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

Wei Ting Lin, Tsung Yu Wu, Yen Ju Chen, Yu Shan Chang, Chyi Her Lin, Yuh Jyh Lin

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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 LOS prediction models using machine learning (ML) techniques. Methods: This retrospective study illustrated LOS data from VLBW infants born between 2016 and 2018 registered in the Taiwan Neonatal Network. Among infants discharged alive, continuous variables (LOS or postmenstrual age, PMA) and categorical variables (late and non-late discharge group) were used as outcome variables to build prediction models. We used 21 early neonatal variables and six algorithms. The performance was compared using the coefficient of determination (R2) for continuous variables and area under the curve (AUC) for categorical variables. Results: A total of 3519 VLBW infants were included to illustrate the profile of LOS. We found 59% of mortalities occurred within the first 7 days after birth. The median of LOS among surviving and deceased infants was 62 days and 5 days. For the ML prediction models, 2940 infants were enrolled. Prediction of LOS or PMA had R2 values less than 0.6. Among the prediction models for prolonged LOS, the logistic regression (ROC: 0.724) and random forest (ROC: 0.712) approach had better performance. Conclusion: We provide a benchmark of LOS among VLBW infants in each gestational age group in Taiwan. ML technique can improve the accuracy of the prediction model of prolonged LOS of VLBW.

Original languageEnglish
Pages (from-to)1141-1148
Number of pages8
JournalJournal of the Formosan Medical Association
Issue number6
Publication statusPublished - 2022 Jun

All Science Journal Classification (ASJC) codes

  • General Medicine


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