55 results about "Demographic statistics" patented technology

The invention relates to the technical field of medical systems, in particular to a multi-disease fusion prediction method and system, and the method comprises the steps: obtaining a medical sequence code, and outputting a disease vector; obtaining a gating memory model, obtaining a doctor-seeing time interval, and configuring an additional time gate based on the doctor-seeing time interval and the disease vector; updating the gated memory model based on a time gate to obtain a health memory state and a context disease hiding state; aggregating the context disease hiding states to obtain aggregation states, calculating attention scores corresponding to the aggregation states, and outputting patient disease states based on the attention scores and the aggregation states; demographic statistical characteristics are obtained, and integration is carried out in combination with the disease state of the patient to obtain a combined vector; and mapping the combined vector to disease risk probability distribution to obtain a prediction result. The method and the device have the effect of predicting various diseases through the general data.

A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools.

The invention provides a lower respiratory tract hospital infection risk prediction method and system based on deep learning, and relates to the field of medical electronic medical record time sequence data analysis processing and deep learning. According to the method, two diagonal masking self-attention mechanism blocks are utilized to carry out missing value filling on electronic medical record medical time sequence data; processing the demographic data in the electronic medical record by using a full-connection layer neural network, and initializing the hidden state of a decoder; constructing an encoder-decoder model based on an attention mechanism, performing model training by using the filled time sequence data and the hidden state of the initialized decoder, and constructing a lower respiratory tract hospital infection risk prediction model; and inputting the processed data into the trained lower respiratory tract hospital infection risk prediction model, and outputting a lower respiratory tract hospital infection risk prediction result of the patient. According to the method, mass data of the electronic medical record can be effectively utilized, the static characteristics and the dynamic time sequence characteristics are fused, the health state of the patient is captured, and the prediction accuracy and effectiveness are improved.

Techniques for augmentation and batch balancing of training data to enhance negation and fairness of a machine learning model. In one particular aspect, a method is provided that includes generating a list of demographic words associated with a demographic group, searching an unlabeled corpus of text to identify unlabeled examples in a target domain comprising at least one demographic word from the list of demographic words, rewriting the unlabeled examples to create one or more versions of each of the unlabeled examples and generate a fairness invariance data set, and training the machine learning model using unlabeled examples from the fairness invariance data set.

In some implementations, an education system may receive demographic information and account information associated with a user. The education system may generate a risk profile based on the demographic information and the account information. The education system may map the risk profile to at least one threat, out of a plurality of possible threats indicated in a data structure, likely to be targeted to the user. The education system may transmit, to a user device, an educational message that is associated with the at least one threat and that is indicated in the data structure.

A method for providing information about the possibility of abdominal aortic aneurysm occurrence according to an embodiment of the present invention comprises the steps of: receiving an input of a plurality of pieces of demographic information and personal clinical information; generating an abdominal aortic aneurysm occurrence prediction model by performing training on the basis of the demographic information and the personal clinical information; inputting demographic information and personal clinical information about a subject to the abdominal aortic aneurysm occurrence prediction model; and outputting information about the possibility of abdominal aortic aneurysm occurrence in the subject from the abdominal aortic aneurysm occurrence prediction model.

A method includes: receiving product data associated with a product; identifying at least one brand characteristic of a brand associated with the product, based on the product data; identifying at least one attribute of the product based on the product data; generating an attribute insight output based on at least the at least one attribute of the product; receiving demographic data associated with the product; generating a demographic insight output based on at least the demographic data associated with the product; receiving a suggested strategy output, wherein the suggested strategy output is generated based on, at least, the attribute insight output and the demographic insight output; generating a content brief output based on the suggested strategy output; and providing, at a display, an actionable output that includes at least the content brief output.

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.

A method for creating a personalized immersive safety simulator includes collecting demographic information relating to a first individual, identifying existing information relating to a work environment of the first individual, and combining the demographic information and the existing information into a semantic model for predicting risk to individuals in the work environment. Training scenarios displayable in an augmented reality or virtual reality device may be created using the combined information. The semantic model may be queried based on the individual and a real-world incident and near miss report related to the work environment or information in a safety protocol or safety standard. The semantic model may be enhanced by integrating information about high-risk zones, high-risk activities, and high-risk demographics into the semantic model, the integrated information being unique to the work environment associated with the semantic model. The semantic model may be updated based on observed tendencies or scene changes.

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.

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.

Methods and apparatus to determine probabilistic media viewing metrics are disclosed herein. An example apparatus includes memory including machine reachable instructions; and processor circuitry to execute the instructions to calculate a first probability for respective ones of a plurality of panelists as having viewed media based on viewing data, the viewing data including incomplete viewing data for one or more of the panelists relative to the media; identify respective ones of a plurality of panelists as included in a demographic subgroup based on demographic data for the panelists; assign a sampling weight to the respective ones of the plurality of panelists based on the demographic data; and calculate a second probability of the demographic subgroup having viewed the media based on the first probabilities and the sampling weights for the respective ones of the plurality of panelists in the demographic subgroup.

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 present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file and converting that data into autonomous data of an export entity file. In an embodiment, the received tabular data is sorted based on a lowest number of unique entities of a certain type in the revised data file, common data describing the lowest number of unique entities is extracted, labeled, and stored as an autonomous export entity file. In an embodiment a plurality of autonomous export entities are linked to create a master export entity comprising the plurality of autonomous export entities.

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 present disclosure is directed to systems and methods for identifying demographic information in a data file. The method may include: receiving the data file containing a plurality of fields of demographic information from a third-party, the data file having inconsistent or mislabeled nomenclatures for one or more fields of the plurality of fields or spurious demographic information; analyzing the data file using a machine learning model trained according to other data files to distinguish between each of the plurality of fields of demographic information, the machine learning model being based on a plurality of machine learning algorithms to identify different types demographic information; generating a score indicating a probability that each of the plurality of fields of demographic information was identified correctly; and generating a revised data file labeling each of the plurality of fields of demographic information based on the identified type.

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 present application discloses a disease prediction method, apparatus, storage medium, device and program product, which can be applied to scenarios such as artificial intelligence, machine learning, and medical treatment. The method includes: obtaining historical disease information and demographic information of the target object; determining a basic intensity that characterizes that the target object suffers from the target disease based on the demographic information; determining a time-varying conditional intensity that characterizes that the target object suffers from the target disease at a target time based on the historical disease information; and determining a predicted intensity that the target object suffers from the target disease at a target time based on the basic intensity and the time-varying conditional intensity. The embodiment of the present application determines the fixed intensity that the target object suffers from the target disease based on the demographic information, and determines the time-varying intensity that the target object suffers from the target disease at the target time based on the historical disease information, so that the intensity of the target object suffering from the target disease at the target time can be predicted, so that the disease of the target object can be accurately predicted.

An apparatus and method of generating personalized HRTFs. The system is prepared by calculating a model for HRTFs described as the relationship between a finite example set of input data, namely anthropometric measures and demographic information for a set of individuals, and a corresponding set of output data, namely HRTFs numerically simulated using a high-resolution database of 3D scans of the same set of individuals. At the time of use, the system queries the user for their demographic information, and then from a series of images of the user, the system detects and measures various anthropometric characteristics. The system then applies the prepared model to the anthropometric and demographic data as part of generating a personalized HRTF. In this manner, the personalized HRTF can be generated with more convenience than by performing a high-resolution scan or an acoustic measurement of the user, and with less computational complexity than by numerically simulating their HRTF.

The present invention discloses a mortality prediction method and computer program product based on the fusion of medical ontology embedding and an attention mechanism, relating to the field of medical information technology. The method comprises: performing feature mapping on a patient's demographic information, diagnosis information, treatment information, and vital sign information; constructing a medical ontology for each of the diagnosis information nodes and treatment information nodes in the feature mapping; performing a nonlinear transformation on the diagnosis ontology embedding matrix and the treatment ontology embedding matrix, and processing the diagnosis and treatment information using a masked attention mechanism; obtaining corresponding treatment outcomes based on the patient's medical visit records, learning the causal relationship between diagnosis and treatment, calculating the conditional probability between the patient's diagnosis and treatment outcomes, and obtaining an adjacency matrix; and predicting the patient's mortality rate based on the demographic information and vital sign information in the feature mapping, the diagnosis sequence, the treatment sequence, and the adjacency matrix. The present invention can effectively improve the accuracy of mortality prediction.

The invention provides a prediction classification model for hospital admission after current examination, and the model is based on electronic health record data, comprises demographic statistical information, diagnosis records, laboratory detection and input / output event multi-source heterogeneous information, and constructs a structured feature matrix after missing value processing, feature screening, standardization and coding preprocessing steps. Multiple machine learning and deep learning algorithms such as logistic regression, random forest, XGBoost, LightGBM, LSTM and Transformer are adopted for training, the problem of class imbalance is solved through SMOTE, and the recognition capability of the model for high-risk samples is improved. And finally, selecting a model with optimal performance and deploying the model to a hospital information system to realize real-time prediction and early warning of the re-admission risk. The method is high in prediction precision, can effectively assist doctors in making hospital discharge management plans, optimizes medical resource configuration, and reduces the unplanned re-admission rate.

To provide a communication network facility layout that minimizes costs while providing high quality communication services.SOLUTION: A system includes means for inputting a specific prompt sentence into a generative AI model to deploy a network including base station equipment on the basis of data including geographical information, demographic statistics, urban planning, and demand forecasts from around the world, anomaly detection means for detecting abnormalities in the base station equipment by periodically executing a self-diagnosis program for the base station equipment, means for activating an automatic repair function that automatically repairs the base station equipment in which the abnormality has been detected, and means for providing information necessary for understanding the current situation to maintenance staff who maintain and manage the base station equipment when the base station equipment cannot be repaired even by the automatic repair function.SELECTED DRAWING: Figure 1

The invention relates to a demographic method, device and equipment, a medium and a program product in the technical field of communication. The method comprises the following steps: according to a list set of discrete base stations, obtaining a first boundary vertex coordinate set of a minimum polygon capable of covering the discrete base stations, and obtaining a region polygon boundary; based on a polygon external expansion algorithm, performing external expansion on the region polygon boundary to obtain a second polygon boundary vertex coordinate set; eliminating boundary intersection points to obtain a third polygon boundary vertex coordinate set; and according to the boundary vertex coordinate set of the third polygon, obtaining the number of users in a specified base station coverage area through two-stage filtering of a spatial index algorithm and combination of real-time user position data. According to the invention, the area boundary generation method is proposed for the area of a known base station list, spatial index calculation is introduced, and the number of users in the specified base station coverage area is obtained. Precise regional population recognition is realized, and the calculation efficiency is improved.

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.

The present invention discloses a method and device for debiasing a deep learning model based on adding noise to sensitive features. First, the CelebA dataset is used as the initial sample set and data preprocessing is performed; then, a bias metric function is defined based on the metrics of differential impact, demographic parity, and equality of opportunity; then, a sensitive feature extraction model is constructed and trained using a warm-up learning rate strategy; then, the noise-adding regions are divided and random noise is added to the sensitive features; finally, a deep learning model is constructed and the deep learning model is trained according to the defined bias metric function until the preset accuracy and bias metric are reached, at which point the training is ended and the debiasing is completed.

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 invention discloses a feature dynamic screening-based secondary blood index data set generation method and system, and the method comprises the steps: collecting basic information and basic blood indexes of a research object, and carrying out the preprocessing, thereby obtaining an effective sample; dividing into a chronic non-infectious disease-free control group and a target disease group, and carrying out demographic characteristic statistical difference analysis; calculating secondary blood indexes according to the basic blood indexes, and integrating the screened demographic characteristics to generate an enhanced data set; the method comprises the following steps: dividing an enhanced data set into a training set and a test set according to stratified sampling of disease states, synchronously standardizing continuous indexes of the training set and the test set, then screening an optimized feature set with a nonzero coefficient through an adaptive elastic network based on stability selection, and finally performing feature synchronous filtering and integration to form a final data set. According to the method, the problem of model selection difficulty caused by low quality, non-uniform processing flow and feature redundancy of the existing blood multi-dimensional index data set can be solved.