کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
504789 864430 2016 5 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
A computational approach to early sepsis detection
ترجمه فارسی عنوان
یک روش محاسباتی برای تشخیص عفونت زودرس
کلمات کلیدی
سپسیس؛ سپسیس شدید؛ انفورماتیک پزشکی؛ تشخیص زود هنگام؛ تشخیص به کمک کامپیوتر ؛ سیستم های پشتیبانی تصمیم گیری بالینی
Sepsis; Severe sepsis; Medical informatics; Early diagnosis; Computer-assisted diagnosis; Clinical decision support systems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی

ObjectiveTo develop high-performance early sepsis prediction technology for the general patient population.MethodsRetrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission.ResultsA sepsis early warning algorithm, InSight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode. When applied to a never-before-seen set of test patients, InSight predictions demonstrated a sensitivity of 0.90 (95% CI: 0.89–0.91) and a specificity of 0.81 (95% CI: 0.80–0.82), exceeding or rivaling that of existing biomarker detection methods. Across predictive times up to three hours before a sustained SIRS event, InSight maintained an average area under the ROC curve of 0.83 (95% CI: 0.80–0.86). Analysis of patient sepsis risk showed that contributions from the coevolution of multiple risk factors were more important than the contributions from isolated individual risk factors when making predictions further in advance.ConclusionsSepsis can be predicted at least three  hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 74, 1 July 2016, Pages 69–73
نویسندگان
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