---
title: Prediction in claims databases for epidemiological research
description: >-
  Explore this publication on real-world evidence: Prediction in claims
  databases for epidemiological research.
date: '2024-01-01'
author: David Pritchard
category: Publications
tags:
  - PMSA Annual Conference
  - Poster Presentation
  - Approval & Commercialization
  - 'Yes'
  - Methods/Pharmacoepi
  - R&D
canonical_url: >-
  https://www.headwaterscience.com/resources/publications/pub-pmsa-annual-conference/prediction-in-claims-databases-for-epidemiological-research/
source: Headwater Science
license: © 2026 Headwater Science. All rights reserved.
slug: prediction-in-claims-databases-for-epidemiological-research
id: 1LaNXvc2Lij5ZZiyiATKS4
contentType: article
---

## Challenge

Machine learning and prediction modeling in healthcare claims databases face unique challenges—including right censoring, feature engineering for longitudinal data, and index date selection—that leave many applied researchers without practical guidance.

## Solution

Target RWE researchers presented a framework for prediction in healthcare claims databases covering index date selection, longitudinal feature engineering strategies, model training with right-censored data, and the potential of deep learning for sequential healthcare event data.

## Impact

Providing an applied prediction modeling framework for claims databases builds Target RWE's methodological differentiation in machine learning-based patient identification and risk stratification.

## Use Cases / Links

Claims-based prediction modeling framework for patient identification and risk stratification in pharma programs, Machine learning methodology guidance for right-censored healthcare data in RWE studies