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Correction to: Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity
BMC Medical Informatics and Decision Making volume 22, Article number: 128 (2022)
The Original Article was published on 29 March 2022
Correction to: BMC Medical Informatics and Decision Making (2022) 22:84 https://doi.org/10.1186/s12911-022-01827-4
Following publication of the original article [1], it was reported that part of the ‘Outcome Variable Definition’ and the entirety of the ‘Descriptive statistics’ subsection was missing. These two subsections are given below with the previously missing text highlighted in bold. The original article [1] has been updated.
Outcome Variable Definition
In the initial development of the CDS tool, we were tasked with predicting four outcomes related to hospital resource utilization: overall length of stay, admission to the intensive care unit (ICU), requirement for mechanical ventilation, and discharge to a skilled nursing facility. Because children are rarely discharged to a skilled nursing facility and evaluating continuous outcomes poses unique challenges, we focused on the two binary outcomes: admission to the ICU and requirement for mechanical ventilation.
Statistical Analysis
Descriptive statistics
We compared the pediatric and adult patient populations. We report standardized mean differences (SMDs) where an SMD > 0.10 indicates that the two groups are out of balance.
Reference
Sabharwal P, Hurst JH, Tejwani R, et al. Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity. BMC Med Inform Decis Mak. 2022;22:84. https://doi.org/10.1186/s12911-022-01827-4.
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Sabharwal, P., Hurst, J.H., Tejwani, R. et al. Correction to: Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity. BMC Med Inform Decis Mak 22, 128 (2022). https://doi.org/10.1186/s12911-022-01846-1
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DOI: https://doi.org/10.1186/s12911-022-01846-1