| Autor: |
Yingxuan Guo, Changke Huang, Yaying Sheng, Wenjie Zhang, Xin Ye, Hengli Lian, Jiahao Xu, Yiqi Chen |
| Rok vydání: |
2023 |
| DOI: |
10.21203/rs.3.rs-2646377/v1 |
| Popis: |
Objective This article proposes a named entity recognition model for electronic medical records in ophthalmology that integrates professional vocabulary information. The aim is to achieve structured processing of important clinical decision-making data and to develop a clinical aided diagnosis platform based on this. The effectiveness of this platform in improving the efficiency and accuracy of ophthalmologists in clinical diagnosis decision-making was validated. Methods Based on the best entity recognition model, we constructed the aided diagnosis platform. By conducting a controlled experiment that compared the use of the platform by doctors with different levels of experience, we analyzed the effectiveness of the aided diagnosis platform in improving diagnosis decision-making efficiency and accuracy. Results The SoftLexicon-Glove-Word2vec model had the highest F1 score at 93.02%. Both junior and senior doctors showed significant improvement in diagnosis efficiency and accuracy (P |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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