Machine Learning Digital Therapeutics Selection System
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Solution Overview
Problem
Traditional contact tracing methods are limited in detecting COVID-19 transmission, as they focus on close proximity and simultaneous location visits, missing events where transmission can occur through contaminated surfaces or airborne particles, and are not versatile enough to capture varied interactions and locations.
Innovation Solution
A system using machine learning models that incorporate location tracking data, behavior patterns, and environmental factors to predict disease hotspots and transmission risks, providing personalized recommendations and interventions, including geofencing and proximity tracing to identify exposure risks and initiate appropriate actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional contact tracing methods are used to detect COVID-19 transmission, then close proximity and simultaneous location visits can be identified, but transmission events through contaminated surfaces or airborne particles are missed
Solution Approach 1:
The patent applies universality by creating a contact tracing system that performs multiple functions: it tracks close proximity encounters, identifies simultaneous location visits, and detects potential transmission events through contaminated surfaces and airborne particles. The system universally monitors various transmission pathways rather than being limited to a single method, thereby improving detection accuracy while maintaining versatility.
Solution Approach 2:
The patent extends contact tracing into another dimension by adding environmental monitoring capabilities. Instead of only tracking interpersonal proximity, the system monitors environmental factors such as surface contamination and airborne particle presence. This dimensional expansion allows the system to detect transmission events that occur without direct close contact, resolving the contradiction between detection accuracy and method versatility.
2Measurement precision
If machine learning models incorporate multiple data sources including location tracking and behavior patterns, then prediction accuracy for disease hotspots improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex machine learning system into modular components: data collection modules that gather location tracking and behavior pattern information, data processing modules that organize and clean the data, machine learning model modules that analyze patterns and generate predictions, and output modules that communicate results. This segmentation manages system complexity while maintaining high prediction accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediary components between data collection and prediction generation, including data normalization layers, feature extraction modules, and validation filters. These intermediaries process raw data from multiple sources before feeding it to the machine learning models, reducing the complexity burden on the core prediction algorithms while improving overall system accuracy through systematic data preparation.
3Reliability
If the system provides personalized recommendations and interventions for different communities, then disease management effectiveness improves, but data processing requirements increase
Solution Approach 1:
The patent applies local quality by tailoring disease management recommendations to specific community characteristics and needs. Instead of providing uniform recommendations, the system analyzes local data patterns, community behavior profiles, and regional risk factors to generate customized intervention strategies. This localized approach improves disease management effectiveness while optimizing data processing by focusing computational resources on relevant local patterns rather than processing all data uniformly.
Solution Approach 2:
The system performs preliminary data aggregation and pattern recognition at the community level before generating personalized recommendations. By pre-processing data to identify community-specific risk profiles and transmission patterns in advance, the system reduces the volume of raw data that needs to be processed when generating individualized recommendations, thereby improving effectiveness while managing data processing requirements.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for machine learning to select digital therapeutics. In some implementations, a machine learning model is trained to generate scores that indicate the level of applicability of different digital therapeutics based on input data for a person. A computer system monitors health of an individual using surveys and sensor measurements. The computer system identifies one or more signs or symptoms of a disease based on the monitoring data for the individual. The computer system uses output of the machine learning model to select a digital therapeutic intervention for the user. The computer system cause one or more devices to deliver the selected digital therapeutic intervention to the individual, monitor a response of the individual to the selected digital therapeutic intervention, and adjust characteristics of the digital therapeutic intervention based on the monitored response.