Browsing by Author "Hobbs, Kevin T"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    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, 2022-03) Sabharwal, Paul; Hurst, Jillian H; Tejwani, Rohit; Hobbs, Kevin T; Routh, Jonathan C; Goldstein, Benjamin A

    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.
  • Thumbnail Image
    ItemOpen Access
    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, 2022-05) Sabharwal, Paul; Hurst, Jillian H; Tejwani, Rohit; Hobbs, Kevin T; Routh, Jonathan C; Goldstein, Benjamin A
    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.