Retrieval-Based Diagnostic Decision Support: Mixed Methods Study.

ABSTRACT:

Background

Diagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.

Objective

This study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support.

Methods

We introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify

SUBMITTER: Abdullahi T 

PROVIDER: S-EPMC11222760 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature