کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
383292 660815 2012 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Data mining using clinical physiology at discharge to predict ICU readmissions
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Data mining using clinical physiology at discharge to predict ICU readmissions
چکیده انگلیسی

Patient readmissions to intensive care units (ICUs) are associated with increased mortality, morbidity and costs. Current models for predicting ICU readmissions have moderate predictive value, and can utilize up to twelve variables that may be assessed at various points of the ICU inpatient stay. We postulate that greater predictive value can be achieved with fewer physiological variables, some of which can be assessed in the 24 h before discharge. A data mining approach combining fuzzy modeling with tree search feature selection was applied to a large retrospectively collected ICU database (MIMIC II), representing data from four different ICUs at Beth Israel Deaconess Medical Center, Boston. The goal was to predict ICU readmission between 24 and 72 h after ICU discharge. Fuzzy modeling combined with sequential forward selection was able to predict readmissions with an area under the receiver-operating curve (AUC) of 0.72 ± 0.04, a sensitivity of 0.68 ± 0.02 and a specificity of 0.73 ± 0.03. Variables selected as having the highest predictive power include mean heart rate, mean temperature, mean platelets, mean non-invasive arterial blood pressure (mean), mean spO2, and mean lactic acid, during the last 24 h before discharge. Collection of the six predictive variables selected is not complex in modern ICUs, and their assessment may help support the development of clinical management plans that potentially mitigate the risk of readmission.


► No predictive models based on physiological variables at ICU discharge have yet been developed.
► A new combination of variables not previously linked to ICU readmission is presented.
► The low number of features selected denotes significant gains in terms of simplicity of the model.
► Significantly better performance than APACHE II or APACHE III scores are obtained.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 18, 15 December 2012, Pages 13158–13165
نویسندگان
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