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http://dx.doi.org/10.14400/JDC.2021.19.7.271

Convergence study to predict length of stay in premature infants using machine learning  

Kim, Cheok-Hwan (Medical Information Team, Soonchunhyang University Cheonan Hospital)
Kang, Sung-Hong (Dept. of Health Policy & Management, Inje University)
Publication Information
Journal of Digital Convergence / v.19, no.7, 2021 , pp. 271-282 More about this Journal
Abstract
This study was conducted to develop a model for predicting the length of stay for premature infants through machine learning. For the development of this model, 6,149 cases of premature infants discharged from the hospital from 2011 to 2016 of the discharge injury in-depth survey data collected by the Korea Centers for Disease Control and Prevention were used. The neural network model of the initial hospitalization was superior to other models with an explanatory power (R2) of 0.75. In the model added by converting the clinical diagnosis to CCS(Clinical class ification software), the explanatory power (R2) of the cubist model was 0.81, which was superior to the random forest, gradient boost, neural network, and penalty regression models. In this study, using national data, a model for predicting the length of stay for premature infants was presented through machine learning and its applicability was confirmed. However, due to the lack of clinical information and parental information, additional research is needed to improve future performance.
Keywords
Length of stay; machine learning; cubist; artificial neural network; convergence study;
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