---
title: >-
  Machine learning–based identification of moderate-to-severe atopic dermatitis
  in US patients using the TARGET-DERM AD registry.
description: >-
  Explore this publication on real-world evidence: Machine learning–based
  identification of moderate-to-severe atopic dermatitis in US patients using
  the…
date: '2026-01-01'
category: Publications
tags:
  - Poster Presentation
  - R&D
  - Care
  - Dermatology
  - Atopic Dermatitis (AD)
  - Pharma Partner
canonical_url: >-
  https://www.pedestalhealth.com/resources/publications/poster-presentation/machine-learning-based-identification-of-moderate-to-severe/
source: Pedestal Health
license: © 2026 Pedestal Health. All rights reserved.
slug: machine-learning-based-identification-of-moderate-to-severe
id: 6ku8dYWd2dZxMd44rwmltO
contentType: article
---

## Challenge

Identifying patients with moderate-to-severe AD in large real-world datasets typically requires manual chart review or IGA-based classification, which is resource-intensive and limits scalability for population-level analyses. There was no validated, automated approach to distinguish moderate-to-severe AD patients from milder cases using structured EHR data in the TARGET-DERM registry.

## Solution

A machine learning algorithm was developed and validated using TARGET-DERM AD registry data to identify patients with moderate-to-severe AD based on structured EHR variables, creating a scalable, automated classification tool applicable to large real-world populations.

## Impact

A validated ML-based patient identification algorithm enables rapid, reproducible cohort construction for population-level AD analyses without manual IGA review, directly supporting Amgen/Kyowa Kirin's development programs by enabling scalable real-world evidence generation from the TARGET-DERM infrastructure.