SXSW 2022
The Subset Seekers
Description:
As Dr. Brian Garibaldi directed the Johns Hopkins Biocontainment Unit in Baltimore amid COVID-19, a fit, active man in his 30s struggled on a ventilator for 40 days. Meanwhile, a 117-year-old French nun contracted the disease, fully recovered and didn’t even know she had it. To explain this shocking difference, Garibaldi and Johns Hopkins Applied Physics Laboratory researchers Will Gray Roncal and Karla Gray Roncal leveraged big data and machine learning to unravel the mystery of the different reactions, predict patients’ probable disease course and identify unique treatment needs. Garibaldi, Will Gray Roncal and Karla Gray Roncal will discuss how big data and machine learning advanced precision medicine for COVID-19 and how the techniques could be applied to other diseases.
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Takeaways
- The team’s prediction model and COVID-19 pandemic have accelerated everyone’s appreciation and understanding of the benefits of precision medicine.
- The COVID-19 patient disease progression model can be applicable to other severe diseases requiring hospitalization, such as pneumonia.
- The team’s prediction model and COVID-19 pandemic have accelerated everyone’s appreciation and understanding of the benefits of precision medicine.
Speakers
- Brian Garibaldi, Assistant Professor of Medicine, Johns Hopkins University School of Medicine
- Will Gray Roncal, Section Supervisor/Project Manager, Johns Hopkins Applied Physics Laboratory
- Karla Gray Roncal, Senior Professional Staff I, Johns Hopkins Applied Physics Laboratory
Organizer
Hannah Longstaff, Communications Specialist, The Johns Hopkins University Applied Physics Laboratory
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