کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13434535 | 1842859 | 2019 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mortality Prediction Based on Echocardiographic Data and Machine Learning: CHF, CHD, Aneurism, ACS Cases
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
CHDAneurism - بی خوابیMixed connective tissue disease - بیماری بافت همبند ترکیبیAutoimmune disease - بیماری خودایمنیEndocrine disease - بیماری غدد درون ریزvalvular heart disease - بیماری قلبی دریچهCancer - سرطانHypertension - فشار خون بالاMortality - مرگ ومیرheart failure - نارسایی قلبیCongenital heart defect - نقص مادرزادی قلبPrediction - پیش بینیCardiomyopathy - کاردیومیوپاتی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Mortality Prediction Based on Echocardiographic Data and Machine Learning: CHF, CHD, Aneurism, ACS Cases Mortality Prediction Based on Echocardiographic Data and Machine Learning: CHF, CHD, Aneurism, ACS Cases](/preview/png/13434535.png)
چکیده انگلیسی
This paper represents the research results of echocardiographic study for early prediction of mortality. The classification task is solved by analyzing the echocardiographic data from medical information system. Echocardiographic data of 90000 hearts condition extracted directly from medical information system were analyzed. The considered echocardiographic studies were conducted for patients with CHF, CHD, hypertension, heart arrhythmia, valvular heart disease, autoimmune disease, congenital heart defect, cardiomyopathy, endocrine disease, heart failure, mixed connective tissue disease, cancer, aneurism. Using machine learning methods and neural networks it is possible to make an early prediction of mortality based on instrumental echocardiographic study. It can be offered to clinicians as support for accurate, reasonable saving clinical decisions with minimization risks for patient's health. The classification task mortality prediction is solved by machine learning methods with 97% ROC curve. The simple echocardiographic test results like FV Simpson, systolic volume, the valves condition, the condition of the ascending aorta and other echocardiographic data are used as predictors. Such a simple approach to solving critical tasks can make the method widely used in clinical practice.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 156, 2019, Pages 97-104
Journal: Procedia Computer Science - Volume 156, 2019, Pages 97-104
نویسندگان
Kirill Kutyrev, Aleksey Yakovlev, Oleg Metsker,