Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

作者：Yingxuan Guo, Changke Huang, Yaying Sheng, Wenjie Zhang, Xin Ye, Hengli Lian, Jiahao Xu, Yiqi Chen · 发表于：BMC Medical Informatics and Decision Making · 年份：2024 · DOI：10.1186/s12911-024-02587-z · 被引用次数：13 · 研究领域：Retinal Imaging and Analysis、Artificial Intelligence in Healthcare、Artificial Intelligence in Healthcare and Education

BACKGROUND: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. METHODS: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the pla...