Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation.

ABSTRACT:

Background

In medical education, enhancing thinking skills is vital. The Virtual Diagnosis and Treatment Platform (VP) refines medical students' diagnostic abilities through interactive patient interviews (simulated patient interactions). By analyzing the questions asked during these interviews, the VP evaluates students' aptitude in medical history inquiries, offering insights into their thinking capabilities. This study aimed to extract insights from case summaries and patient interviews to improve evaluation and feedback in medical education.

Methods

This study employs a systematic approach to knowledge-point classification by utilizing both simple long short-term memory (LSTM)-based and Siamese-based networks, coupled with cross-validation techniques. The dataset under s

SUBMITTER: Lin YL 

PROVIDER: S-EPMC11763137 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature