دورية أكاديمية
أعرض في EDS

Classifying cancer pathology reports with hierarchical self-attention networks.

التفاصيل البيبلوغرافية
العنوان: Classifying cancer pathology reports with hierarchical self-attention networks.
المؤلفون: Gao S; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA. