BACKGROUND: Venoarterial (VA) extracorporeal membrane oxygenation (ECMO) undoubtedly saves many lives, but it is associated with a high degree of patient morbidity, mortality, and resource use. This study aimed to develop a machine learning algorithm...
CONTEXT: The critical nature of patients in Intensive Care Units (ICUs) demands intensive monitoring of their vital signs as well as highly qualified professional assistance. The combination of these needs makes ICUs very expensive, which requires in...
Journal of vascular and interventional radiology : JVIR
May 4, 2020
PURPOSE: To demonstrate that random forest models trained on a large national sample can accurately predict relevant outcomes and may ultimately contribute to future clinical decision support tools in IR.
International journal of medical informatics
Apr 15, 2020
BACKGROUND: Febrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation...
BACKGROUND: Many mortality prediction models have been developed for patients in intensive care units (ICUs); most are based on data available at ICU admission. We investigated whether machine learning methods using analyses of time-series data impro...
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral ...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
In evolving clinical environments, the accuracy of prediction models deteriorates over time. Guidance on the design of model updating policies is limited, and there is limited exploration of the impact of different policies on future model performanc...
OBJECTIVES: Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critic...
BACKGROUND: Acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions requiring emergency specialist management. The aim of this study was to build a prediction algorithm to assist prehospital triage of AAS.
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