Microbiome Data Analysis Platform for Personalized Healthcare
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Solution Overview
Problem
The human and animal microbiome's role in health is poorly understood due to the lack of frequent, convenient, and location-specific sampling across populations, as well as limited information about interacting pairs or groups of individuals.
Innovation Solution
A computer-implemented method and system for collecting and analyzing microbiome data, including detecting user selections, accessing microbiome data, generating overview reports on subject predispositions, and displaying them on a user interface, utilizing machine learning methods to predict subject predispositions and provide personalized healthcare interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent and location-specific microbiome sampling is implemented, then data collection quality and health understanding are improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent introduces a cloud-based processing platform as an intermediary between microbiome sampling devices and healthcare systems. This platform receives, stores, and analyzes microbiome data from multiple sources, enabling frequent and location-specific sampling without increasing local system complexity. The cloud platform handles the computational burden of analyzing complex microbiome datasets, allowing distributed sampling while maintaining centralized processing capability.
Solution Approach 2:
The system is designed to handle multiple types of microbiome data from various sources (human, animal, environmental samples) through a unified platform. The same infrastructure processes different sample types, locations, and frequencies, making the system universally applicable to diverse microbiome study scenarios without requiring separate specialized systems for each data source.
2Loss of information
If comprehensive microbiome data analysis is performed, then health insights and personalized treatment are improved, but computational resources and processing time increase
Solution Approach 1:
The data analysis process is segmented into multiple stages: initial data processing and storage in the cloud platform, followed by targeted analysis based on specific health questions or conditions. Rather than analyzing all microbiome data uniformly, the system segments the analysis process to focus computational resources on specific subsets of data relevant to particular health outcomes, reducing overall computational burden while maintaining comprehensive insights when needed.
Solution Approach 2:
The system performs preliminary data processing, cleaning, and organization in the cloud platform before analysis. This preliminary action includes data standardization, quality control, and preparation of datasets for analysis, reducing the computational complexity of subsequent analysis steps and optimizing resource utilization during the actual health insight generation phase.
3Measurement precision
If location-specific microbiome sampling is implemented, then disease diagnosis accuracy is improved, but sampling logistics and implementation difficulty increase
Solution Approach 1:
The cloud-based platform serves as an intermediary that coordinates location-specific sampling logistics. It provides standardized protocols for different locations, manages sample tracking and data association, and handles the complexity of coordinating multiple sampling sites. This allows location-specific sampling to be implemented without requiring complex local coordination systems at each site.
Solution Approach 2:
The system adapts sampling parameters (frequency, location, depth) based on specific diagnostic needs and population characteristics. Rather than using a fixed sampling protocol, the system adjusts parameters dynamically to optimize diagnosis accuracy for different conditions while maintaining operational simplicity through automated parameter configuration in the cloud platform.
Data Source
AI summary
Systems and methods for leveraging subject microbiome data to achieve a target goal are disclosed. The method contains operations including: detecting, on a graphical user interface of an application platform of a user computing device, a selection of a subject by a user; accessing, based on the detecting, data associated with the subject, wherein the data comprises the subject microbiome data; generating, by a processor, an overview report comprising a first set of subject predispositions; and displaying, on the application platform, the generated overview report. Other aspects are described and claimed.