90 results about "Outcome analysis" patented technology

The invention discloses a medical adverse event risk prediction, prevention and control method based on causal inference. The method comprises the following steps: S1, collecting and preprocessing multi-modal medical data; s2, constructing a causal network through a time slicing technology; s3, introducing ethical factors to form an ethical causal network; s4, generating a causal enhancement vector; s5, constructing a dynamic risk prediction model, and outputting an evaluation result; s6, analyzing a path contribution degree, and screening high-risk paths and nodes; s7, generating a personalized prevention and control scheme; and S8, generating a transparent report, and displaying the transparent report to the medical personnel and the patient. According to the method, the dynamic causal relationship among the medical variables is accurately modeled through the dynamic causal network, the multi-modal data fusion and the ethical correction technology, the personalized prevention and control scheme is generated in combination with the optimization algorithm, comprehensive support is provided for medical decision making, and implementation of precise medical treatment is promoted.

The invention discloses a health degree assessment management system and method based on equipment state monitoring, and relates to the technical field of industrial equipment monitoring and intelligent health assessment, and the system comprises a temperature rise data acquisition module which is used for obtaining real-time temperature rise data in an equipment operation process and comparing the real-time temperature rise data with a pre-stored standard temperature rise curve, and a data analysis module which is used for analyzing the real-time temperature rise data and comparing the real-time temperature rise data with the pre-stored standard temperature rise curve. The system comprises a temperature rise curve comparison module for analyzing the change trend of a temperature rise curve according to a comparison result and identifying abnormal temperature rise characteristics, an early warning adjustment module for dynamically adjusting an early warning threshold according to the identified abnormal temperature rise characteristics so as to early warn risks in advance, and an early warning trigger module for carrying out equipment real-time early warning based on the dynamically adjusted early warning threshold. According to the health degree assessment management system and method based on equipment state monitoring, the change trend of a temperature rise curve during equipment operation is dynamically identified so as to solve the problem of overheating early warning delay.

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.

A large model agent generation content quality evaluation method based on double baselines comprises the steps that data generated by human beings is acquired, preprocessing and semantic segmentation are carried out, and data samples after semantic segmentation are formed; performing theme extraction on the data samples after data segmentation to form a theme list; the content of the data forming the theme list is generated through an intelligent agent workflow and the content of the data forming the theme list is directly generated through a large model, and an evaluated sample and a contrast sample are obtained; comparing and scoring the data sample after semantic segmentation, the evaluated sample and the contrast sample; selecting a sorting algorithm to carry out score conversion on the graded and compared samples; and performing statistical verification and result analysis. According to the method, a comprehensive, reliable and efficient solution is provided for quality evaluation of the generation content of the large model agent. The method not only can accurately evaluate the content quality, but also provides powerful support for continuous optimization of an intelligent agent system.

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 physical examination quality control system. The quality control system is in interactive connection with a mobile terminal through the Internet; the quality control system comprises a quality control module and a data management and analysis module. The quality control module comprises a first-level quality control module, a second-level quality control module and a third-level quality control module. The primary quality control module comprises a problem unit, an analysis unit and a marking unit; the secondary quality control module comprises a quality control spot check unit, a quality control table unit and a rectification unit; the third-level quality control module comprises a quality control summary conference management unit, a conference content unit and a tracking evaluation unit; and the data management and analysis module provides a mobile terminal with query, statistics and analysis permissions to perform query, quantity statistics and query result analysis according to the screening conditions. According to the invention, comprehensive quality control of multiple inventions is realized, omissions and errors in the service process are reduced, and high quality and safety of medical services are ensured.

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 diagnosis and treatment data processing, and particularly discloses a clinical examination result analysis method based on a medical knowledge graph, and the method comprises the steps: collecting historical clinical examination data of patients with different target diseases in an electronic medical record system; screening and extracting a disease keyword text sequence of the target disease according to the historical clinical examination data; constructing an ontology model based on the medical knowledge mapping relationship in the database, inputting the disease keyword text sequence into the ontology model to construct a medical knowledge graph model of the target disease, and outputting a plurality of disease keyword parameters; acquiring clinical manifestation information of target diseases of different patients in real time, extracting a knowledge graph of current clinical examination data of the patients and illness state trend values corresponding to the illness state keyword parameters according to the illness state keyword parameters, judging whether the illness states of the patients are abnormal or not, generating illness state evaluation parameters and outputting clinical examination scheme results of the patients; the accuracy of clinical examination results is improved; and the medical detection investment cost is reduced.

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 relates to the technical field of model data processing, and discloses a data processing method based on artificial intelligence modeling, and the method comprises the steps: data preprocessing: removing low-information-content features, and standardizing input data; feature screening and optimization: constructing a feature association network by using mutual information, removing redundant features through a spectrum segmentation algorithm, and optimizing feature distribution; modeling and evaluation: completing model construction, training and performance evaluation based on a sparse neural network, and generating feature contribution rate analysis; performing task scheduling and feedback, realizing automatic scheduling of data processing tasks through a time expression, and performing closed-loop management on processing results; and performing result analysis and optimization decision, performing deep analysis on the evaluation result and the feature contribution rate, and generating an optimization suggestion. According to the method, full-process automation of data processing is realized, the efficiency, the performance and the interpretability are improved, the problems of insufficient feature optimization, high modeling complexity, shallow result analysis and the like are solved, and the method is suitable for various data analysis scenes.

