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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Mortality prediction based on imbalanced high-dimensional ICU big data
ترجمه فارسی عنوان مقاله:
پیش بینی مرگ و میر بر اساسداده های بزرگ ICU عدم تعادل بعد بالا
منبع:
Sciencedirect - Elsevier - Computers in Industry, 98 (2018) 218-225: doi:10:1016/j:compind:2018:01:017
نویسنده:
Jiankang Liua,b, Xian Xiang Chena, Lipeng Fanga,b, Jun Xia Lic, Ting Yangd, Qingyuan Zhand,***, Kai Tongb,**, Zhen Fanga,e,*
چکیده انگلیسی:
With the development of biomedical equipment and healthcare level, large amounts of data have been
brought out in hospital, especially in Intensive Care Unit (ICU). However, how to better exploit
meaningful information from these rich data still remains a challenge. This paper focuses on ICU
mortality prediction, which is a typical example of second use of ICU big data. Patient ICU mortality
prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution and
time asynchronization etc. To solve these challenges, a series of analytical methods and tools, including
variables selection, preprocessing, feature extraction & feature selection and predictive modeling, have
been utilized and developed. High-dimensional and unbalanced natures of the ICU data badly affect the
performance of classifiers. We modified the cost-sensitive principal component analysis (CSPCA), which
is denoted by MCSPCA, to handle these problems in feature extraction stage. As for parameter
optimization, a variant of standard particle swarm optimization called chaos particle swarm optimization
(CPSO) was adopted for its capacity of finding optimal solution. In order to obtain the best prediction
model, different algorithms were investigated and their AUC performances were evaluated in a large real
world benchmark data. The final results show that our proposed method improved the performance of
the traditional machine learning methods, in which the support vector machine (SVM) reach best AUC
performance of 0.7718. This study gives a paradigm to handle similar problems in big health data and
helps promote healthcare services.
Keywords: Health data processing ، Analytical tools ، Modified cost-sensitive principal ، component analysis ، Support vector machine ، Chaos particle swarm optimization
قیمت: رایگان
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