本文探讨了通过应用人工智能（AI）技术利用电子健康记录（EHR）进行个性化健康研究的潜力，具体命名实体识别(NER)。通过从临床文本中提取关键的患者信息，包括诊断,药物,症状,和实验室测试，人工智能有助于快速识别相关数据，为未来的护理范式铺平道路。该研究的重点是非小细胞肺癌（NSCLC）在意大利的临床记录，引入一组新的29个临床实体，包括是否存在（否定）与NSCLC相关的相关信息。使用在意大利生物医学文本上预先训练的最先进的模型，我们取得了有希望的结果(平均F1分数为80.8%)，证明了采用人工智能提取意大利语生物医学信息的可行性。
This paper explores the potential of leveraging electronic health records (
EHRs) for personalized health research through the application of artificial intelligence (AI) techniques, specifically Named Entity Recognition (NER). By extracting crucial patient information from clinical texts, including diagnoses, medications, symptoms, and lab tests, AI facilitates the rapid identification of relevant data, paving the way for future care paradigms. The study focuses on Non-small cell lung cancer (NSCLC) in Italian clinical notes, introducing a novel set of 29 clinical entities that include both presence or absence (negation) of relevant information associated with NSCLC. Using a state-of-the-art model pretrained on Italian biomedical texts, we achieve promising results (average F1-score of 80.8%), demonstrating the feasibility of employing AI for extracting biomedical information in the Italian language.
