- By:
- Gao, Shang ; Qiu, John X; Alawad, Mohammed M; Hinkle, Jacob D; Schaefferkoetter, Noah T; Yoon, Hong Jun ; Christian, James B; Fearn, Paul; Boten, Jessica; Penberthy, Lynne; Wu, Xiao-Cheng; Tourassi, Georgia ; Ramanathan, Arvind
- Journal Name:
- Artificial Intelligence in Medicine
- Page Number:
- 101726
- Volume:
- 101
- Issue Number:
- n/a
- Publication Date:
- November 9, 2023
- View DOI Listing:
- https://doi.org/10.1016/j.artmed.2019.101726
Abstract
We introduce a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and show how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability. We evaluate performance on a corpus of 374,899 pathology reports obtained from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program. Each pathology report is associated with five clinical classification tasks – site, laterality, behavior, histology, and grade. We compare the performance of the HiSAN against other machine learning and deep learning approaches commonly used on medical text data – Naive Bayes, logistic regression, convolutional neural networks, and hierarchical attention networks (the previous state-of-the-art). We show that HiSANs are superior to other machine learning and deep learning text classifiers in both accuracy and macro F-score across all five classification tasks. Compared to the previous state-of-the-art, hierarchical attention networks, HiSANs not only are an order of magnitude faster to train, but also achieve about 1% better relative accuracy and 5% better relative macro F-score.
