Machine Learning in Precision Medicine to Preserve Privacy via Encryption

02/05/2021
by   William Briguglio, et al.
0

Precision medicine is an emerging approach for disease treatment and prevention that delivers personalized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles. Despite the rapid advancement of precision medicine and its considerable promise, several underlying technological challenges remain unsolved. One such challenge of great importance is the security and privacy of precision health-related data, such as genomic data and electronic health records, which stifle collaboration and hamper the full potential of machine-learning (ML) algorithms. To preserve data privacy while providing ML solutions, this article makes three contributions. First, we propose a generic machine learning with encryption (MLE) framework, which we used to build an ML model that predicts cancer from one of the most recent comprehensive genomics datasets in the field. Second, our framework's prediction accuracy is slightly higher than that of the most recent studies conducted on the same dataset, yet it maintains the privacy of the patients' genomic data. Third, to facilitate the validation, reproduction, and extension of this work, we provide an open-source repository that contains the design and implementation of the framework, all the ML experiments and code, and the final predictive model deployed to a free cloud service.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/17/2019

Opportunities for artificial intelligence in advancing precision medicine

Machine learning (ML), deep learning (DL), and artificial intelligence (...
research
08/10/2021

Bandit Algorithms for Precision Medicine

The Oxford English Dictionary defines precision medicine as "medical car...
research
08/22/2022

SoK: Machine Learning with Confidential Computing

Privacy and security challenges in Machine Learning (ML) have become a c...
research
02/16/2023

HE-MAN – Homomorphically Encrypted MAchine learning with oNnx models

Machine learning (ML) algorithms are increasingly important for the succ...
research
11/09/2021

Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives

Background: Electronic Health Records (EHRs) contain rich information of...
research
01/11/2021

Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

Machine Learning (ML) models typically require large-scale, balanced tra...
research
10/15/2019

Learning Sample-Specific Models with Low-Rank Personalized Regression

Modern applications of machine learning (ML) deal with increasingly hete...

Please sign up or login with your details

Forgot password? Click here to reset