66 results about "Outcome analysis" patented technology

The invention discloses a health monitoring system based on pulse signal multi-domain feature extraction and deep learning, and the system is characterized in that a multi-dimensional feature extraction module employs an FMCW radar and a sensor device to collect and obtain pulse signals, carries out the multi-level processing and feature extraction of the obtained pulse signals, and extracts features related to a health state; the feature association mapping module matches and associates the pulse signal features of different frequency bands with the health state; the state recognition module automatically recognizes and classifies pulse signal features and health states based on a deep learning model; and the health information feedback module analyzes the health state of the user according to the health state recognition result of the state recognition module, provides health feedback and generates corresponding personalized health guidance information. By using the system, comprehensive analysis of health conditions can be realized, continuous and highly-targeted health tracking and evaluation services can be provided, and modern personalized and intelligent traditional Chinese medicine health monitoring and management requirements can be met.

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 an RWS-based post-market drug effect tracking platform, which comprises a data acquisition layer, a data processing layer and a result analysis layer, and is characterized in that the data acquisition layer is used for acquiring RWS data from different sources, the data processing layer is used for preprocessing the RWS data from different sources, constructing an original data chain based on a block chain technology, and analyzing the result of the original data chain; writing the preprocessed RWS data of different sources into an original data chain for storage; and the result analysis layer is used for acquiring the preprocessed RWS data through the original data chain, analyzing the preprocessed RWS data, constructing a drug tracking knowledge graph, and comprehensively analyzing the drug tracking knowledge graph to obtain an indication distribution result, an adverse reaction monitoring result, a treatment cost distribution result and a curative effect analysis result.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

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 discloses an evaluation method for a multi-modal large model, and the method comprises the following steps: S1, data collection: collecting multi-modal data from a multi-modal data source, including images, voices and / or texts, the data of each modal including features related to the performance of the model; s2, model training: training a multi-modal large model by using the collected multi-modal data; s3, performance evaluation: performing performance evaluation on the trained multi-modal large model; s4, model optimization: optimizing the multi-modal large model according to a performance evaluation result; and S5, result analysis and suggestion: carrying out result analysis on the optimized multi-modal large model, and providing a corresponding optimization suggestion. The scheme has the advantages of less manual participation, high efficiency and high accuracy, and is suitable for the evaluation requirements of various multi-modal large models.

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 invention discloses an operation duration prediction and scheduling method based on machine learning, and belongs to the technical field of medical care informatics, and the method comprises the following steps: obtaining platform historical processing data of which the operation type is the same as a predicted operation type, and carrying out the analysis to obtain a historical processing difficulty index; a resource consumption index is obtained through analysis, and a duration prediction emergency judgment result is obtained through comprehensive analysis; analyzing to obtain a data transmission channel effect index, analyzing to obtain a final data segmentation judgment result based on the data segmentation preliminary judgment result and the data transmission channel effect index, and performing corresponding data processing to obtain a feature data packet; a corresponding data transmission protocol is selected based on the duration prediction emergency judgment result, so that the feature data packet is transmitted to an operation duration prediction module; and the operation duration prediction module receives the feature data packet, analyzes the feature data packet by using a machine learning algorithm to obtain an operation duration prediction value, and performs automatic scheduling prompt, so that the problem of slow operation duration prediction response in the prior art is solved.

The invention relates to the technical field of mechanical route adjustment, in particular to an intelligent medical instrument production monitoring system based on big data management analysis. The system comprises a disinfection state analysis module, a disinfection route adjustment module and a matching adaptation mode matching module. The disinfection state analysis module is used for carrying out detection result analysis on the initialized disinfection state, the disinfection state of the current batch of medical instruments is obtained, a sensitive disinfection area is positioned to serve as an object of later adjustment, and the disinfection route adjustment module is used for matching the same type of medical instruments; a disinfection interval is divided according to an initial disinfection state and a disinfection state of a sensitive disinfection area of the same type, finally, an adaptive mode matching module is matched and combined with a stage disinfection effect analysis result, and a disinfection state suitable for a current batch of medical instruments is selected through actual data simulation comparison, so that stage adjustment of the disinfection state can be carried out orderly, and the medical instruments can be disinfected. The disinfection state adjusting efficiency is improved.

