of paths and data.

The invention provides a GRU-attention mechanism-based metering terminal meter fault rate prediction method, relates to the technical field of smart power grid and power equipment health management, and aims to solve the problems of insufficient feature capture and low prediction precision of complex time sequence data in the prior art. The metering terminal meter fault rate prediction method based on the GRU-attention mechanism comprises the following steps: constructing a multi-dimensional fault prediction index system based on equipment operation parameters, age limit parameters and environmental parameters, and preprocessing original data by adopting a median filtering method and Min-Max normalization; an attention mechanism is embedded into a GRU layer to form an end-to-end GRU-attention mixed prediction model, and the steps of model training and optimization, failure rate prediction, result analysis and the like are carried out. According to the method, through fusing the time sequence modeling capability of the GRU and the feature focusing capability of the attention mechanism, the accuracy of metering terminal meter fault rate prediction is remarkably improved, the early warning of the metering equipment fault rate is effectively realized, and an auxiliary decision basis is provided for operation and maintenance work.

The present application relates to the technical field of natural language processing, and discloses a large language model clinical diagnosis capability fine-grained evaluation method and system. The method comprises: obtaining a patient medical record data set; synchronously performing the following tasks: scoring a question-standard answer-model answer triple based on a predetermined scoring dimension, calculating the medical knowledge score of the evaluated model; the evaluated model performs a recall task and a differential task, confirms or excludes a differential diagnosis list, and obtains a final main diagnosis; linking the differential diagnosis list and the final main diagnosis output by the evaluated model in the patient medical record; and analyzing factors affecting the clinical diagnosis capability of the evaluated model based on the linking result. The present application aims to deeply and synchronously evaluate the medical knowledge and reasoning capability of a large language model based on the same clinical diagnosis situation.

The invention relates to the technical field of artificial intelligence, and discloses a task result analysis method and device, equipment and a medium, and the method comprises the steps: obtaining task result data, and carrying out the preprocessing operation of the task result data, and obtaining structured data; performing feature extraction on the structured data through a deep learning model to obtain a high-dimensional feature matrix; analyzing the high-dimensional feature matrix through an exception detection model to obtain a verification data set containing exception annotations; and determining a field type and data distribution information in the verification data set, and inputting the verification data set into an analysis model, so that the analysis model determines an abnormal distribution result corresponding to the task result data based on the field type and the data distribution information. The method can be applied to business program systems in the fields of financial science and technology and medical pension, and the accuracy and the intelligent degree of task result analysis can be improved.

This invention relates to the field of power system automation testing technology and discloses a fault-tolerant testing method for automated equipment based on an event knowledge graph. The method includes the following steps: extracting entities, relationships, and attributes of fault events from multi-source heterogeneous data, constructing a triplet knowledge representation, eliminating entity ambiguity through semantic alignment technology, and forming a unified fault event knowledge graph; and employing a weighted subgraph traversal algorithm based on the fault event knowledge graph. The aim is to address the problems in existing fault-tolerant testing methods, such as test case generation relying on human experience, difficulty in automatically extracting related event chains from multi-source heterogeneous data to construct scenario-based test cases, lack of temporal logic and physical model constraints in fault injection leading to a disconnect between abnormal message sequences and real fault characteristics, and lack of comprehensive intelligent analysis capabilities for multi-dimensional response signals and a self-evolution mechanism for test knowledge in test result analysis, thus failing to form a continuously learning adaptive intelligent testing system.

This invention relates to the field of talent training quality assessment technology in medical and pharmaceutical colleges, and discloses a big data closed-loop assessment system for talent training quality in medical and pharmaceutical colleges. The system includes: a data acquisition module to collect multi-source training data; a preprocessing and fusion module to construct a talent training quality dataset; a big data closed-loop assessment module containing three-level indicator construction, multi-dimensional assessment, and result analysis units to complete the assessment and generate personalized reports; an intelligent feedback module to push and visualize the assessment results; a training optimization module to adapt rigid constraints and transform them into optimization measures; and a system iteration module to ensure the assessment system is updated synchronously with the pharmaceutical industry. This invention constructs a full-link closed loop, solving problems such as assessment disconnect and low accuracy, improving the quality of talent training and its industry adaptability, and adapting to the characteristics of medical and pharmaceutical college training.

The invention discloses a digital intelligent neurological critical patient tracheal intubation and extubation decision risk assessment system, and relates to the technical field of medical risk assessment, and the technical key points are that the system comprises a data acquisition module used for integrating clinical parameters of a patient, and obtaining oxyhemoglobin saturation, respiratory rate and airway secretion amount data in real time through an Internet of Things device; the model building module is used for receiving the data acquired by the data acquisition module, determining weight distribution by adopting a Delphi expert consultation method, and building a dynamic risk assessment model in combination with a machine learning algorithm; and the clinical decision module performs evaluation result analysis according to the dynamic risk evaluation model established by the model construction module, comprises a risk early warning subsystem, and generates a tube drawing suggestion when the real-time monitoring data triggers a preset threshold value. The system has a remarkable clinical application value, the nursing quality is improved, clinical decisions are supported through scientific risk assessment, and the working efficiency of medical staff is improved.

The invention belongs to the technical field of system comprehensive guarantee, and discloses a system autonomy evaluation model design method. According to the invention, an autonomy evaluation index system is constructed by combining the characteristics of an intelligent system; designing an index standardization method according to the index types; quantitatively determining the importance degree of the indexes by comprehensively utilizing expert experience, and determining a comprehensive weight; after the index set, the weight set and the comment set are determined, a weighted synthesis method and a multiplication synthesis method are adopted for layer-by-layer synthesis from a low layer to a high layer, a final quantitative evaluation result is obtained, an evaluation result explanation conclusion and result analysis are provided, support is provided for subsequent autonomy optimization design, and autonomy evaluation accuracy is improved.

An example method includes determining a point from a data set closest to a particular data point using a particular metric and scoring a particular data point based on whether the closest point shares a similar characteristic, selecting a subset of metrics based on the metric score to generate a subset of metrics, evaluating a metric-lens combination by calculating a metric-lens score based on entropy of shared characteristics across subspaces of a reference map generated by the metric-lens combination, selecting a metric-lens combination based on the metric-lens score, generating topological representations using the received data set, associating each node with at least one shared characteristic based on member data points of that particular node sharing the shared characteristic, scoring groups within each topological representation based on entropy, scoring topological representation based on the group scores, and providing a visualization of at least one topological representation based on the graph scores.

This invention discloses an IgA nephropathy prediction model and its construction method, relating to the field of medical technology. By constructing a feature set, it measures whether the renal mesangial area is in a state of pathological enhancement, thereby assessing the potential risk of IgA nephropathy. Next, based on the measurement results in S1, it analyzes the occult risk of the lesions and further assesses whether the patient has a high risk of disease progression by calculating an occult progression factor. The calculation of the occult progression factor helps identify high-risk individuals among patients with mild symptoms. If the occult progression factor exceeds a set threshold, the system automatically triggers a prediction mechanism to guide subsequent treatment strategies. Based on this, through training the prediction model using the patient's historical physical examination data, it further outputs a comprehensive IgA prediction value and generates a risk level. This process achieves a comprehensive analysis from historical data to current physical examination results, ensuring the accuracy of the prediction results.

The application discloses a background activity automatic identification method and system based on resting state electroencephalogram (EEG), which automatically identifies the EEG signals of a patient in a closed-eye state in different regions, extracts EEG features of each region and each frequency, analyzes and compares the EEG features by referring to a standardized range, and finally outputs the identification and analysis results as a visual report, so as to facilitate quick manual verification, online editing and modification when necessary, realize the efficiency of EEG automatic interpretation, guarantee the accuracy of result analysis, provide a possibility of interpreting high-dimensional data information from original two-dimensional EEG efficiently and quickly, and provide a potential technical means for non-EEG professionals who are not familiar with EEG data analysis and processing. The application can realize the automation of the identification process, improve the identification efficiency, thereby reducing heavy and repetitive mechanical labor, facilitating medical staff to analyze and judge the result report, improving the work efficiency, and saving the labor cost.

