Let's ask the patient: disease prediction based on patients' symptom descriptions in free text.

ABSTRACT:

Objective

This study evaluates the value self-reported free-text symptom descriptions for supporting diagnostic decisions in osteoarthritis (OA), fibromyalgia (FM) and immune-mediated rheumatic diseases (imRD) using natural language processing (NLP) and machine learning (ML).

Methods

Free-text descriptions from 8454 patients were processed using a word-weighting method (TF-IDF vectorization) that reflects how relevant each word is across the dataset, and then classified with support vector machine (SVM) models. OA and FM models were optimized for specificity and the imRD model for sensitivity based on disease context and validated against an independent dataset. Model explainability was explored using SHapley Additive exPlanations (SHAP) values.

Results

The SVM models

SUBMITTER: Perez-Sancristobal I 

PROVIDER: S-EPMC12456275 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature