Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients.

ABSTRACT: Develop and compare separate prediction models for ICU and non-ICU care for hospitalized children in four future time periods (6-12, 12-18, 18-24, and 24-30 hr) and assess these models in an independent cohort and simulated children's hospital.

Design

Predictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO).

Setting

Children hospitalized in ICUs.

Patients

Children with greater than or equal to one ICU admission (n = 20,014) and randomly selected routine care children without ICU admission (n = 20,130) from 2009 to 2016 were used for model development and validation. An independent 2017-2018 cohort consisted of 80,089 children.

Interventions

None.

Measurement and main results

Initially, we undersam

SUBMITTER: Trujillo Rivera EA 

PROVIDER: S-EPMC8357255 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature