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Early Prediction of Organ Failures in Patients with Acute Pancreatitis Using Text Mining
Scientific Programming ( IF 1.672 ) Pub Date : 2021-05-12 , DOI: 10.1155/2021/6683942
Jiawei Luo 1 , Lan Lan 1 , Dujiang Yang 2 , Shixin Huang 3 , Mengjiao Li 1 , Jin Yin 1 , Juan Xiao 4 , Xiaobo Zhou 5
Affiliation  

It is of great significance to establish an assessment model for organ failures in the early stage of admission in acute pancreatitis (AP). And the clinical notes are underutilized. To predict organ failures for AP patients using early clinical notes in hospital, early text features obtained from the pretrained Chinese Bidirectional Encoder Representations from Transformers model and attention-based LSTM were combined with early structured features (laboratory tests, vital signs, and demographic characteristics) to predict organ failures (respiratory, cardiovascular, and renal) in 12,748 AP inpatients in West China Hospital, Sichuan University, from 2008 to 2018. The text plus structured features fusion model was used to predict organ failures, compared to the baseline model with only structured features. The performance of the model with text features added is superior to the model that only includes structured features.

中文翻译:

文本挖掘技术对急性胰腺炎患者器官衰竭的早期预测

建立急性胰腺炎(AP)入院初期器官衰竭的评估模型具有重要意义。并且临床笔记未得到充分利用。为了使用医院的早期临床笔记来预测AP患者的器官衰竭,将从变形模型和基于注意的LSTM的预训练中文双向编码器表示中获得的早期文本特征与早期结构化特征(实验室测试,生命体征和人口统计学特征)结合起来预测2008年至2018年四川大学华西医院的12748名AP住院患者的器官衰竭(呼吸,心血管和肾功能衰竭)。采用文本加结构特征融合模型预测器官衰竭,与之相比,基线模型仅结构化功能。
更新日期:2021-05-12
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