Speakers
E. Hope Weissler, MD, MHS
Resident, Vascular and Endovascular Surgery
Duke University School of Medicine
Erich Huang, MD, PhD
Chief Science and Innovation Officer, Onduo
Topic
Great Power and Great Responsibility: Machine Learning in Clinical Research
Keywords
Machine Learning; Artificial Intelligence; Data Liquidity; Data Storage; HL7FHIR
Key Points
- Machine learning may address issues that have reduced the efficiency and effectiveness of clinical research and help clinical research projects reach their full potential.
- Machine learning may improve the pragmatism of research, decreasing costs and time it takes to conduct a research study.
- Machine learning can be used to canvas the literature, hypothesize drug-target interactions, propose new therapeutics, and analyze highly dimensional research output.
- Effects of machine learning are up to us and could potentially reduce the pragmatism of research if applied indiscriminately. Machine learning could produce overly selected study participant groups, too closely managing adherence, and using ultra-high-touch follow-up methods.
- Data Liquidity refers to the ease with which data can be transferred or exchanged. This depends largely on the manner in which the data is stored.
- Some forms of data are liquid than others due to privacy, security, and ethical concerns.
Discussion Themes
A lot of emphasis is currently being placed on the mobile/wearable device area, but an equally important area to develop in machine learning is patient identification and recruitment.
Is data ever really de-identified? Should data be owned by the patient? Why is health data treated differently than consumer data? Privacy regulation is difficult and needs to be addressed further by Congress in the future.
Read more about Dr. Weissler and Dr. Huang’s machine learning in clinical research.
Tags
#pctGR, @Collaboratory1