The invention discloses a method for unifying an InSAR reference framework by using a GNSS and decomposing a velocity field, which comprises the following steps: (1) modeling a GNSS north-south velocity field based on a multi-scale spherical wavelet method, and deducting the contribution of the modeling velocity field in the sight line direction from an InSAR deformation field; (2) directly resolving east-west direction and vertical direction deformation fields by using an InSAR lifting rail sight direction velocity field; (3) establishing a quadratic polynomial function to carry out difference fitting so as to remove the influence of orbit errors and a reference frame; and (4) carrying out result analysis and verification in combination with cross comparison of ground measurement data. According to the method, the InSAR sight direction velocity field is decomposed in the east-west direction and the vertical direction by considering the contribution of north-south deformation, the reference framework of the calculated velocity field is unified by using GNSS data, so that more real deformation information is obtained, the data accuracy is ensured through multi-source data cross validation, and reliable data support can be provided.

The invention relates to the technical field of medical image processing and radiotherapy, in particular to a cervical cancer internal and external irradiation dose superposition system based on deep learning, which comprises a data input module, a deformation registration module, a dose calculation module, a dose superposition module and a result analysis module. The data input module verifies and preprocesses internal and external irradiation CT image data; the deformation registration module adopts a V-Net framework to be combined with an algorithm based on Transform to accurately align images; the dose calculation module calculates dose distribution by using a convolutional neural network; the dose superposition module performs weighted superposition on the internal and external irradiation doses to generate a comprehensive dose volume histogram; and the result analysis module evaluates the dose conditions of the target region and the normal tissue by using statistical and visual tools. According to the method, the accuracy of dose-related operation is improved, the working efficiency is improved, personalized precise radiotherapy is assisted, and the method has positive significance in promoting development and clinical application of the radiotherapy technology.

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 relates to the technical field of hospital integrated management, in particular to a hospital integrated service platform system for performance appraisal, which comprises a data quality control module, a simulation appraisal module and an appraisal result analysis module, the data quality control module is used for performing format verification and logic consistency verification on the data uploaded by the hospital, performing abnormal data detection and data integrity check on the uploaded data, correcting the data and then feeding back a data quality control result; the simulation assessment module is used for carrying out simulation assessment according to the formulated performance assessment indexes and scoring rules after the data quality meets the requirements, carrying out assessment scoring based on assessment index data and outputting an assessment result; and the assessment result analysis module is used for carrying out index and historical trend analysis on the assessment result of the hospital after the simulation assessment is completed, and finding out problems in hospital operation. Rapid and accurate simulation examination is carried out on the hospital, and the examination result is output, so that the examination efficiency is improved.

The invention discloses a method for checking distributed supporting stand columns between a cylindrical FPSO main deck and a process deck. The method for checking the distributed supporting stand columns between the cylindrical FPSO main deck and the process deck comprises the following checking methods that S1, a finite element calculation software model is established; s2, applying a load on the finite element model; s3, module supports are established and arranged, and stand columns are arranged between the main deck and the process deck to serve as supports; s4, pre-analyzing the finite element model; s5, performing finite element model optimization analysis; s6, the screened stand columns are subjected to refining treatment; and S7, performing result analysis on the model, and returning to S6 for adjustment if the overall structural strength meets the requirements of operation, extreme and cabin breaking working conditions, the structural fatigue exceeds the specified service life and the local part does not meet the requirements. According to the checking method for the distributed supporting stand columns between the cylindrical FPSO main deck and the process deck, the calculation result is more accurate, the professional iteration workload of upstream and downstream is reduced, the design period is shortened, and the efficiency is improved.

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 an illness condition outcome prediction method and device based on a gating adaptive mechanism. The method comprises the following steps: acquiring chronic disease related data, performing feature correlation analysis according to the chronic disease related data, generating graph data, and performing feature enhancement on the graph data; based on the graph data, utilizing a multi-layer GAT hybrid model to carry out prediction, and obtaining a time sequence of a transition state; wherein the multi-layer GAT hybrid model comprises a plurality of GATConv layers, a plurality of LSTMAggregation layers and a feature fusion layer based on gating self-adaption; and outputting the time sequence of the conversion state as a conversion matrix by using a Grubm angle field. According to the method, graph-level global feature fusion is carried out through a gating mechanism and an attention mechanism, so that the multi-layer GAT hybrid model has a strong graph feature extraction capability, and the precision and accuracy of power prediction can be effectively improved. Meanwhile, the multi-layer GAT hybrid model is used for carrying out outcome prediction on each data point of each patient, and then a Gream angle field is adopted to generate an outcome matrix, so that further outcome analysis is facilitated.

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 invention relates to the technical field of measurement economics analysis models, and discloses a measurement economics analysis model and a measurement analysis method, and the system comprises a data collection and management module which is used for collecting economic data from a plurality of data sources; the model construction and selection module is used for providing various models; the parameter estimation and optimization module estimates model parameters by using an advanced algorithm; the result analysis and visualization module is used for carrying out detailed statistical analysis on an estimation result of the model; and the risk assessment and early warning module is used for assessing data and model risks in measurement analysis. According to the invention, the data acquisition and management module provides a high-quality data basis for other modules and improves the accuracy and reliability of the model, and the model structure and type determined by the model construction and selection module and optimized parameters can present more valuable results in the result analysis and visualization module.

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.