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Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity.

Publication ,  Journal Article
Sabharwal, P; Hurst, JH; Tejwani, R; Hobbs, KT; Routh, JC; Goldstein, BA
Published in: BMC Med Inform Decis Mak
March 29, 2022

BACKGROUND: Clinical decision support (CDS) tools built using adult data do not typically perform well for children. We explored how best to leverage adult data to improve the performance of such tools. This study assesses whether it is better to build CDS tools for children using data from children alone or to use combined data from both adults and children. METHODS: Retrospective cohort using data from 2017 to 2020. Participants include all individuals (adults and children) receiving an elective surgery at a large academic medical center that provides adult and pediatric services. We predicted need for mechanical ventilation or admission to the intensive care unit (ICU). Predictor variables included demographic, clinical, and service utilization factors known prior to surgery. We compared predictive models built using machine learning to regression-based methods that used a pediatric or combined adult-pediatric cohort. We compared model performance based on Area Under the Receiver Operator Characteristic. RESULTS: While we found that adults and children have different risk factors, machine learning methods are able to appropriately model the underlying heterogeneity of each population and produce equally accurate predictive models whether using data only from pediatric patients or combined data from both children and adults. Results from regression-based methods were improved by the use of pediatric-specific data. CONCLUSIONS: CDS tools for children can successfully use combined data from adults and children if the model accounts for underlying heterogeneity, as in machine learning models.

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Published In

BMC Med Inform Decis Mak

DOI

EISSN

1472-6947

Publication Date

March 29, 2022

Volume

22

Issue

1

Start / End Page

84

Location

England

Related Subject Headings

  • Retrospective Studies
  • Medical Informatics
  • Machine Learning
  • Intensive Care Units
  • Humans
  • Hospitalization
  • Decision Support Systems, Clinical
  • Child
  • Adult
  • 4203 Health services and systems
 

Citation

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ICMJE
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Sabharwal, P., Hurst, J. H., Tejwani, R., Hobbs, K. T., Routh, J. C., & Goldstein, B. A. (2022). 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(1), 84. https://doi.org/10.1186/s12911-022-01827-4
Sabharwal, Paul, Jillian H. Hurst, Rohit Tejwani, Kevin T. Hobbs, Jonathan C. Routh, and Benjamin A. Goldstein. “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, no. 1 (March 29, 2022): 84. https://doi.org/10.1186/s12911-022-01827-4.
Sabharwal P, Hurst JH, Tejwani R, Hobbs KT, Routh JC, Goldstein BA. 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 Mar 29;22(1):84.
Sabharwal, Paul, 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, vol. 22, no. 1, Mar. 2022, p. 84. Pubmed, doi:10.1186/s12911-022-01827-4.
Sabharwal P, Hurst JH, Tejwani R, Hobbs KT, Routh JC, Goldstein BA. 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 Mar 29;22(1):84.
Journal cover image

Published In

BMC Med Inform Decis Mak

DOI

EISSN

1472-6947

Publication Date

March 29, 2022

Volume

22

Issue

1

Start / End Page

84

Location

England

Related Subject Headings

  • Retrospective Studies
  • Medical Informatics
  • Machine Learning
  • Intensive Care Units
  • Humans
  • Hospitalization
  • Decision Support Systems, Clinical
  • Child
  • Adult
  • 4203 Health services and systems