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Tiffany Purcell Pellathy
University Of Pittsburgh At Pittsburgh
$106,542
Attributed
$106,542
Total exposure
1
Grants
1
Lead (contact PI)
Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants. They are the sole PI on all grants (the two match).
Funding over time
peak $45K · FY2018–20$50K$37.5K$25K$12.5K$0
'18
'19
'20
Funding mix
By agency
NIH$106,542 · 1
By mechanism
F31$106,542 · 1
Top collaborators
No co-investigators on record.
Most similar at University Of Pittsburgh At Pittsburgh
Same institution · by research overlap
- Panagiotis V Benos$14,018,248
- Anna E Lokshin$15,272,209
- Michael E Talkowski$34,167,259
- Debra Kaye Weiner$3,397,320
- Michael R Pinsky$13,601,966
Others in their field
Other Emerging Leaders on “Data Set”
- Joel A Lipkin · Four Points Technology, Llc$182,936,915
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- April Brinkoetter · Hungry Heart Media, Inc.$59,802,265
- Martha Hering · Westat, Inc.$57,098,843
- Susan Abushakra · Alzheon, Inc.$47,265,244
Research focus
Data SetDecision MakingAdverse EventData ElementData ScienceBiologicalBiological MarkersCare DeliveryCensusesBaseBig DataAdultClinical DataClinical Data WarehouseClinical Decision-MakingClinical RiskCodeCohortCohort StudiesComplexComplicationCessation Of LifeCostDeep Vein Thrombosis
Grant awards (3)
Machine Learning to Determine Dynamically Evolving New-Onset Venous Thromboembolic (VTE) Event Risk in Hospitalized Patients$17,002
F31 · FY2020 · NR · contact PI
Machine Learning to Determine Dynamically Evolving New-Onset Venous Thromboembolic (VTE) Event Risk in Hospitalized Patients$45,016
F31 · FY2019 · NR · contact PI
Machine Learning to Determine Dynamically Evolving New-Onset Venous Thromboembolic (VTE) Event Risk in Hospitalized Patients$44,524
F31 · FY2018 · NR · contact PI