Sorbonne Université, Inserm, Université Sorbonne Paris-Nord, LIMICS, Paris, France.
Assistance Publique-Hôpitaux de Paris, Paris, France.
Stud Health Technol Inform. 2024 Aug 22;316:1839-1843. doi: 10.3233/SHTI240789.
This study addresses the challenge of leveraging free-text descriptions in Electronic Health Records (EHR) for clinical research and healthcare improvement. Despite the potential of this data, its direct interpretation by computers is limited. Semantic annotation emerges as a method to make EHR free text machine-interpretable but struggles with specific domain ontologies and faces heightened difficulties in psychiatry. To tackle these challenges, this study proposes a system based on unsupervised learning techniques to extract entities and their relationships, aligning them with a domain ontology. The effectiveness of this system has been validated within PsyCARE project by analyzing 60 patient discharge summaries.
本研究旨在解决利用电子健康记录（EHR）中的自由文本描述进行临床研究和改善医疗保健的挑战。尽管这些数据具有很大的潜力，但计算机直接解释这些数据的能力有限。语义标注作为一种方法，可以使 EHR 中的自由文本能够被计算机理解，但它在特定的领域本体方面存在困难，在精神病学方面更是面临巨大的挑战。为了解决这些挑战，本研究提出了一个基于无监督学习技术的系统，用于提取实体及其关系，并将其与领域本体对齐。该系统在 PsyCARE 项目中通过分析 60 份患者出院总结得到了验证。
