Data_Sheet_1_Identification and Impact Analysis of Family History of Psychiatric Disorder in Mood Disorder Patients With Pretrained Language Model.pdf

Mood disorders are ubiquitous mental disorders with familial aggregation. Extracting family history of psychiatric disorders from large electronic hospitalization records is helpful for further study of onset characteristics among patients with a mood disorder. This study uses an observational clinical data set of in-patients of Nanjing Brain Hospital, affiliated with Nanjing Medical University, from the past 10 years. This paper proposes a pretrained language model: Bidirectional Encoder Representations from Transformers (BERT)–Convolutional Neural Network (CNN). We first project the electronic hospitalization records into a low-dimensional dense matrix via the pretrained Chinese BERT model, then feed the dense matrix into the stacked CNN layer to capture high-level features of texts; finally, we use the fully connected layer to extract family history based on high-level features. The accuracy of our BERT–CNN model was 97.12 ± 0.37% in the real-world data set from Nanjing Brain Hospital. We further studied the correlation between mood disorders and family history of psychiatric disorder.

心境障碍（Mood disorders）是一类具有家族聚集性的常见精神障碍。从大规模电子住院病历中提取精神疾病家族史，有助于进一步研究心境障碍患者的发病特征。本研究采用南京医科大学附属南京脑科医院近10年的住院患者观察性临床数据集。本文提出一款预训练语言模型（pretrained language model）：基于Transformer的双向编码器表征（Bidirectional Encoder Representations from Transformers，BERT）-卷积神经网络（Convolutional Neural Network，CNN）模型。研究具体流程为：首先通过预训练中文BERT模型将电子住院病历映射为低维稠密矩阵，随后将该稠密矩阵输入堆叠卷积层以捕获文本的高阶特征，最终借助全连接层（fully connected layer）基于高阶特征提取家族病史信息。在南京脑科医院的真实世界数据集（real-world data set）中，本模型的准确率达97.12±0.37%。此外，本研究还进一步分析了心境障碍与精神疾病家族史之间的相关性。