Predicting in-hospital length of stay for very-low-birth-weight preterm infants using machine learning techniques

Predicting in-hospital length of stay for very-low-birth-weight preterm infants using machine learning techniques

Background/Purpose: The in-hospital length of stay (LOS) among very-low-birth-weight (VLBW, BW < 1500 g) infants is an index for care quality and affects medical resource allocation. We aimed to analyze the LOS among VLBW infants in Taiwan, and to develop and compare the performance of different...

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Bibliographic Details
Main Authors: Wei-Ting Lin, Tsung-Yu Wu, Yen-Ju Chen, Yu-Shan Chang, Chyi-Her Lin, Yuh-Jyh Lin
Format: Article
Language:English
Published: Elsevier 2022-06-01
Series:Journal of the Formosan Medical Association
Subjects:
Machine learning
Length of stay
Retrospective study
Very-low-birth-weight infants
Online Access:http://www.sciencedirect.com/science/article/pii/S092966462100440X

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http://www.sciencedirect.com/science/article/pii/S092966462100440X

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