■使用标准化结构化报告(SSR)和SNOMED-CT等合适的术语可以增强数据检索和分析,促进大规模的研究和合作。然而,在我们的实验室中仍然普遍存在的叙述性报告需要替代和自动化的标签方法.在这个项目中,使用自然语言处理(NLP)方法将SNOMED-CT代码与意大利数字病理学部门的结构化和非结构化报告相关联。
■两个基于NLP的自动编码系统(支持向量机,SVM,和长短期记忆,对LSTM)进行了培训,并将其应用于一系列叙述性报告。
■用两种算法对1163例进行了测试,在准确性方面表现良好,精度,召回,和F1得分,与LSTM相比,SVM表现出略好的性能(分别为0.84、0.87、0.83、0.82和0.83、0.85、0.83、0.82)。可解释性的整合允许识别术语和重要单词组,启用微调,平衡语义含义和模型性能。
■AI工具允许病理档案的自动SNOMED-CT标记,为叙述性报告缺乏组织提供回顾性修复。
UNASSIGNED: The use of standardized structured
reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative
reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured
reports from an Italian Digital Pathology Department.
UNASSIGNED: Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative
reports.
UNASSIGNED: The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance.
UNASSIGNED: AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative
reports.