# Predicting Pulmonary and Cardiac Morbidity in Preterm Infants with Deep Learning

> **NIH NIH K01** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2021 · $166,320

## Abstract

RESEARCH SUMMARY
The goal of this award is to provide Andrew Beam, PhD with research support and comprehensive mentoring
designed to transition him to an independent investigator in perinatal and neonatal informatics. Preterm labor
(PTL) is labor which occurs before 37 weeks of gestation and carries with it enormous health and financial
consequences. Preterm infants have some of the highest levels of pulmonary and cardiac morbidity, yet
machine-learning techniques for these important outcomes remains under developed. The research strategy is
focused developing predictive models for two very important clinical scenarios using large sources of existing
healthcare data. The focus of Specific Aim 1 develops a new form of machine learning known as deep learning
for predicting PTL in pregnant women, while the focus of Specific Aim 2 investigates the use of deep learning
for predicting clinical trajectories of preterm infants in the NICU. Currently, management and anticipation of
both clinical scenarios is challenging and advancement in our predictive capacity could dramatically improve
the quality and efficiency of the healthcare system. These models will be built using an existing database of 50
million patient-lives obtained through a partnership with a major US health insurer. Specific Aim 3 seeks to
understand how the models constructed using this unique data resource translate and generalize to data from
the electronic health records of Boston-area hospitals, which is a key concern for all healthcare data scientists.
The education plan focuses on augmenting Dr. Beam’s graduate degrees in statistics and bioinformatics with
additional training in clinical medicine and human pathology. This additional education will grant Dr. Beam a
deeper understanding of the clinical problems faced by these populations and will allow for more fluid
collaborations with clinicians in the future. The composition of Dr. Beam’s mentorship committee, which
includes expertise in neonatology, biostatistics, and translational informatics, reflects his long-term desire to be
quantitative scientist who works side-by-side practicing physicians so that quantitative research is translated
into impactful clinical practice.

## Key facts

- **NIH application ID:** 10198019
- **Project number:** 5K01HL141771-04
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Andrew L. Beam
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $166,320
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10198019

## Citation

> US National Institutes of Health, RePORTER application 10198019, Predicting Pulmonary and Cardiac Morbidity in Preterm Infants with Deep Learning (5K01HL141771-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10198019. Licensed CC0.

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