Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012
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Mark_Predicting in-hospital.pdf
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Author(s)
Silva, Ikaro
Moody, George B.
Scott, Daniel J.
Celi, Leo Anthony G.
Mark, Roger Greenwood
Date Issued
September 2012
Journal
Computing in Cardiology
Publisher
Computing in Cardiology
Citation
Silva, Ikaro, George Moody, Daniel J Scott, Leo A. Celi, and Roger G Mark. "Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012." Computing in Cardiology 2012, Volume 39. p.245-248.
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Author's final manuscript
Abstract
Acuity scores, such as APACHE, SAPS, MPM, and SOFA, are widely used to account for population differ ences in studies aiming to compare how medications, care guidelines, surgery, and other interventions impact mortality in Intensive Care Unit (ICU) patients. By contrast, the focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality. The data used for the challenge consisted of 5 general descriptors and 36 time series (measurements of vital signs and laboratory results) from the first 48 hours of the first available ICU stay of 12,000 adult patients from the MIMIC II database. The challenge was organized as two events: event 1 measured performance of a binary classifier, and event 2 measured performance of a risk estimator. The score of event 1 was the lower of sensitivity and positive predictive value. The score for event 2 was a range-normalized Hosmer-Lemeshow statistic. A baseline algorithm (using SAPS-1) obtained event 1 and 2 scores of 0.3125 and 68.58 respectively. Most participants submitted entries that outperformed the baseline algorithm. The top final scores for events 1 and 2 were 0.5353 and 17.88 respectively.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
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DOI of Published Version
http://www.cinc.org/archives/2012/pdf/0245.pdf
