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 scrutiny originates from the "Clinical Diagnosis and Treatment Skills Competitions" spanning the first to third years in Taiwan. The methodology involves generating knowledge points from sequential questions posed during case summaries and patient interviews. These knowledge points are then subjected to classification using the designated neural network architectures.

Results: The experimental findings reveal promising outcomes, particularly when the Siamese-based network is used for knowledge-point classification. Through repeated (stratified) 10-fold cross validation, the accuracies achieved consistently exceeded 93%, with a standard deviation less than 0.007. These results underscore the efficacy of the proposed methodologies in enhancing virtual clinical diagnosis systems.

Conclusions: This study underscores the viability of leveraging advanced neural network architectures, particularly the Siamese-based network, for knowledge-point classification within virtual clinical diagnosis systems. By effectively discerning and classifying knowledge points derived from case summaries and patient interviews, these systems offer invaluable insights into students' thinking capabilities in medical education. The robust accuracies attained through cross-validation affirm the feasibility and efficacy of the proposed methodologies, thus paving the way for enhanced virtual clinical training platforms.