The invention relates to the technical field of health data processing, in particular to an intelligent physical examination item recommendation and result analysis method and platform and electronic device.The method comprises the steps that historical physical examination information and historical training data of a target user are collected, and physical characteristics of the target user are constructed based on the historical physical examination information; the method comprises the steps of establishing an environment adaptation feature set of a target user according to historical training data, collecting training data and training environment data of the target user in real time, determining a real-time physical ability state by combining the training data with physical ability features of the target user, and updating the real-time physical ability state of the target user according to the environment adaptation feature set of the target user. And extracting a post feature item set according to the post attribute of the target user, and recommending physical examination items to the target user according to the real-time physical fitness state of the target user in the monitoring period and the post feature item set. According to the invention, the recommendation efficiency of the physical examination items is effectively improved.

Embodiments of the present application relate to the technical field of examination result analysis, and provide a cervical lesion related examination result analysis method and apparatus, a device, and a medium. The method comprises: receiving a case report picture uploaded by a current user terminal, and reading case data in the case report picture; acquiring prediction result information corresponding to the case data on the basis of a current prediction model, and selecting optimal expert information on the basis of the prediction result information; pushing the case data and the prediction result information to an expert terminal corresponding to the optimal expert information, and receiving a final clinical analysis result fed back by the expert terminal; and sending the final clinical analysis result to the current user terminal corresponding to a current user. The present application sends a final clinical analysis result to a current user terminal, so that a current user can conveniently check the final clinical analysis result, and choose whether to perform further pathological examination on the basis of the final clinical analysis result, so that the rationality of medical resource use is improved.

The invention discloses a water conservancy model simulation calculation method and system, and the method comprises the following steps: S1, data collection and preprocessing; s2, constructing a water conservancy model; s3, calculating and optimizing; s4, calibrating and verifying the model; s5, performing real-time simulation and result analysis; s6, scene simulation and elastic expansion; s7, risk early warning and collaborative decision making; the water conservancy model simulation calculation system comprises a data acquisition and preprocessing module, a model construction module, a calculation module, a calibration verification module, an intelligent simulation and analysis module, a simulation expansion module, a risk early warning decision module and a scheme generation optimization module. According to the method, advanced technologies such as the Internet of Things and deep learning are fused, data processing, dynamic modeling, parallel computing optimization and interactive analysis, newly added digital twinborn simulation, intelligent risk early warning and generative intelligent scheme optimization are achieved, and the simulation precision and decision-making efficiency of the water conservancy model are improved through multi-link cooperation.

The invention discloses a visual paper-based cancer miRNA marker detection kit, the kit comprises an integrally formed foldable T-shaped filter paper, and a DNA probe TEprobe, urease and a color developing agent dropwise added on the filter paper, and the principle of the kit is as follows: the TEprobe is combined with a cancer nucleic acid marker (miRNA) to release Ag < + > to inhibit the activity of the urease; the composite signal response of the pH response probe phenol red and fluorescein is triggered, and the detection sensitivity is as low as 10-12 M. The kit provided by the invention can significantly shorten the detection time and reduce the sample demand, and realizes zero false positive rate and 97.10% specificity by combining with machine learning algorithm classification during result analysis. The performance is successfully verified in real samples of prostatic cancer, liver cancer and colorectal cancer, and a cheap and convenient means is expected to be provided for family self-inspection and cancer screening and diagnosis in resource-deficient areas.

The invention provides a clinical pathway and DRG cost collaborative management and control method and system. The method comprises the following steps: acquiring multi-source heterogeneous medical data; constructing a medical knowledge graph based on the medical data; reasoning the initial diagnosis and treatment information of the patient based on the medical knowledge map, and predicting and outputting a recommended clinical path of the patient and a corresponding DRG pre-grouping result; monitoring the deviation between the actual diagnosis and treatment behavior of the patient in the diagnosis and treatment process and the recommended clinical path and the difference between the actual cost and the DRG pre-grouping cost in real time, and performing intervention when a preset condition is triggered; and collecting complete diagnosis and treatment process data and a path execution result after the patient is discharged from the hospital, and feeding back an analysis and evaluation result to the medical knowledge graph after analysis and evaluation so as to carry out iterative optimization on the knowledge graph. According to the application, the originally isolated clinical pathways, DRG grouping, patient diagnosis and treatment and cost information are deeply fused and associated, and a rigid and static management mode of the traditional clinical pathways is broken through.

