Privacy-Preserving AI in Healthcare: Sepsis Prediction ModelPrivacy-Preserving AI in Healthcare: Sepsis Prediction Model
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Sumbal Murtaza
• Jul 10
Project Title: Privacy-Preserving Sepsis Prediction Model
Role: AI Research Intern
The Challenge:
Integrating complex Recurrent Neural Network (RNN) architectures within a privacy-preserving Multi-Party Computation (MPC) environment.
Addressing the "fixed-point arithmetic" limitations inherent in cryptographic frameworks when handling 48-hour sequential medical data.
My Approach:
Mentored by a Yale University researcher to bridge the gap between AI and secure cryptographic protocols.
Independently engineered custom normalization and clipping techniques to resolve data overflow issues caused by sequential computation loops.
Successfully optimized the model for the MIMIC-III dataset, ensuring high-quality, functional code performance.
The Outcome:
Successfully contributed the finalized research implementation to the official open-source repository.
Demonstrated technical maturity in handling complex engineering bottlenecks while adhering to formal research methodologies. Here is my repo link: https://github.com/sum710/sequre
Completed work
Data AnalysisMachine Learning EngineeringGitGitHubPython
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