Big Data, Surveillance Capitalism, and Precision Medicine: Challenges for Privacy

2021 ◽  
Vol 49 (4) ◽  
pp. 666-676
Author(s):  
Mark A. Rothstein
Keyword(s):  
Big Data ◽  
Precision Medicine ◽  
Health Information ◽  
Medicine Research ◽  
Amount Of Information ◽  
Data Surveillance ◽  
Precision Medicine Research

AbstractSurveillance capitalism companies, such as Google and Facebook, have substantially increased the amount of information collected, analyzed, and monetized, including health information increasingly used in precision medicine research, thereby presenting great challenges for health privacy.

scholarly journals Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform (Preprint)

2018 ◽  
Author(s):  
Jacob McPadden ◽  
Thomas JS Durant ◽  
Dustin R Bunch ◽  
Andreas Coppi ◽  
Nathaniel Price ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Precision Medicine ◽  
Open Source ◽  
Real Time ◽  
Data Science ◽  
Patient Monitoring ◽  
Medicine Research ◽  
Health Care Data ◽  
Precision Medicine Research

BACKGROUND Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. OBJECTIVE The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. METHODS We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. RESULTS Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. CONCLUSIONS The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.

Increasing inclusivity in precision medicine research: Views of individuals who are deaf and hard of hearing

2021 ◽  
Author(s):  
Diana C. Garofalo ◽  
Howard A. Rosenblum ◽  
Yuan Zhang ◽  
Ying Chen ◽  
Paul S. Appelbaum ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Hard Of Hearing ◽  
Medicine Research ◽  
Precision Medicine Research

scholarly journals Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial

2020 ◽  
Vol 30 (Suppl 1) ◽  
pp. 217-228 ◽  
Author(s):  
Sanjay Basu ◽  
James H. Faghmous ◽  
Patrick Doupe
Keyword(s):  
Machine Learning ◽  
Health Disparities ◽  
Precision Medicine ◽  
Learning Approaches ◽  
Learning Methods ◽  
Medicine Research ◽  
Machine Learning Methods ◽  
Box Models ◽  
Precision Medicine Research ◽  
Key Terms

  Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health research­ers on the application of machine learning methods to conduct precision medicine research designed to reduce health dispari­ties. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advan­tages and disadvantages of different learning approaches, describe strategies for interpret­ing “black box” models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.Ethn Dis. 2020;30(Suppl 1):217-228; doi:10.18865/ed.30.S1.217

scholarly journals Human gene and disease associations for clinical‐genomics and precision medicine research

2020 ◽  
Vol 10 (1) ◽  
pp. 297-318 ◽  
Author(s):  
Zeeshan Ahmed ◽  
Saman Zeeshan ◽  
Dinesh Mendhe ◽  
XinQi Dong
Keyword(s):  
Precision Medicine ◽  
Human Gene ◽  
Clinical Genomics ◽  
Medicine Research ◽  
Disease Associations ◽  
Precision Medicine Research

scholarly journals Disability inclusion in precision medicine research: a first national survey

2019 ◽  
Vol 21 (10) ◽  
pp. 2319-2327 ◽  
Author(s):  
Maya Sabatello ◽  
Ying Chen ◽  
Yuan Zhang ◽  
Paul S. Appelbaum
Keyword(s):  
Precision Medicine ◽  
National Survey ◽  
Medicine Research ◽  
Precision Medicine Research

scholarly journals Conducting Precision Medicine Research with African Americans

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0154850 ◽  
Author(s):  
Chanita Hughes Halbert ◽  
Jasmine McDonald ◽  
Susan Vadaparampil ◽  
LaShanta Rice ◽  
Melanie Jefferson
Keyword(s):  
African Americans ◽  
Precision Medicine ◽  
Medicine Research ◽  
Precision Medicine Research
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