[Colloquium] [DSI] 12/6: Gari Clifford (Emory) – Mythological Medical Machine Learning

[Rob Mitchum]

*Gari Clifford*
*Professor of Biomedical Informatics and Biomedical Engineering, Emory
University and the Georgia Institute of Technology*

*Chair of the Department of Biomedical Informatics at Emory*

*Monday, December 6th*
*3:00 p.m. - 4:00 p.m.*
*In-Person (JCL 390) or Live Stream (YouTube, Zoom)*
*Register
<https://www.eventbrite.com/e/gari-clifford-emory-mythological-medical-machine-learning-tickets-211801041617>
with
preference for in-person or remote*


*Mythological Medical Machine Learning: Boosting the performance of a deep
learning medical data classifier using realistic medical models*

*Abstract*: There is a myth in modern machine learning, that as the size of
a database increases, and the network depth increases, the performance of
an algorithm will continue to improve. This myth is particularly untrue for
medical data, which require intense curation to create high-quality labels.
As the databases increase in size, the data labels drop in quality or even
vanish. Often, the data become noisier with rising levels of non-random
missingness. Increasingly, transfer learning is being leveraged to mitigate
these problems, allowing algorithms to tune on smaller (or rarer)
populations while leveraging information from much larger datasets. I’ll
present an emerging paradigm in which we insert an extensive
model-generated database in the transfer learning process to help a deep
learner explore a much larger and denser data distribution. Since a model
allows the generation of realistic data beyond the boundaries of the real
data, the model can help train the deep learner to extrapolate beyond the
observable collection of samples. Using cardiac time series data, I’ll
demonstrate that this technique provides a significant performance boost.
I’ll then discuss how general this approach is, and how it can distinguish
AI from machine learning.

*Bio:* Gari Clifford is a tenured Professor of Biomedical Informatics and
Biomedical Engineering at Emory University and the Georgia Institute of
Technology, and the Chair of the Department of Biomedical Informatics at
Emory. His research team applies signal processing and machine learning to
medicine to classify, track and predict health and illness. His focus
research areas include critical care, digital psychiatry, global health,
mHealth, neuroinformatics and perinatal health, particularly in LMIC
settings.  After training in Theoretical Physics, he transitioned to
Machine Learning and Engineering for his doctoral work at the University of
Oxford in the 1990’s. He subsequently joined MIT as a postdoctoral fellow,
then a Principal Research Scientist, where he managed the creation of the
MIMIC II database, the largest open-access critical care database in the
world. He later returned to Oxford as an Associate Professor of Biomedical
Engineering, where he helped found its Sleep & Circadian Neuroscience
Institute and served as Director of the Centre for Doctoral Training in
Healthcare Innovation at the Oxford Institute of Biomedical Engineering.
Gari is a strong supporter of commercial translation, working closely with
industry as an advisor to multiple companies, co-founding and serving as
CTO of an MIT spin-out (MindChild Medical) since 2009, and co-founding and
serving as CSO for Lifebell AI since 2020. Gari is a champion for
open-access data and open-source software in medicine, particularly through
his leadership of the PhysioNet/CinC Challenges
<https://physionet.org/challenge/> and contributions to the PhysioNet
Resource <https://physionet.org/>. He is committed to developing
sustainable solutions to healthcare problems in resource poor locations,
with much of his work focused in Guatemala.


*Part of the Data Science Institute Distinguished Speaker Series:*

*Defining The Field of Data Science*
As data science evolves from buzzword to a mature and singular field, its
research questions dive deeper into the foundations of this new discipline.
The Fall 2021 Distinguished Speaker Series convenes world-class experts
actively exploring and expanding the fundamental methods and approaches
that transform large and complex datasets into knowledge and action,
fueling new applications in areas such as artificial intelligence,
healthcare, and the social sciences. Join the new UChicago Data Science
Institute for provocative talks and discussion that will illuminate the
bedrock and promise of the flourishing field of data science.


*This convening is open to all invitees who are compliant with UChicago
vaccination requirements and, because of ongoing health risks, particularly
to the unvaccinated, participants are expected to adopt the risk mitigation
measures (masking and social distancing, etc.) appropriate to their
vaccination status as advised by public health officials or to their
individual vulnerabilities as advised by a medical professional. Public
convening may not be safe for all and carries a risk for contracting
COVID-19, particularly for those unvaccinated. Participants will not know
the vaccination status of others and should follow appropriate risk
mitigation measures.*
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