Jump to content

Suchi Saria

From Wikipedia, the free encyclopedia
Suchi Saria
Suchi Saria in 2019 video from the National Science Foundation
Born1982 or 1983 (age 41–42)[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

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.

Early life and education

[edit]

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

[edit]

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

[edit]

Her awards and honors include:

References

[edit]
  1. ^ a b Suchi Saria publications indexed by Google Scholar Edit this at Wikidata
  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 Edit this at Wikidata
  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. Free access icon
  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. Free access icon
  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". IEEE Intelligent Systems. 29 (4): 82–87. 2014. doi:10.1109/MIS.2014.58. ISSN 1541-1672. S2CID 11091114.
  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. Closed access icon
  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. Closed access icon
  23. ^ "Suchi Saria – TEDxBoston". tedxboston.org. Retrieved 2018-12-16.
  24. ^ "Better Medicine Through Machine Learning | Suchi Saria", youtube.com, 12 October 2016, 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.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Suchi_Saria&oldid=1246287693"