一致性
人工智能
自然语言处理
乳腺癌
计算机科学
信息抽取
机器学习
医学
数据集
文本挖掘
算法
癌症
肿瘤科
内科学
作者
Elisabetta Munzone,Antonio Marra,Federico Comotto,Luca del Guercio,Enrico Cassano,M. Lo Cascio,Eleonora Pagan,Davide Sangalli,Ilaria Bigoni,Francesca Porta,Marianna D’Ercole,Fabiana Ritorti,Vincenzo Bagnardi,Nicola Fusco,Giuseppe Curigliano
出处
期刊:JCO clinical cancer informatics
[American Society of Clinical Oncology]
日期:2024-08-01
卷期号: (8)
摘要
PURPOSE Electronic health records (EHRs) are valuable information repositories that offer insights for enhancing clinical research on breast cancer (BC) using real-world data. The objective of this study was to develop a natural language processing (NLP) model specifically designed to extract structured data from BC pathology reports written in natural language. METHODS During the initial phase, the algorithm's development cohort comprised 193 pathology reports from 116 patients with BC from 2012 to 2016. A rule-based NLP algorithm was applied to extract 26 variables for analysis and was compared with the manual extraction of data performed by both a data entry specialist and an oncologist. Following the first approach, the data set was expanded to include 513 reports, and a Named Entity Recognition (NER)-NLP model was trained and evaluated using K-fold cross-validation. RESULTS The first approach led to a concordance analysis, which revealed an 82.9% agreement between the algorithm and the oncologist, whereas the concordance between the data entry specialist and the oncologist was 90.8%. The second training approach introduced the definition of an NER-NLP model, in which the accuracy showed remarkable potential (97.8%). Notably, the model demonstrated remarkable performance, especially for parameters such as estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 (F1-score 1.0). CONCLUSION The present study aligns with the rapidly evolving field of artificial intelligence (AI) applications in oncology, seeking to expedite the development of complex cancer databases and registries. The results of the model are currently undergoing postprocessing procedures to organize the data into tabular structures, facilitating their utilization in real-world clinical and research endeavors.
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