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 interv...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02866-3 |
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| author | Yih-Lon Lin Yu-Min Chiang Tsuen-Chiuan Tsai Sheng-Gui Su |
| author_facet | Yih-Lon Lin Yu-Min Chiang Tsuen-Chiuan Tsai Sheng-Gui Su |
| author_sort | Yih-Lon Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-35f5932c24e245168f3e696cc3777077 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-35f5932c24e245168f3e696cc37770772025-01-26T12:36:54ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125111110.1186/s12911-025-02866-3Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulationYih-Lon Lin0Yu-Min Chiang1Tsuen-Chiuan Tsai2Sheng-Gui Su3Department of Computer Science and Information Engineering, National Yunlin University of Science and TechnologyDepartment of Automation Engineering, National Formosa UniversityLandseed International HospitalDepartment of Computer Science and Information Engineering, National Yunlin University of Science and TechnologyAbstract 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.https://doi.org/10.1186/s12911-025-02866-3Knowledge pointsSiamese networksConvolutional neural network (CNN)Long short-term memory (LSTM)K-fold cross-validation |
| spellingShingle | Yih-Lon Lin Yu-Min Chiang Tsuen-Chiuan Tsai Sheng-Gui Su Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation BMC Medical Informatics and Decision Making Knowledge points Siamese networks Convolutional neural network (CNN) Long short-term memory (LSTM) K-fold cross-validation |
| title | Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation |
| title_full | Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation |
| title_fullStr | Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation |
| title_full_unstemmed | Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation |
| title_short | Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation |
| title_sort | knowledge point classification using simple lstm based and siamese based networks for virtual patient simulation |
| topic | Knowledge points Siamese networks Convolutional neural network (CNN) Long short-term memory (LSTM) K-fold cross-validation |
| url | https://doi.org/10.1186/s12911-025-02866-3 |
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