An intelligent warning model for early prediction of cardiac arrest in sepsis patients

•The effectiveness of a wide range of classical and ensemble machine learning techniques in predicting cardiac arrest were evaluated through a systematic approach.•Patient's time series dynamics of vital signs was investigated as a new factor for predicting cardiac arrest.•Cardiac arrest incide...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Computer methods and programs in biomedicine Ročník 178; s. 47 - 58
Hlavní autoři:	Layeghian Javan, Samaneh, Sepehri, Mohammad Mehdi, Layeghian Javan, Malihe, Khatibi, Toktam
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Ireland Elsevier B.V 01.09.2019
Témata:	Adolescent Adult Algorithms APACHE Case-Control Studies Decision Trees Electronic Health Records Female Heart arrest Heart Arrest - complications Heart Arrest - diagnosis Heart Arrest - epidemiology Humans Incidence Intelligent warning model Machine Learning Male Middle Aged Monitoring, Ambulatory - methods Multivariate Analysis Normal Distribution Prediction Reproducibility of Results Sensitivity and Specificity Sepsis Sepsis - complications Sepsis - physiopathology Severity of Illness Index Vital Signs Young Adult NN B D LR ENR FPR GLM CHP KNN BM SVM FS HRV bp EHR AUC DT MEWS Intelligent warning model MLT T MICE APPACHE CA Acc DSR Prec Prediction ECG F1 ROSC LAB Heart arrest NB RF Sepsis ICU Spec Sen
ISSN:	0169-2607, 1872-7565, 1872-7565
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!