Cloud-Based COVID-19 Diagnosis Workflows with Privacy-Aware AI
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Solution Overview
Problem
The rapid evolution and mutation of the SARS-CoV-2 virus complicates COVID-19 diagnosis and treatment, necessitating a system that can rapidly process big data to facilitate intelligent identification of diagnoses and treatments tailored to individual patients.
Innovation Solution
A cloud-based application using trained machine-learning models processes subject data to predict COVID-19 diagnoses and suitable treatments, while ensuring data privacy compliance through obfuscation and adherence to jurisdictional regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-based registries and machine learning models are used to process big data for COVID-19 diagnosis and treatment, then diagnostic accuracy and treatment personalization are improved, but data privacy risks and computational complexity increase
Solution Approach 1:
The system segments the computational workload by distributing data storage across multiple cloud-based registries and organizing machine learning models into modular components. Subject data is divided into structured records with specific attributes (demographics, clinical findings, lab results) that can be independently processed, allowing parallel computation while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between raw data and diagnostic output through structured subject records and trained machine learning models. These intermediaries transform complex big data into standardized formats that can be processed efficiently, reducing computational complexity while preserving diagnostic precision through systematic data organization and algorithmic processing.
2Adaptability or versatility
If subject data is stored in cloud-based registries for AI processing, then treatment personalization is improved, but data privacy compliance becomes more difficult
Solution Approach 1:
The system applies local quality by implementing jurisdiction-specific data governance rules within the cloud registry structure. Different data fields are tagged with applicable jurisdictional requirements, allowing the system to apply appropriate privacy protections locally to each data element while maintaining overall treatment personalization capabilities across diverse regulatory environments.
Solution Approach 2:
The patent implements preliminary action by establishing data governance frameworks and privacy compliance mechanisms before data is stored in the cloud registry. Subject records are structured with built-in privacy protections and access controls from the outset, enabling treatment personalization through AI processing while ensuring data privacy compliance is embedded in the system architecture rather than added later.
Data Source
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AI summary
Disclosed herein are systems, methods, and techniques for building and using a data platform to facilitate intelligent identification of coronavirus disease 2019 (COVID-19) related diagnoses, treatment selection, and interaction tracing. The present disclosure relates to a cloud-based application that generates outputs predictive of a subject's COVID-19 diagnoses and/or suitability for COVID-19 treatments.