MedReadr: Development and Evaluation of an In-Browser, Rule-Based Natural Language Processing Algorithm to Estimate the Reliability of Consumer Health Articles.

ABSTRACT:

Introduction

The internet is a major source of medical information for patients, yet the quality of online health content remains highly variable. Existing assessment tools are often labor-intensive, invalidated, or limited in scope. We developed and validated MedReadr, an in-browser, rule-based natural language processing (NLP) algorithm that automatically estimates the reliability of consumer health articles for patients and providers.

Methods

Thirty-five consumer medical articles were independently assessed by two reviewers using validated manual scoring systems (QUEST and Sandvik). Interrater reliability was evaluated with Cohen's κ, and metrics with κ > 0.6 were selected for model fitting. MedReadr extracted key features from article text and metadata using predefined N

SUBMITTER: Winograd J 

PROVIDER: S-EPMC12503773 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature