Automated Identification of Patients with Immune-related Adverse Events from Clinical Notes using Word embedding and Machine Learning
Automated Identification of Patients with Immune-related Adverse Events from Clinical Notes using Word embedding and Machine Learning
复制标题
使用词嵌入和机器学习从临床记录中自动识别患有免疫相关不良事件的患者
DOI：
10.1101/2020.05.19.20106583
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发表时间：
2020
影响因子：
4.2
通讯作者：
Subha Madhavan
中科院分区：
文献类型：
--
作者：
Samir Gupta;A. Belouali;Neil J. Shah;M. Atkins;Subha Madhavan
免疫检查点抑制剂（ICIs）显着提高了晚期恶性肿瘤患者的生存率。然而，ICIs 与一系列独特的副作用相关，称为免疫相关不良事件 (irAE)。为了确保治疗安全，需要开展研究工作，从现实世界数据 (RWD) 中全面检测和了解 irAE。这项工作的目标是评估基于机器学习的表型分析方法，该方法可以从代表 RWD 的大量回顾性临床记录中识别 irAE 患者。评估显示出有希望的结果，平均 F1 分数 = 0.75，AUC-ROC = 0.78。虽然图表中任何可用 irAE 的提取都具有很高的准确性，但单个 irAE 提取还有进一步改进的空间。
Immune Checkpoint Inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies. However, ICIs are associated with a unique spectrum of side effects termed Immune-Related Adverse Events (irAEs). To ensure treatment safety, research efforts are needed to comprehensively detect and understand irAEs from real world data (RWD). The goal of this work is to evaluate a Machine Learning-based phenotyping approach that can identify patients with irAEs from a large volume of retrospective clinical notes representing RWD. Evaluation shows promising results with an average F1-score=0.75 and AUC-ROC=0.78. While the extraction of any available irAEs in charts achieves high accuracy, individual irAEs extraction has room for further improvement.
DOI：
10.1136/jamia.2009.001560
发表时间：
2010-09-01
影响因子：
6.4
作者：
Savova, Guergana K.;Masanz, James J.;Chute, Christopher G.
通讯作者：
Chute, Christopher G.