Wikipedia

Suchi Saria

Suchi Saria is an Associate Professor of Machine Learning and Healthcare at Johns Hopkins University, where she uses big data to improve patient outcomes.[1][3][4][5] She is a World Economic Forum Young Global Leader. From 2022 to 2023, she was an investment partner at AIX Ventures.[6] AIX Ventures is a venture capital fund that invests in artificial intelligence startups.

Suchi Saria
Suchi Saria in 2019 video from the National Science Foundation
Born1982 or 1983 (age 40–41)[2]
Alma mater
Known for
Awards
Scientific career
Fields
InstitutionsJohns Hopkins University
ThesisThe Digital Patient: Machine Learning Techniques for Analyzing Electronic Health Record Data (2011)
Doctoral advisorDaphne Koller
Websitesuchisaria.jhu.edu

Early life and education

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Saria is from Darjeeling.[7] She earned her bachelor's degree at Mount Holyoke College.[8] She was awarded a full scholarship from Microsoft. In 2004 she joined Stanford University as a Rambus Corporation Fellow.[8] She earned her Master of Science and Doctor of Philosophy[9] degrees at Stanford University, supervised by Daphne Koller and advised by Anna Asher Penn and Sebastian Thrun. At Stanford University, Saria developed a statistical model that could predict premature baby outcomes with a 90% accuracy.[10] The model used data from monitors, birth weight and length of time spent in the womb to predict whether a preemie would develop an illness.[11] [12] She worked in the startup Aster Data Systems.[13]

Career and research

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Saria believes that big data can be used to personalise healthcare.[14][15] She is considered an expert in computational statistics and their applications to the real world.[8] She uses Bayesian and probabilistic modelling.[7] In 2014 Saria was funded by a $1.5 million Gordon and Betty Moore Foundation project that looked to make intensive care units safer.[16] The project used data collected at patients' bedsides along with noninvasive 3D sensors that monitor care in patient's hospital rooms.[17] The sensors collect information on steps that might have been missed by doctors; like washing hands.[17]

Saria uses big data to manage chronic diseases.[18] She is part of a National Science Foundation (NSF) award that looks at scleroderma. She uses machine learning to analyse medical records and identify similar patterns of disease progression.[18] The system works out which treatments have been effectively used for various symptoms to aid doctors in choosing treatment plans for specific patients.[18] She has developed another algorithm that can be used to predict and treat Septic shock.[19] The algorithm used 16,000 items of patient health records and generates a targeted real-time warning (TREWS) score.[20] She collaborated with David N. Hager to use the algorithm in clinics, and it was correct 86% of the time. Saria modified the algorithm to avoid missing high risk patients- for example, those who have suffered from septic shock previously and who have sought successful treatment.[21] She was described by XRDS magazine as being a Pioneer in transforming healthcare.[22] In 2016 Saria spoke at about using machine learning for medicine at TEDxBoston.[23] The talk has been viewed over 100,170 times.[24]

Awards and honours

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Her awards and honors include:

References

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  1. ^ a b Suchi Saria publications indexed by Google Scholar  
  2. ^ a b "These are the young people in tech to watch right now—meet this year's 35 Innovators Under 35". technologyreview.com. MIT Technology Review. Retrieved 2018-12-16.
  3. ^ Suchi Saria at DBLP Bibliography Server  
  4. ^ Bates, David W.; Saria, Suchi; Ohno-Machado, Lucila; Shah, Anand; Escobar, Gabriel (2014). "Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients". Health Affairs. 33 (7): 1123–1131. doi:10.1377/hlthaff.2014.0041. ISSN 0278-2715. PMID 25006137.  
  5. ^ Saria, S.; Rajani, A. K.; Gould, J.; Koller, D.; Penn, A. A. (2010). "Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants". Science Translational Medicine. 2 (48): 48ra65. doi:10.1126/scitranslmed.3001304. ISSN 1946-6234. PMC 3564961. PMID 20826840.
  6. ^ "AIX Ventures - An AI Fund". AIX Ventures. Retrieved 2023-01-13.
  7. ^ a b "Suchi Saria – Machine Learning, Computational Health Informatics". suchisaria.jhu.edu. Retrieved 2018-12-16.
  8. ^ a b c d e f g "Suchi Saria, M.Sc., Ph.D". hopkinsmedicine.org. Johns Hopkins University. Retrieved 2018-12-16.
  9. ^ Saria, Suchi (2011). The digital patient : machine learning techniques for analyzing electronic health record data. stanford.edu (PhD thesis). Stanford University. OCLC 748681635.  
  10. ^ Willyard, Cassandra (2010-09-08). "New Model Predicts Complications in Preemies". sciencemag.org. AAAS. Retrieved 2018-12-16.
  11. ^ "Electronic tool accurately assesses disease risk for preterm infants". healthcareitnews.com. Healthcare IT News. 2010-09-09. Retrieved 2018-12-16.
  12. ^ Klein, Dianne (21 June 2010). "Researchers design more accurate method of determining premature infants' risk of illness". med.stanford.edu. Stanford University. Retrieved 2018-12-16.
  13. ^ "Plenary Speakers | SRI 2017 Annual Meeting". www.sri-online.org. Retrieved 2018-12-17.
  14. ^ a b Spring 2015, Jim Duffy / Published (2015-03-05). "Personalizing health care through big data". hub.jhu.edu. The Hub. Retrieved 2018-12-16.{{cite web}}: CS1 maint: numeric names: authors list (link)
  15. ^ "A $3 Trillion Challenge to Computational Scientists: Transforming Healthcare Delivery - IEEE Journals & Magazine". doi:10.1109/MIS.2014.58. S2CID 11091114. {{cite journal}}: Cite journal requires |journal= (help)
  16. ^ "Johns Hopkins Winter 2014 Engineering Magazine". eng.jhu.edu. Retrieved 2018-12-16.
  17. ^ a b "Johns Hopkins Winter 2014 Engineering Magazine". eng.jhu.edu. Retrieved 2018-12-16.
  18. ^ a b c "Predictive Medicine - Science Nation". nsf.gov. National Science Foundation. Retrieved 2018-12-16.
  19. ^ "Predictive Model Identifies Patients Who Might Go Into Septic Shock". popsci.com. Popular Science. 6 August 2015. Retrieved 2018-12-16.
  20. ^ Saria, Suchi; Pronovost, Peter J.; Hager, David N.; Henry, Katharine E. (2015). "A targeted real-time early warning score (TREWScore) for septic shock". Science Translational Medicine. 7 (299): 299ra122. doi:10.1126/scitranslmed.aab3719. ISSN 1946-6242. PMID 26246167.  
  21. ^ Young, Lauren J. (2015-08-07). "A Computer That Can Sniff Out Septic Shock". IEEE Spectrum: Technology, Engineering, and Science News. Retrieved 2018-12-16.
  22. ^ Razavian, Narges (2015). "Advancing the Frontier of Data-driven Healthcare". XRDS. 21 (4): 34–37. doi:10.1145/2788506. ISSN 1528-4972. S2CID 33163301.  
  23. ^ "Suchi Saria – TEDxBoston". tedxboston.org. Retrieved 2018-12-16.
  24. ^ "Better Medicine Through Machine Learning | Suchi Saria", youtube.com, retrieved 2018-12-16
  25. ^ "CS' Suchi Saria named a 2018 Sloan Research Fellow". cs.jhu.edu. Department of Computer Science. 2018-02-15. Retrieved 2018-12-16.
  26. ^ "Four Johns Hopkins scientists named Sloan Research Fellows". hub.jhu.edu. The Hub. 2018-02-15. Retrieved 2018-12-16.
  27. ^ a b "North America - Meet the 2018 Young Global Leaders". widgets.weforum.org. Retrieved 2018-12-16.
  28. ^ "Young Faculty Award". darpa.mil. Retrieved 2018-12-16.
  29. ^ "The Woman Who Predicts Septic Shock And Other Health Outcomes". popsci.com. Popular Science. 8 September 2016. Retrieved 2018-12-16.
  30. ^ "IEEE-AI-10-to-Watch.pdf" (PDF). Dropbox.com. Retrieved 2018-12-16.
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