1. A system for predicting disease risk in populations, comprising: a data storage device for storing demographic and health information of a population; a statistical model training module for training a statistical model using the demographic and health information; a disease risk prediction module for predicting the disease risk of the population based on the demographic and health information using the trained statistical model; and an output module for outputting the predicted disease risk for the population.

2. The system of claim 1, wherein the statistical model training module comprises a machine learning algorithm.

3. The system of claim 1, wherein the demographic and health information comprises genetic data of the population.

4. The system of claim 1, further comprising a user interface module for displaying the predicted disease risk to a user.

5. The system of claim 1, wherein the statistical model is trained using a longitudinal dataset to account for changes in the population over time.

6. The system of claim 1, wherein the disease risk prediction module further predicts the probability of disease onset within a predetermined time period.

7. The system of claim 1, wherein the output module further provides personalized recommendations to individuals based on their predicted disease risk.

8. The system of claim 1, wherein the statistical model training module further incorporates environmental factors and lifestyle choices into the model.

9. The system of claim 1, wherein the disease risk prediction module is updated in real-time as new data is added to the data storage device.

10. A method for predicting disease risk in populations, comprising the steps of: receiving input data comprising demographic and health information of a population; training a statistical model using the input data; using the trained statistical model to predict the disease risk of the population based on the demographic and health information; and outputting the predicted disease risk for the population Statistical Model for Predicting Disease Risk in Populations Abstract The present invention provides a system and method for predicting disease risk in populations. The system includes a data storage device for storing demographic and health information, a statistical model training module that utilizes machine learning algorithms to train a statistical model based on the demographic and health information, a disease risk prediction module that predicts disease risk using the trained statistical model, and an output module for outputting the predicted disease risk for the population. The system can incorporate genetic data, environmental factors, and lifestyle choices into the model and can be updated in real-time as new data is added to the data storage device. The method involves receiving input data, training a statistical model, predicting disease risk, and outputting the predicted disease risk. The system and method can improve disease prevention and management efforts by providing personalized recommendations based on the predicted disease risk, ultimately benefiting population health outcomes. , Claims:Claims :

1. A system for predicting disease risk in populations, comprising: a data storage device for storing demographic and health information of a population; a statistical model training module for training a statistical model using the demographic and health information; a disease risk prediction module for predicting the disease risk of the population based on the demographic and health information using the trained statistical model; and an output module for outputting the predicted disease risk for the population.

2. The system of claim 1, wherein the statistical model training module comprises a machine learning algorithm.

3. The system of claim 1, wherein the demographic and health information comprises genetic data of the population.

4. The system of claim 1, further comprising a user interface module for displaying the predicted disease risk to a user.

5. The system of claim 1, wherein the statistical model is trained using a longitudinal dataset to account for changes in the population over time.

6. The system of claim 1, wherein the disease risk prediction module further predicts the probability of disease onset within a predetermined time period.

7. The system of claim 1, wherein the output module further provides personalized recommendations to individuals based on their predicted disease risk.

8. The system of claim 1, wherein the statistical model training module further incorporates environmental factors and lifestyle choices into the model.

9. The system of claim 1, wherein the disease risk prediction module is updated in real-time as new data is added to the data storage device.

10. A method for predicting disease risk in populations, comprising the steps of: receiving input data comprising demographic and health information of a population; training a statistical model using the input data; using the trained statistical model to predict the disease risk of the population based on the demographic and health information; and outputting the predicted disease risk for the population

Description:Statistical Model for Predicting Disease Risk in Populations
Field of the Invention
[0001] The present invention relates generally to the field of healthcare and more specifically to a statistical model for predicting disease risk in populations.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Disease prevention is a critical aspect of healthcare, as it can improve patient outcomes and reduce healthcare costs. Early detection and intervention of diseases can lead to better treatment outcomes and improved quality of life for patients. Predictive models that can identify individuals at high risk of developing diseases can aid in early intervention and prevention of the disease. However, developing accurate predictive models for disease risk is a complex and challenging task that requires extensive data analysis and statistical modeling.
[0004] Traditional risk prediction models often use generic factors, such as age, gender, and family history, to predict disease risk in individuals. However, these models do not take into account the unique demographic and health characteristics of populations, leading to suboptimal predictions. To address this limitation, there is a need for a statistical model that can accurately predict disease risk in populations by incorporating demographic and health data specific to the population being studied.
[0005] The proposed statistical model for predicting disease risk in populations aims to incorporate a wide range of demographic and health data, such as genetic data, environmental factors, and lifestyle choices, to predict disease risk accurately. By analyzing large datasets and incorporating machine learning algorithms, this model can provide personalized risk predictions to individuals based on their unique demographic and health characteristics.
[0006] Moreover, the statistical model is trained using a longitudinal dataset to account for changes in the population over time, which is crucial for accurate predictions. Additionally, the output module of the system can provide personalized recommendations to individuals based on their predicted disease risk.
[0007] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[0008] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[0009] The present invention relates generally to the field of healthcare and more specifically to a statistical model for predicting disease risk in populations.
[00010] The present invention provides a system and method for predicting disease risk in populations. The system includes a data storage device for storing demographic and health information, a statistical model training module that uses machine learning algorithms to train a statistical model, a disease risk prediction module that predicts disease risk based on the demographic and health information using the trained statistical model, and an output module that outputs the predicted disease risk. The system can incorporate genetic data, environmental factors, and lifestyle choices into the model and can be updated in real-time as new data is added. The method involves receiving input data, training a statistical model, predicting disease risk, and outputting the predicted disease risk. The system and method can improve disease prevention and management efforts by providing personalized recommendations based on the predicted disease risk, ultimately benefiting population health outcomes.
[00011] An exemplary use case for the system could be in a healthcare organization or public health agency that wants to improve disease prevention and management efforts for a specific population. The data storage device could collect demographic and health information, such as medical history, genetic data, lifestyle choices, and environmental factors, for the population. The statistical model training module could use machine learning algorithms to train a statistical model based on this data, accounting for complex interactions between multiple risk factors. The disease risk prediction module could predict disease risk based on the demographic and health information using the trained statistical model, potentially including the probability of disease onset within a predetermined time period. The output module could output the predicted disease risk, potentially providing personalized recommendations to individuals based on their predicted disease risk.
[00012] Overall, the present invention provides a valuable tool for healthcare professionals and public health agencies to identify and manage disease risk in populations, ultimately improving population health outcomes.
Brief Description of the Drawings
[00013] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00014] FIG. 1 represents an exemplary architecture of system predicting disease risk in populations, according to some embodiments of the present disclosure.
[00015] FIG. 2 is a flowchart illustrating a method relates to prediciting diease riks factor among poluplation of specific geolocation, according to some embodiments of the present disclosure.
Detailed Description
[00016] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00017] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00018] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00019] The present invention relates generally to the field of healthcare and more specifically to a statistical model for predicting disease risk in populations.
[00020] The present invention relates to a system 100 for predicting disease risk in populations, comprising a data storage device 102, a statistical model training module 104, a disease risk prediction module 106, and an output module 108. This system aims to help public health organizations, researchers, and healthcare providers in developing preventive strategies, allocating resources, and reducing the burden of disease in populations.
[00021] In an embodiment, the data storage device is responsible for storing demographic and health information of a population. This information may include age, gender, ethnicity, genetic data, medical history, and environmental factors, among other relevant data points. The data storage device can be a centralized or distributed database, a cloud storage system, or any other suitable storage medium. The data storage device may also include data encryption and security measures to protect sensitive information and ensure compliance with data privacy regulations.
[00022] In an embodiment, the statistical model training module is responsible for training a statistical model using the demographic and health information stored in the data storage device. This module may employ various machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, or neural networks, depending on the specific characteristics of the data and the desired prediction accuracy. The training module may also incorporate cross-validation and hyperparameter tuning techniques to optimize the model's performance.
[00023] The statistical model training module can further include environmental factors and lifestyle choices into the model, such as air pollution levels, diet, exercise habits, and smoking status. This can provide a more comprehensive understanding of disease risk factors and their interplay in the population.
[00024] In an embodiment, the disease risk prediction module is responsible for predicting the disease risk of the population based on the demographic and health information using the trained statistical model. The module generates a risk score or probability of disease onset for each individual within the population, as well as aggregated risk measures for subpopulations or the entire population.
[00025] The disease risk prediction module may further predict the probability of disease onset within a predetermined time period, such as the risk of developing type 2 diabetes within the next five years. This can help public health officials prioritize interventions and monitor the effectiveness of preventive measures.
[00026] The disease risk prediction module may be updated in real-time as new data is added to the data storage device. This enables the system to adapt to changes in the population over time, providing more accurate and timely disease risk predictions.
[00027] In an embodiment, the output module is responsible for outputting the predicted disease risk for the population. This may include visualizations, reports, or data exports to facilitate communication of the results to public health officials, researchers, and healthcare providers. The output module may also provide personalized recommendations to individuals based on their predicted disease risk, such as lifestyle modifications, screening tests, or preventive medications.
