Automated Identification of Patients with Immune-related Adverse Events from Clinical Notes using Word embedding and Machine Learning
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.
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