# Phenotyping Support Core

> **NIH NIH P50** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $242,190

## Abstract

PROJECT SUMMARY – SUPPORT CORE
The recent widespread adoption of electronic health records (EHRs) provides the opportunity to leverage real-
world data generated as a byproduct of healthcare for clinical discovery. EHRs contain a wealth of valuable
information, particularly for pregnant women, women who just gave birth, and for children, as these individuals
have scheduled health care visits on a frequent basis which provide a source of longitudinal data. Further,
these populations have been previously under-represented in research efforts. However, the data in EHRs are
not always easily accessible for research. Pregnancy and childhood have not been focal points for
development of phenotyping methods in EHRs. Algorithms to assess specific drug exposures, important
covariates (which may change rapidly), and outcomes (which may be population-specific) have not been
validated for obstetric or pediatric populations. The Phenotyping Support Core will provide full technical support
for phenotyping within the proposed Center and other sites of the MRPRINT Hub. In Aim 1, the Core will
support the Project 1 and Project 2 by assisting with medication exposure, therapeutic response, and adverse
event outcomes. We will develop and validate natural language processing pipelines and machine learning
approaches to extract drug outcomes. We will define and evaluate algorithms for detecting neonatal opioid
withdrawal syndrome (NOWS) and related maternal and infant outcomes. We will implement the algorithms
and organize datasets and results into common data models. In Aim 2, we will support the work of the
MPRINT-Hub by assisting with validation of findings at other sites using real-world, EHR data, including
Vanderbilt’s BioVU biobank resource. In Aim 3, we will develop and disseminate tools for EHR-based
therapeutics research for the scientific community. We will assemble a suite of existing and new analytical
tools for MPRINT research. All the tools and algorithms generated through the core will be published and
shared on PheKB and with MPRINT Hub platforms, as appropriate. The tools generated from this project will
significantly enhance our efficiency when using EHRs and offer a potential approach to identify new
phenotypes and to generate novel research hypotheses for the benefit of pediatric and maternal populations.

## Key facts

- **NIH application ID:** 10480935
- **Project number:** 5P50HD106446-02
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Wei-Qi Wei
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $242,190
- **Award type:** 5
- **Project period:** 2021-09-10 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10480935

## Citation

> US National Institutes of Health, RePORTER application 10480935, Phenotyping Support Core (5P50HD106446-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10480935. Licensed CC0.

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