The invention provides an internal threat identification and early warning method based on behavior analysis, and the method comprises the steps: analyzing the matching degree of the record of a query object unassociated patient and the retrieval range exceeding the responsibility authority for a classified behavior feature subset, and carrying out the comparison through a pre-established field responsibility authority library, if the query time is concentrated in a non-working period or the retrieval time presents a periodic rule, judging that the behavior deviates from an expected range, and determining a preliminary abnormal behavior mark; and for the deep behavior analysis result, analyzing the similarity between the rare disease category related to the query content and the specific medical history focused by the retrieval content, and if the retrieval range exceeds the responsibility authority and the query behavior lacks the business context, determining the classification label of the illegal behavior to obtain the illegal behavior judgment conclusion.

Provided is a monitoring system of a vital sign monitoring optical fiber sensor, comprising a database module, an optical fiber sensor signal acquisition module, an optical fiber sensor signal processing module, a data analysis module, a comparative verification and calibration module, a data comprehensive analysis module, an optical fiber sensor monitoring and evaluation module, and a user interface module. The database module is used for collecting vital sign data of healthy people acquired by an optical fiber sensor, the data comprising heart rate, body temperature, respiratory rate, and blood oxygen saturation, for analyzing, screening, classifying, and statistically analyzing the data to form a normal numerical range under different age, gender, weight, and height conditions, and for storing the normal numerical range in a database. The monitoring system can accurately monitor vital signs of a patient in real time, and provide, by comparing and analyzing the vital signs with normal values of the vital signs in the database, a monitoring result analysis suggestion, and health warning information.

The present application relates to underwater scene recognition and classification technical field, specifically to a kind of coral classification method of fusing depth data enhancement and transfer learning, comprising the following steps: network structure building;Image preprocessing;Calculate the F1-score of each category on original training set;Original training set is deepened data enhanced using DeepSMOTE-F1 to obtain new sample;Using transfer learning to initialize network parameters, obtain the pre-training model of coral classification;The parameters of pre-training model are retrained on original sample and new generation sample, and the final model of coral classification is obtained;Model test and result analysis.The present application realizes the targeted data enhancement of each category sample, so that the model can learn the characteristics of each class more fully, thereby solving the problem of data set imbalance in coral classification task.

The present invention provides a molecular force field multi-objective fitting algorithm library and the workflow, including: FFOptlterator as the main module for input and output and force field parameter training iteration; EnergyCalculator module used for MM energy and energy derivatives calculation for the required value of each iteration in the optimization algorithm; the PropertyEstimator module used for the thermodynamic property calculation based on the MD simulation. Wherein, when initializing the FFOptlterator and EnergyCalculator objects, the user specifies the training force field parameters, adjustable parameter ranges, system setting arguments, and MD simulation parameters. The invention is suitable for the related applications of molecular force field training and verification, and implement the framework for different training targets, the prediction of different molecular physical properties, the compatibility and conversion of force field parameters in different formats, integration of multi-objective optimization, result analysis and graphing, etc.

The invention relates to the technical field of pet health monitoring, and discloses a pet health monitoring system based on microbiome gene detection. The system comprises a data acquisition and fluidization module, a cross-level association and preliminary screening module, a dynamic baseline modeling module, an anomaly comparison and background analysis module and a deep threat tracing module. According to the system, a pet gene sequence and metadata are obtained by formulating an acquisition scheme, and a real-time data stream is formed; carrying out cross-level correlation analysis, identifying an abnormal interaction mode and generating an abnormal mark data set with a weight; constructing and dynamically calibrating a personalized baseline model by using the data set and the data stream; comparing real-time data with a baseline to find deviation, and analyzing an abnormal background in combination with metadata; and finally, based on an anomaly identification result, analyzing metadata to trace the deep health threat mode and the root cause. According to the invention, continuous dynamic monitoring and multi-level correlation analysis of the pet microbiome are realized, and the accuracy of early health risk early warning is improved.

Provided are a system and method for predicting a disease risk of a female subject in need thereof, including an input module, a sub-outcome analysis module, an outcome analysis module, an outcome display module, and a sub-outcome display module. The input module is configured to input feature data of the female subject. The sub-outcome analysis module is configured to generate a sub-outcome corresponding to a risk of a comorbidity of a disease based on to the feature data. The outcome analysis module is configured to generate an outcome corresponding to a risk of the disease based on a comprehensive assessment of the sub-outcome. The outcome display module is configured to present the outcome on a first operation interface. The sub-outcome display module is configured to present the sub-outcome on a second operation interface in response to an input selection made via the first operation interface.

