Authors:
Ikuo Keshi
1
;
2
;
Ryota Daimon
2
;
Yutaka Takaoka
3
;
4
and
Atsushi Hayashi
5
;
4
Affiliations:
1
AI & IoT Center, Fukui University of Technology, 3-6-1, Gakuen, Fukui, Fukui, Japan
;
2
Electrical, Electronic and Computer Engineering Course, Department of Applied Science and Engineering, Fukui University of Technology, 3-6-1, Gakuen, Fukui, Fukui, Japan
;
3
Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Toyama, Japan
;
4
Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Toyama, Japan
;
5
Department of Ophthalmology, University of Toyama, 2630 Sugitani, Toyama, Toyama, Japan
Keyword(s):
Generative AI, Electronic Medical Record (EMR), Chief Complaints, Disease Name Estimation, Medical AI, Medical Diagnostic Support Tool, Semantic Representation Learning, BERT, GPT-4.
Abstract:
This study compared semantic representation learning + machine learning, BERT, and GPT-4 to estimate disease names from chief complaints and evaluate their accuracy. Semantic representation learning + machine
learning showed high accuracy for chief complaints of at least 10 characters in the International Classification of Diseases 10th Revision (ICD-10) codes middle categories, slightly surpassing BERT. For GPT-4, the
Retrieval Augmented Generation (RAG) method achieved the best performance, with a Top-5 accuracy of
84.5% when all chief complaints, including the evaluation data, were used. Additionally, the latest GPT-4o
model further improved the Top-5 accuracy to 90.0%. These results suggest the potential of these methods
as diagnostic support tools. Future work aims to enhance disease name estimation through more extensive
evaluations by experienced physicians.