[00028] The output module can further include a user interface module for displaying the predicted disease risk to a user. This user interface may be accessed through various devices, such as computers, smartphones, or tablets, and can be designed to accommodate different user roles, such as public health officials, researchers, healthcare providers, or individual members of the population.
[00029] In this embodiment, the system is designed to predict the risk of cardiovascular diseases, such as heart attack, stroke, or coronary artery disease, in a population. The statistical model training module may incorporate risk factors such as age, gender, blood pressure, cholesterol levels, smoking status, and genetic predispositions into the model. The disease risk prediction module generates individual and population-level risk scores, which can be used by public health officials to develop targeted interventions and allocate resources effectively.
[00030] In this embodiment, the system is tailored to predict the risk of type 2 diabetes in a population. The statistical model training module may consider risk factors such as age, body mass index (BMI), family history of diabetes, physical activity levels, and dietary habits in the model. The disease risk prediction module generates individual and population-level risk scores, which can be used by public health officials to develop targeted interventions, such as promoting healthy eating and exercise habits, and allocating resources effectively.
[00031] In this embodiment, the system is designed to predict the risk of various types of cancer, such as breast, lung, or colorectal cancer, in a population. The statistical model training module may incorporate risk factors such as age, gender, genetic mutations, family history of cancer, exposure to environmental carcinogens, and lifestyle factors (e.g., smoking, diet, and physical activity) into the model. The disease risk prediction module generates individual and population-level risk scores, which can be used by public health officials to develop targeted interventions, such as promoting cancer screening programs and reducing exposure to environmental carcinogens, and allocating resources effectively.
[00032] In this embodiment, the system is tailored to predict the risk of infectious diseases, such as influenza, COVID-19, or tuberculosis, in a population. The statistical model training module may consider risk factors such as age, vaccination status, underlying health conditions, population density, and social mixing patterns in the model. The disease risk prediction module generates individual and population-level risk scores, which can be used by public health officials to develop targeted interventions, such as vaccination campaigns, social distancing measures, and resource allocation for healthcare infrastructure.
[00033] These embodiments demonstrate the versatility of the system for predicting disease risk in populations across various disease types and populations. The system can be adapted to address specific public health challenges, providing valuable insights for public health officials, researchers, and healthcare providers in their efforts to reduce the burden of disease and improve population health. The real-time updating capabilities and the incorporation of various risk factors into the statistical model ensure accurate and timely disease risk predictions, enhancing the effectiveness of preventive strategies and resource allocation decisions.
[00034] In this embodiment, the statistical model training module employs machine learning algorithms to create predictive models based on demographic and health information. The algorithms can include supervised learning methods, such as linear regression, logistic regression, support vector machines, decision trees, random forests, and neural networks. The choice of the algorithm may depend on the specific characteristics of the data and the desired prediction accuracy. The training module may also incorporate techniques such as cross-validation and hyperparameter tuning to optimize the performance of the machine learning models.
[00035] In this embodiment, the demographic and health information stored in the data storage device includes genetic data of the population, such as single nucleotide polymorphisms (SNPs), gene mutations, or gene expression profiles. The inclusion of genetic data enables the system to account for hereditary risk factors and identify individuals with a higher predisposition to specific diseases, enabling more accurate disease risk predictions and targeted preventive interventions.
[00036] In this embodiment, the output module includes a user interface module that presents the predicted disease risk to users through various visualizations, such as charts, graphs, maps, or interactive dashboards. The user interface module can be accessed through different devices, including computers, smartphones, or tablets, and designed to accommodate different user roles, such as public health officials, researchers, healthcare providers, or individual members of the population.
[00037] In this embodiment, the statistical model training module uses longitudinal datasets to train the statistical model. Longitudinal datasets track the same individuals or populations over time, capturing changes in demographic and health information, risk factors, and disease outcomes. By using longitudinal datasets, the system can account for temporal trends and better predict disease risk in the context of an evolving population.
[00038] In this embodiment, the disease risk prediction module not only predicts the overall disease risk but also estimates the probability of disease onset within a specific time period, such as the risk of developing type 2 diabetes within the next five years. This feature enables public health officials and healthcare providers to prioritize interventions and monitor the effectiveness of preventive measures in a more targeted manner.
[00039] In this embodiment, the output module generates personalized recommendations for individuals based on their predicted disease risk. These recommendations may include lifestyle modifications, screening tests, or preventive medications, which can help individuals proactively manage their health and reduce their risk of developing the disease.
[00040] In this embodiment, the statistical model training module incorporates environmental factors, such as air pollution levels, water quality, and exposure to hazardous substances, as well as lifestyle choices, such as diet, exercise habits, and smoking status, into the model. This allows for a more comprehensive understanding of disease risk factors and their interplay in the population, leading to more accurate disease risk predictions.
[00041] In this embodiment, the disease risk prediction module is designed to update its predictions in real-time as new data is added to the data storage device. This feature enables the system to adapt to changes in the population over time, providing more accurate and timely disease risk predictions. As new demographic and health information, genetic data, environmental factors, or lifestyle choices are added to the data storage device, the statistical model training module re-trains the model, and the disease risk prediction module generates updated risk predictions. This real-time updating capability ensures that the system remains current and relevant, enhancing the effectiveness of preventive strategies and resource allocation decisions by public health officials, researchers, and healthcare providers.
[00042] The present invention provides a method 200 for predicting disease risk in populations, which includes the following steps; At step 202, the method involves collecting and receiving input data that consists of demographic and health information of a population. The input data may include age, gender, ethnicity, genetic data, medical history, and other relevant health-related data points. The data can be obtained from various sources, such as electronic health records, surveys, or public health registries. At step 204, a statistical model is trained using the input data. The statistical model may be based on various machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, or neural networks, depending on the specific characteristics of the data and the desired prediction accuracy. The training process may incorporate techniques such as

