Leveraging Case Narratives to Enhance Patient Age Ascertainment from Adverse Event Reports.

ABSTRACT:

Introduction

Missing age presents a significant challenge when evaluating individual case safety reports (ICSRs) in the FDA Adverse Event Reporting System (FAERS). When age is missing in an ICSR's structured field, it may be in the report's free-text narrative.

Objectives

This study aimed to evaluate the performance and assess the potential impact of a rule-based natural language processing (NLP) tool that utilizes a text string search to identify patients' numerical age from unstructured narratives.

Methods

Using FAERS ICSRs from 2002 to 2018, we evaluated the annual proportion of ICSRs with age missing in the structured field before and after NLP application. Reviewers manually identified patients' age from ICSR narratives (gold standard) from a random sample of 150

SUBMITTER: Pham P 

PROVIDER: S-EPMC9136956 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature