A Clinical Reasoning-Encoded Case Library Developed through Natural Language Processing.

ABSTRACT:

Importance

Case reports that externalize expert diagnostic reasoning are utilized for clinical reasoning instruction but are difficult to search based on symptoms, final diagnosis, or differential diagnosis construction. Computational approaches that uncover how experienced diagnosticians analyze the medical information in a case as they formulate a differential diagnosis can guide educational uses of case reports.

Objective

To develop a "reasoning-encoded" case database for advanced clinical reasoning instruction by applying natural language processing (NLP), a sub-field of artificial intelligence, to a large case report library.

Design

We collected 2525 cases from the New England Journal of Medicine (NEJM) Clinical Pathological Conference (CPC) from 1965 to 2020 and

SUBMITTER: Zack T 

PROVIDER: S-EPMC9849536 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature