22 results about "Demographic statistics" patented technology

The invention relates to the technical field of data processing, and discloses a dynamic cognitive training method and system based on a large language model and multi-dimensional evaluation, which combines multi-dimensional input data such as standardized cognitive evaluation, demographic statistics, historical behaviors and real-time performance to construct a user cognitive portrait. The dynamic prompting engineering module and the content generation process of the large language model are integrated, so that the training content, the prompting mode, the interaction style and the like can deeply meet the individual differences and specific requirements of users, the fine-grained and deep personalized training process is provided, and through the dynamic prompting engineering module, the user experience is improved. According to the method, the fine cognitive state change of the user in each interaction and task can be captured in real time, the change is immediately converted into accurate control over large language model behaviors, training content and difficulty which are accurately matched with the current level of the user are generated, highly-dynamic and real-time adaptive cognitive training can be achieved, and the user experience is improved. The problems that a traditional cognitive training system is lagged in adjustment and insufficient in roughness are solved.

This invention provides a method for predicting individual mortality rates based on activity data from wearable devices, comprising the following steps: acquiring multi-source characteristic data of the target individual, wherein the multi-source characteristic data includes at least activity data and baseline characteristic data, the activity data being collected by the wearable device worn by the target individual over a continuous monitoring period, and the baseline characteristic data including at least one of demographic characteristics, health characteristics, living environment characteristics, and lifestyle characteristics; processing the activity data to calculate an average activity data feature value representing the overall activity level of the target individual; inputting the average activity data feature value and the baseline characteristic data together into a trained nonlinear machine learning prediction model, and the prediction model outputting a predicted mortality risk value for the target individual within a preset future time window. This invention integrates the average activity data feature value with traditional baseline characteristic data, enabling risk assessment to be updated synchronously with an individual's daily activity level.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analyzed attributes for each unit sub-region to a predetermined value.

The invention provides a crowd health-oriented environment risk dynamic regulation and control method and a storage medium, relates to the field of public health, and systematically integrates macroscopic environment monitoring and demographic statistics data, multi-pollutant air quality health indexes and individual-level longitudinal queue data. A multi-pollutant air quality health index is used as priori knowledge through a constructed fusion module, an individual-level causal model is processed, a processed health risk function is coupled with a pollution control marginal cost function and refined health economic evaluation parameters, and a comprehensive optimization function is constructed. And performing probabilistic representation and propagation on uncertainty of all key parameters in the target function, finally solving to obtain a control threshold range with a confidence interval, re-substituting the optimized control threshold into the multi-pollutant air quality health index calculation framework, evaluating the risk level change of the multi-pollutant air quality health index calculation framework under a historical reference, and calculating the multi-pollutant air quality health index. And an empirical support from historical data is provided for the rationality and feasibility of an optimization result.

The invention relates to a time-space diagram neural network autism classification method based on dynamic function connection and dynamic effective connection feature fusion, which can realize accurate classification of autism by using resting state functional magnetic resonance imaging data. The method comprises the following steps: firstly, constructing a dynamic function connection matrix and a dynamic effective connection matrix to respectively extract brain network diagram characteristics, and under the guidance of a dynamic effective connection network, forming fused brain network space-time connection characteristics by adopting a space-time fusion position Transform based on a cross attention mechanism; introducing a multi-layer perceptron to extract high-order image features in the brain network, embedding the high-order image features as node representation of a population graph, constructing edges of the population graph by using demographic information, realizing fusion of the magnetic resonance image features and the demographic information, and finally learning node embedding through a graph convolutional network to obtain the demographic information of the population graph. And autism classification is realized based on a linear classifier. Experimental results show that the provided method has excellent performance in autism diagnosis tasks, and the accuracy and robustness of diagnosis are remarkably improved.

A method for managing computer resource use in computer-implemented risk assessment of a subject in respect of a context. Demographic subject data for the subject is passed to a first trained machine learning model trained on first context-specific historical risk outcomes correlated with historical demographic data corresponding to the demographic subject data. If a first threshold assessment from the first trained machine learning model is passed, the subject is approved. Responsive to failing the first threshold assessment, supplemental context-related subject data for the subject, in addition to the demographic subject data, is passed with the demographic subject data to a second trained machine learning model trained on second context-specific historical risk outcomes correlated with the historical demographic data and with historical context-related data corresponding to the supplemental context-related subject data. If a second threshold assessment from the second trained machine learning model is passed, the subject is approved.

The invention discloses a chronic disease patient portrait generation method and system based on big data, and relates to the technical field of data processing, and the method comprises the following steps: screening target data, obtaining stage data of a to-be-analyzed disease, analyzing to obtain a first patient feature and a second patient feature, constructing a patient portrait, and compensating the patient portrait. Static demographic characteristics, dynamic behaviors and disease course association are included at the same time, the one-sidedness problem that traditional portraits only pay attention to demographic statistics or test indexes is solved, intervening factors are quantified into the portraits through a living habit regression equation, the portraits have intervention and guidance values, stage data are used as a minimum analysis unit, and the accuracy of the portraits is improved. Redundant calculation of full-amount long-period data is avoided, stage data naturally supports sliding window refreshing, portraits can be iterated weekly and monthly, and the chronic disease long-period management requirement is met.

This invention provides an Alzheimer's disease risk assessment system, comprising: a data acquisition module for acquiring test data of subjects, the test data including at least the concentration of Aβ oligomers, APOEε4 gene carrier status data, and multi-domain risk factor data; the multi-domain risk factor data including demographic indicators and clinical data related to Alzheimer's disease risk; a risk assessment module, communicatively connected to the data acquisition module, for taking the test data from the data acquisition module as input, and outputting a risk probability value of the subject having Alzheimer's disease through a pre-trained machine learning model; and a result output module, communicatively connected to the risk assessment module, for comparing the risk probability value with a preset threshold and outputting risk classification information based on the comparison result. This invention is the first to achieve high-precision and high-sensitivity clinical cognitive impairment screening by fusing multidimensional conventional data through a nonlinear model.

The application discloses an Alzheimer's disease risk prediction system and method, and belongs to the field of biomedical detection and artificial intelligence technology, and the system comprises: a multi-modal data acquisition module for acquiring biological samples, images, voices and demographic information; a biomarker detection module for detecting the expression levels of p-tau217 and GFAP; an image analysis module for extracting fat metabolism parameters in PET / CT images; a voice feature extraction module for extracting acoustic features; and a risk prediction module with an integrated prediction model combining multiple machine learning models, which takes the above multi-modal data as input variables, comprehensively analyzes and outputs risk probabilities. The application combines blood biomarkers, image metabolism parameters, voice acoustic features and demographic information, and through multi-dimensional data complementation and correction, the accuracy, convenience and reliability of early risk prediction of Alzheimer's disease are significantly improved.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

An apparatus comprises a communication interface to send a communication to a first client device in response to a first request from the first client device, the communication to cause the first client device to send a first identifier to a database proprietor in association with a cookie identifier of the first client device, the first identifier corresponding to media accessed by the first client device and a data structure to: log a first impression in association with the first identifier corresponding to the media accessed by the first client device; and log second impressions associated with the first identifier and corresponding to access to the media by second client devices, the second impressions logged in response to second requests from the second client devices. The apparatus further comprises a processor to generate a report based on (a) the first impression and corresponding first demographic information from the database proprietor, and (b) the second impressions and corresponding second demographic information from an audience measurement entity different from the database proprietor. 16182084_1 20 24 22 75 29 22 O ct 2 02 4 M E T H O D S A N D A P P A R A T U S T O D E T E R M I N E I M P R E S S I O N S U S I N G D I S T R I B U T E D D E M O G R A P H I C I N F O R M A T I O N 2 0 2 4 2 2 7 5 2 9 2 2 O c t 2 0 2 4 A B S T R A C T

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analysis target attributes for each unit sub-region to a predetermined value.

Disclosed herein are system, method, and computer program product aspects for managing and recommending changes of demographic information. An example implementation may operate by receiving a data file containing a plurality of changes associated with one or more fields of one or more entries of demographic information. The implementation may then aggregate the data file based on identifying the plurality of changes in the one or more fields of demographic information to generate an aggregated data file. The implementation may then generate a user interface to request a user to provide a user preference. The implementation may then compare a level of aggressiveness provided by the user to respective scores associated with one or more changes within the one or more entries of the aggregated data file to identify a subset of the one or more changes to apply. The implementation may then apply the subset of the one or more changes to the data file to generate an updated data file. The implementation may then export, in the user interface, the updated data file to the user. The implementation may then update a plurality of scores associated with a plurality of updated changes to be applied in the updated data file.

A system arranged to calculate one or more malnutrition indicators for an individual human being comprises processing circuitry arranged to: receive information about the individual, the information comprising demographic information of the individual, and at least two unique photographs of the individual (including a front-view and a side-view); extract measurements indicative of body shape from the photographs; generate a 3D model of the individual using the photographs; retrieve information from a body composition or health database, the database comprising internal body composition data and corresponding body shape and demographic data, the retrieved information being selected based on identifying a subset of data in the database based on the demographic information, and matching the measurements extracted from the photographs to body shape data in the identified subset of data; and determine a value for the malnutrition indicator by combining the extracted measurements with the retrieved information from the database..

In some aspects, a computing system may use a surrogate machine learning model to detect whether a production or other machine learning model has a tendency to generate different output depending on which subpopulation a particular sample belongs to. The surrogate machine learning model may be trained using features / outputs that are not included in the data used by the production model. For example, by using demographic information in lieu of the original labels of a dataset that was used to train a production model, a surrogate model may be used to detect whether the production model is able to discern one or more characteristics associated with but not present in a sample using other features of the dataset. Output of the surrogate machine learning model may be clustered to detect whether certain subpopulations are treated differently by the production model.

The present application relates to a kind of infantile asthma three branch classification auxiliary judgment method and medium, it is related to data processing technical field.The method includes: the tidal breathing data and demographic data of infant are collected and preprocessed, the tidal breathing data and demographic data after preprocessing are extracted and grouped log likelihood ratio, obtain the log likelihood ratio corresponding to each group;Each log likelihood ratio is fused by hierarchical bayes, and the fusion result is obtained, and the fusion result is based on three branch classification auxiliary judgment of asthma risk.Compared with prior art, infantile asthma can be more objectively and accurately based on tidal breathing data auxiliary judgment.

This invention discloses a severe depression assessment model based on cloud-edge collaboration and knowledge-guided cross-comparative learning, belonging to the field of intelligent medical technology. The model employs a cloud-edge collaborative architecture, acquiring fMRI data and demographic information at the edge, combining this with AAL and Harvard brain atlases to extract ROI time series, modeling using Pearson correlation coefficients, conducting inter-group t-tests for significance analysis, and constructing a functional connectivity graph through feature fusion before uploading it to the cloud. On the cloud, based on multi-view brain map hierarchical analysis, it integrates graph attention mechanisms, default mode network medical prior knowledge, and cross-comparative learning to complete the training and optimization of graph attention, subgraph generation, and the prediction network. Then, feature extraction, knowledge-guided pruning, and cross-view fusion are performed on the functional connectivity graph to be tested, and the depression assessment result is obtained through inference by the prediction network. This invention shortens transmission time, reduces bandwidth pressure, avoids privacy leakage risks, and improves assessment accuracy.

Particular example embodiments described herein can provide for a system, an apparatus, and a method for collecting household income and expenditures data for a country, extending the household income and expenditures data for the country for each year in a period of time, collecting distribution demographic data for the country, extending the distribution demographic data for the country for each year in the period of time, and merging the household income and expenditures data and the distribution demographic data for the country to create a report determining and forecasting demographic expenditures on one or more categories of goods and / or services for the country.

A method of identifying a principal investigator for a clinical trial includes identifying candidate principal investigators based on similarity of past clinical trials of each of the candidate principal investigators to the clinical trial. The method also includes accessing open-source information to identify patients associated with each of the candidate principal investigators, and recording demographic information for the patients associated with each of the candidate principal investigators. A principal investigator is identified for the clinical trial based on a match between the demographic information for the patients associated with each of the candidate principal investigators and demographic requirements defined for the clinical trial.

Methods and apparatus to compensate impression data for misattribution and non-coverage are disclosed. An example method includes receiving a first request from a first type of computing device; sending a request for demographic information corresponding to requests received at a first internet domain from the first type of computing device; generating an aggregated audience distribution including a first audience distribution of a first household aggregated with a second audience distribution of a second household; normalizing the aggregated audience distribution to generate a misattribution correction matrix, the misattribution correction matrix including a probability that an impression of the media is attributable to a first demographic group when the database proprietor determines the impression corresponds to a person in a second demographic group; and compensating misattribution error in the impressions by re-assigning the impressions from the second demographic group to the first demographic group using the misattribution correction matrix.