cross-validation and hyperparameter tuning to optimize the performance of the model. The model may also include environmental factors and lifestyle choices, such as air pollution levels, diet, exercise habits, and smoking status, to provide a more comprehensive understanding of disease risk factors and their interplay in the population. At step 206, the trained statistical model is used to predict the disease risk of the population based on the demographic and health information. The disease risk prediction module generates a risk score or probability of disease onset for each individual within the population, as well as aggregated risk measures for subpopulations or the entire population. The disease risk prediction module may further predict the probability of disease onset within a predetermined time period, such as the risk of developing type 2 diabetes within the next five years. At step 208, the method involves outputting the predicted disease risk for the population. This may include visualizations, reports, or data exports to facilitate communication of the results to public health officials, researchers, and healthcare providers. The output may also provide personalized recommendations to individuals based on their predicted disease risk, such as lifestyle modifications, screening tests, or preventive medications.
[00043]
[00044] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00045] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00046] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00047] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00048] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00049] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
 

Claims
I/We Claim:
1. A system for predicting disease risk in populations, comprising: a data storage device for storing demographic and health information of a population; a statistical model training module for training a statistical model using the demographic and health information; a disease risk prediction module for predicting the disease risk of the population based on the demographic and health information using the trained statistical model; and an output module for outputting the predicted disease risk for the population.
2. The system of claim 1, wherein the statistical model training module comprises a machine learning algorithm.
3. The system of claim 1, wherein the demographic and health information comprises genetic data of the population.
4. The system of claim 1, further comprising a user interface module for displaying the predicted disease risk to a user.
5. The system of claim 1, wherein the statistical model is trained using a longitudinal dataset to account for changes in the population over time.
6. The system of claim 1, wherein the disease risk prediction module further predicts the probability of disease onset within a predetermined time period.
7. The system of claim 1, wherein the output module further provides personalized recommendations to individuals based on their predicted disease risk.
8. The system of claim 1, wherein the statistical model training module further incorporates environmental factors and lifestyle choices into the model.
9. The system of claim 1, wherein the disease risk prediction module is updated in real-time as new data is added to the data storage device.
10. A method for predicting disease risk in populations, comprising the steps of: receiving input data comprising demographic and health information of a population; training a statistical model using the input data; using the trained statistical model to predict the disease risk of the population based on the demographic and health information; and outputting the predicted disease risk for the population

