← Leaderboards
Matthew Michael Churpek
University Of Chicago
$8,108,899
Attributed
$9,789,921
Total exposure
5
Grants
4
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.
Funding over time
peak $1.7M · FY2014–25$2M$1.5M$1M$500K$0
'14
'15
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25
Funding mix
By agency
NIH$9,789,921 · 5
By mechanism
R01$7,483,997 · 3
R35$1,555,000 · 1
K08$750,924 · 1
Top collaborators
- Jay L Koyner5 shared
Most similar at University Of Chicago
Same institution · by research overlap
- Julian Solway$86,779,881
- Nuala Jennings Meyer$12,062,038
- Anirban Basu$11,177,822
- Anne I. Sperling$25,843,318
- Jay L Koyner$2,844,723
Others in their field
Top investigators on “Hospitals”
- Larry Arthur$288,323,000
- Glenda E Gray · Wits Health Consortium (Pty), Ltd$230,275,254
- Judith S. Currier · University Of California Los Angeles$223,149,861
- Gerald T Nepom · Benaroya Research Inst At Virginia Mason$221,207,102
- Lawrence Corey · Fred Hutchinson Cancer Center$217,829,567
- Daniel R Kuritzkes · Brigham And Women'S Hospital$179,610,394
Research focus
HospitalsElectronic Health RecordMachine LearningHigh RiskMortalityCaringAlgorithmsEarly DiagnosisPersonalized CareFutureComplexWardLifeCessation Of LifeMachine Learning MethodPatient-Focused OutcomesHospitalizationCostManualsRisk FactorsImproved OutcomeNatural Language ProcessingHourMorbidity - Disease Rate
Grant awards (24)
Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury$692,905
R01 · FY2025 · DK
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$388,750
R35 · FY2025 · GM · contact PI
Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury$670,152
R01 · FY2024 · DK
Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients$565,408
R01 · FY2024 · HL · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$388,750
R35 · FY2024 · GM · contact PI
Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury$697,926
R01 · FY2023 · DK
Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients$567,225
R01 · FY2023 · HL · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$388,750
R35 · FY2023 · GM · contact PI
Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury$679,306
R01 · FY2022 · DK
Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients$555,000
R01 · FY2022 · HL · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$388,750
R35 · FY2022 · GM · contact PI
Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury$621,756
R01 · FY2021 · DK
Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients$574,395
R01 · FY2021 · HL · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$329,779
R01 · FY2021 · GM · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$331,866
R01 · FY2020 · GM · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$365,438
R01 · FY2019 · GM · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$29,604
R01 · FY2019 · GM · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$385,792
R01 · FY2018 · GM · contact PI
Predicting In-hospital Cardiac Arrest Using Electronic Health Record Data$163,944
K08 · FY2018 · HL · contact PI
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)$417,445
R01 · FY2017 · GM · contact PI
Predicting In-hospital Cardiac Arrest Using Electronic Health Record Data$163,944
K08 · FY2017 · HL · contact PI
Predicting In-hospital Cardiac Arrest Using Electronic Health Record Data$163,944
K08 · FY2016 · HL · contact PI
Predicting In-hospital Cardiac Arrest Using Electronic Health Record Data$129,546
K08 · FY2015 · HL · contact PI
Predicting In-hospital Cardiac Arrest Using Electronic Health Record Data$129,546
K08 · FY2014 · HL · contact PI