Le Thanh-Dung, Noumeir Rita, Rambaud Jerome, Sans Guillaume, Jouvet Philippe
Biomedical Information Processing Lab, École de Technologie SupérieureUniversity of Québec Montreal QB H3G 1M8 Canada.
Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada.
IEEE Open J Eng Med Biol. 2022 Sep 26;3:142-149. doi: 10.1109/OJEMB.2022.3209900. eCollection 2022.
The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are ideal clinical research environments for such development because they collect many clinical data and are highly computerized. We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36% of total) and 3503 negative cases classified by two independent physicians using a standardized approach. The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89% accuracy, 88% recall, and 89% precision. This study successfully applied learning representation and machine learning algorithms to detect heart failure in a single French institution from clinical natural language. Further work is needed to use the same methodology in other languages and institutions.
临床数据管理系统和人工智能方法的快速发展开启了个性化医疗时代。重症监护病房（ICU）是此类发展的理想临床研究环境，因为它们收集了大量临床数据且高度计算机化。我们利用临床自然语言，针对一个前瞻性ICU数据库开展了一项回顾性临床研究，以助力危重症儿童心力衰竭的早期诊断。该方法包括对一种学习算法进行实证实验，以了解法语临床记录数据的潜在解读和呈现方式。本研究纳入了1386例患者的临床记录，共计5444条单行记录。由两名独立医生采用标准化方法分类，其中有1941例阳性病例（占总数的36%）和3503例阴性病例。多层感知器神经网络优于其他判别式和生成式分类器。因此，所提出的框架实现了总体分类性能，准确率为89%，召回率为88%，精确率为89%。本研究成功应用学习表征和机器学习算法，从临床自然语言中检测出一家法国机构内的心力衰竭情况。还需要进一步开展工作，以便在其他语言和机构中使用相同方法。