Statistical Model for Predicting Disease Risk in Populations
Abstract
The present invention provides a system and method for predicting disease risk in populations. The system includes a data storage device for storing demographic and health information, a statistical model training module that utilizes machine learning algorithms to train a statistical model based on the demographic and health information, a disease risk prediction module that predicts disease risk using the trained statistical model, and an output module for outputting the predicted disease risk for the population. The system can incorporate genetic data, environmental factors, and lifestyle choices into the model and can be updated in real-time as new data is added to the data storage device. The method involves receiving input data, training a statistical model, predicting disease risk, and outputting the predicted disease risk. The system and method can improve disease prevention and management efforts by providing personalized recommendations based on the predicted disease risk, ultimately benefiting population health outcomes. , Claims:Claims
I/We Claim:
1. A system for predicting disease risk in populations, comprising: a data storage device for storing demographic and health information of a population; a statistical model training module for training a statistical model using the demographic and health information; a disease risk prediction module for predicting the disease risk of the population based on the demographic and health information using the trained statistical model; and an output module for outputting the predicted disease risk for the population.
2. The system of claim 1, wherein the statistical model training module comprises a machine learning algorithm.
3. The system of claim 1, wherein the demographic and health information comprises genetic data of the population.
4. The system of claim 1, further comprising a user interface module for displaying the predicted disease risk to a user.
5. The system of claim 1, wherein the statistical model is trained using a longitudinal dataset to account for changes in the population over time.
6. The system of claim 1, wherein the disease risk prediction module further predicts the probability of disease onset within a predetermined time period.
7. The system of claim 1, wherein the output module further provides personalized recommendations to individuals based on their predicted disease risk.
8. The system of claim 1, wherein the statistical model training module further incorporates environmental factors and lifestyle choices into the model.
9. The system of claim 1, wherein the disease risk prediction module is updated in real-time as new data is added to the data storage device.
10. A method for predicting disease risk in populations, comprising the steps of: receiving input data comprising demographic and health information of a population; training a statistical model using the input data; using the trained statistical model to predict the disease risk of the population based on the demographic and health information; and outputting the predicted disease risk for the population