# Using Machine Learning to Predict Clinical Deterioration in Hospitalized Children

> **NIH NIH K01** · UNIVERSITY OF CHICAGO · 2020 · $147,563

## Abstract

PROJECT SUMMARY
Children who are admitted to the hospital and experience deterioration have a high risk of mortality and poor
long-term health. Current warning early scores indicating risk of deterioration are subjectively derived and have
not reduced in-hospital mortality. In recent work, I developed a vital sign based statistical model that
demonstrated improved accuracy over current risk scores at predicting clinical deterioration in hospitalized
children 24 hours in advance. Within adults, the combination of longitudinal data analysis techniques, machine
learning, and electronic health record (EHR) data have led to highly accurate early warning scores. Therefore,
my aim in this grant proposal is to utilize longitudinal integration of EHR data in a machine learning framework
to develop a model for predicting clinical deterioration in hospitalized children as early as possible. I will do this
by first deriving and validating a prediction model using structured EHR data collected from three pediatric
hospitals (Aim 1). Using the same cohort, I will then build and validate a prediction model using features
derived from unstructured clinical notes (Aim 2). I will also compare if the addition of unstructured features
improves the prediction accuracy of the model derived in Aim 1. Finally, I will determine the association
between non-patient level environmental variables within the hospital ecosystem and risk of clinical
deterioration in hospitalized children (Aim 3). I will also determine if the addition of these environmental risk
factors improves performance of the prediction model derived through Aims 1 and 2. Completion of this
proposal will result in a validated pediatric risk prediction model that will enable clinicians to recognize early
signs of deterioration in hospitalized children. This will facilitate timely intervention, thereby saving lives and
improving long-term health. In addition, this grant will also provide me with crucial data for a future R01 trial
aimed at assessing the impact of the prediction model in reducing mortality, decreasing costs, and improving
long-term outcomes in hospitalized children. To establish myself as an independent investigator in pediatric
prediction modeling, I propose a training plan that includes comprehensive didactics and mentorship in the
areas of longitudinal data analysis, advanced machine learning, natural language processing, and concepts in
pediatric care. I have assembled a first-class mentorship team comprised of national experts in longitudinal
data analysis techniques and EHR-based machine learning (Robert Gibbons PhD and Matthew Churpek MD,
PhD). My advisory team is comprised of experts in natural language processing (Dmitriy Dligach PhD), clinical
decision support around deterioration events (Dana Edelson MD, MS and Priti Jani MD), and pediatric early
warning scores (Christopher Parshuram MB., ChB., D. Phil., FRACP). By completing my research and career
development goals, I will develop ...

## Key facts

- **NIH application ID:** 9969192
- **Project number:** 5K01HL148390-02
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Anoop Mayampurath
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $147,563
- **Award type:** 5
- **Project period:** 2019-07-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9969192

## Citation

> US National Institutes of Health, RePORTER application 9969192, Using Machine Learning to Predict Clinical Deterioration in Hospitalized Children (5K01HL148390-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9969192. Licensed CC0.

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