scholarly journals Thought Leader Comparisons of Risks in Precision Medicine Research

2020 ◽  
Vol 42 (6) ◽  
pp. 35-40
Author(s):  
Laura M. Beskow ◽  
Catherine M. Hammack‐Aviran ◽  
Kathleen M. Brelsford
Keyword(s):  
Precision Medicine ◽  
Medicine Research ◽  
Thought Leader ◽  
Precision Medicine Research

scholarly journals Thought Leader Perspectives on Participant Protections in Precision Medicine Research

2019 ◽  
Vol 47 (1) ◽  
pp. 134-148 ◽  
Author(s):  
Catherine M. Hammack ◽  
Kathleen M. Brelsford ◽  
Laura M. Beskow
Keyword(s):  
Precision Medicine ◽  
Human Subjects ◽  
Security Measures ◽  
Technical Data ◽  
Medicine Research ◽  
Thought Leader ◽  
Leader Perspectives ◽  
Precision Medicine Research ◽  
Participation Decisions ◽  
Depth Interviews

Precision medicine research is rapidly taking a lead role in the pursuit of new ways to improve health and prevent disease, but also presents new challenges for protecting human subjects. The extent to which the current “web” of legal protections, including technical data security measures, as well as measures to restrict access or prevent misuse of research data, will protect participants in this context remains largely unknown. Understanding the strength, usefulness, and limitations of this constellation of laws, regulations, and procedures is critical to ensuring not only that participants are protected, but also that their participation decisions are accurately informed. To address these gaps, we conducted in-depth interviews with a diverse group of 60 thought-leaders to explore their perspectives on the protections associated with precision medicine research.

Thought-Leader Perspectives on Risks in Precision Medicine Research

2018 ◽  
pp. 161-174 ◽  
Author(s):  
Laura M. Beskow ◽  
Catherine M. Hammack ◽  
Kathleen M. Brelsford ◽  
Kevin C. McKenna
Keyword(s):  
Precision Medicine ◽  
Medicine Research ◽  
Thought Leader ◽  
Leader Perspectives ◽  
Precision Medicine Research

scholarly journals Thought leader perspectives on benefits and harms in precision medicine research

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207842 ◽  
Author(s):  
Laura M. Beskow ◽  
Catherine M. Hammack ◽  
Kathleen M. Brelsford
Keyword(s):  
Precision Medicine ◽  
Medicine Research ◽  
Thought Leader ◽  
Leader Perspectives ◽  
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 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
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