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.

The invention relates to the field of image processing and biomechanics, in particular to a wrist joint mechanical detection method based on finite element analysis, which comprises the steps of acquiring medical image data, preprocessing images, endowing material attributes, setting mechanical loads and applying boundary conditions, solving finite elements and analyzing results. Through finite element analysis, the mechanical behaviors of the wrist joint under different loads can be accurately simulated, and the stress and strain distribution conditions of the wrist joint can be comprehensively analyzed. According to the method, the stress condition of the bone can be detected, the mechanical response of the cartilage tissue can be analyzed in detail, richer and more accurate information is provided for clinical diagnosis, and independent segmentation and assignment processing can be carried out on the lesion part. According to the method, the influence of lesion tissues on the mechanical characteristics of the wrist joint is fully considered, personalized analysis is carried out on patients, a more accurate treatment scheme can be formulated for each patient, and the treatment effect is improved.

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 present invention discloses an isogeometric heat conduction simulation method based on deep learning, which includes the following steps: Step (1) production of a data set; Step (2) proposing a custom loss function and using it for training; Step (3) training a UNet3+ network enhanced by introducing an ISSA module; Step (4) prediction of the optimal network model and result analysis. By adopting the above technical solution, heat simulation analysis and prediction can be carried out on a complex model with consistent topology, and a smooth and continuous simulation solution can be obtained quickly. On the premise of ensuring accuracy, the simulation time is greatly reduced, which is applicable to the scenario of real-time heat simulation analysis where the shape of the CAD model changes continuously. Compared with PINNs, which have been studied more in recent years, the network model in the present invention only takes 0.013 s to carry out heat simulation analysis and prediction on a new CAD model with the same topology; while the network model in PINNs needs to be trained for about 44 minutes to achieve the same accuracy as the present invention for a new CAD model.

Provided is an intelligent security system, which generates a more specific query about a situation in which video is captured and query to generative AI to be able to reduce ambiguity in result analysis different from intent, thereby more accurately identifying a security event.

The present invention discloses a method for predicting and scheduling operation duration based on machine learning, which belongs to the technical field of medical care informatics and comprises the following steps: obtaining historical processing data of a platform of the same operation type as the predicted operation type, and analyzing to obtain a historical processing difficulty index; analyzing to obtain a resource consumption index, and comprehensively analyzing to obtain an emergency judgment result of operation duration prediction; analyzing to obtain a data transmission channel effect index, and obtaining a final data segmentation judgment result based on a preliminary judgment result of data segmentation and a data transmission channel effect index analysis, and performing corresponding data processing to obtain a feature data packet; selecting a corresponding data transmission protocol based on the emergency judgment result of the operation duration prediction, thereby transmitting the feature data packet to an operation duration prediction module; the operation duration prediction module receives the feature data packet, and uses a machine learning algorithm to analyze to obtain an operation duration prediction value, and performs automatic scheduling prompts, thereby solving the problem of slow response of operation duration prediction in the prior art.

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 discloses a clinical test inspection and inspection automatic operation system which comprises a task management end, a data collection and recording end, a data quality control end, a result analysis and report end and a communication and cooperation end. The task management end, the data collection and recording end, the data quality control end, the result analysis and report end and the communication and cooperation end are in data communication. The system can automatically manage and process information of examination and inspection data in clinical tests, can effectively coordinate and optimize the examination and inspection process, and improves the accuracy, consistency and efficiency of the data.

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.

The invention provides a pregnancy outcome analysis method and device based on low-molecular heparin machine learning, and the method comprises the steps: obtaining various information of a target patient, and the various information comprises the basic information of pregnant and lying-in women, the pregnancy condition information and the drug exposure information; based on a pre-trained target pregnancy outcome analysis model, according to the various information of the target patient, determining various pregnancy abnormity risks of the target patient; and according to the multiple pregnancy abnormity risks of the target patient, adjusting and early warning medication safety of the target patient. The pregnancy risk of the patient can be predicted at least according to various information of the patient, and the medication safety of the patient can be adjusted and early warned according to the prediction.

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.