The invention relates to an electroencephalogram analysis report generation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring structured text data corresponding to an electroencephalogram detection document; calling a neuroelectrophysiology cue word to construct a template; receiving user-defined demand information input by a user in a user-defined manner; according to the self-defined demand information, the structured text data and a neuroelectrophysiology cue word construction template, generating a dynamic cue word; inputting the dynamic cue word and the structured text data into a big data analysis model to obtain analysis result data output by the big data analysis model; and extracting detection result analysis data related to a preset electroencephalogram detection index from the analysis result data, and generating an electroencephalogram analysis report according to the detection result analysis data. By adopting the method, the efficiency and accuracy of generating the electroencephalogram analysis report can be improved.

The invention relates to a canteen food material credible traceability management method based on a supply chain. The canteen food material credible traceability management method comprises the steps of 1, constructing a multi-dimensional evaluation index system; 2, collecting and preprocessing time sequence data; 3, screening and optimizing dynamic indexes: analyzing and calculating the dynamic variance contribution rate of the indexes in the sliding time window by adopting time sequence factors, eliminating redundant indexes and retaining core explanatory variables; and 4, bimodal weight generation: through a bimodal weight generation mechanism, integrating expert triangular fuzzy judgment and entropy weight objective calculation by applying a fuzzy Delphi-entropy weight fusion method in a cold start stage, and deploying an LSTM time sequence model in normalized operation to capture an index importance evolution rule. The final weight is output after dynamic attenuation fusion and causal verification of the gateway; 5, fuzzy comprehensive evaluation: setting an evaluation grade based on the fuzzy relation matrix and a dynamic weighted scoring formula; and 6, result analysis: displaying trend evolution, weight comparison and an evaluation result ring through a visual instrument panel.

Embodiments of the present application disclose a method for determining a privacy budget, the method comprising: obtaining a disturbed feature vector, and initializing node features based on the disturbed feature vector; constructing a graph dataset based on the node features, and encoding the graph dataset to obtain a graph embedding; decoding the graph embedding, reconstructing an adjacency matrix, and determining link prediction accuracy based on the adjacency matrix; determining attribute attack accuracy according to the graph embedding and sensitive attribute labels of partial objects; and performing result analysis on the link prediction accuracy and the attribute attack accuracy to obtain a privacy budget analysis result. The above method can directly obtain the privacy budget analysis result, and relevant personnel can intuitively understand the correlation between the link prediction accuracy and the attribute attack accuracy under each privacy budget value, and then can select a corresponding privacy budget value according to actual task use requirements.

The application discloses a natural resource environment quality comprehensive evaluation method and system based on spatial distribution, and relates to the technical field of environmental evaluation; the steps of the evaluation method comprise: S1, determining an evaluation target and range; S2, data collection and processing; S3, constructing an evaluation index system; S4, implementing comprehensive evaluation; S5, a dynamic weight adjustment mechanism; S6, result analysis and strategy making; the technical points are that a related mechanism for triggering weight adjustment of abnormal indexes is established, on the one hand, the identification and feedback capability for key problems is enhanced, and on the other hand, the evaluation system is changed from static evaluation to dynamic response, thereby solving the problem that a traditional evaluation result is lagging and cannot timely reflect ecological environment changes, embodying the closed-loop regulation and control capability of dynamic optimization, and after a weight correction action, when an index deviates from a normal threshold value, a secondary weight correction program is automatically started, and the system weight of a criterion layer where the index is located is improved.

A computing apparatus includes a processor, a communication interface coupled via a bus configured to communicate over a communication network with provider computing devices, external data server devices, and clinical server devices, and a memory storing a decision engine, workflow engine, outcome analysis tool, parameter recommendation tool, curriculum rules database, and hours rules database. The processor receives electronic requests, retrieves client specific data from external data server devices, and analyzes the data using automated evaluation assessment tools. The decision engine correlates client specific data with prior stored client outcome data to identify matching data patterns, improving accuracy. The workflow engine automatically processes and correlates evaluation data with stored curriculum and hour rules from clinical server devices to determine parameters without manual intervention, reducing processing time. Plan data is generated by correlating scores with client data stored in memory and transmitted over the communication network, standardizing data processing across multiple provider computing devices.