ArticleCritical care explorations2021
Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients.
Article in Critical care explorations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Comparison of Machine Learning Algorithms Identifying Children at Increased Risk of Out-of-Home Placement: Development and Practical Considerations.Health services research · 2025Article
- Article
- Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.Frontiers in digital health · 2024Review
- Article
- The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.Frontiers in pediatrics · 2022Article
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Authors and funding
8 authors.
Funding
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.
designPredictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO).
settingChildren hospitalized in ICUs. PATIENTS: Children with greater than or equal to one ICU admission (
interventionsNone. MEASUREMENT AND MAIN
resultsInitially, we undersampled non-ICU patients for development and comparison of the models. We randomly assigned 64% of patients for training, 8% for validation, and 28% for testing in both clinical groups. Two additional validation cohorts were tested: a simulated children's hospitals and the 2017-2018 cohort. The main outcome was ICU care or non-ICU care in four future time periods based on physiology, therapy, and care intensity. Four independent, sequential, and fully connected neural networks were calibrated to risk of ICU care at each time period. Performance for all models in the test sample were comparable including sensitivity greater than or equal to 0.727, specificity greater than or equal to 0.885, accuracy greater than 0.850, area under the receiver operating characteristic curves greater than or equal to 0.917, and all had excellent calibration (all
conclusionsMachine learning models using physiology, therapy, and care intensity predicting future care needs had promising performance metrics. Notably, performance metrics were similar as the prediction time periods increased from 6-12 hours to 24-30 hours.
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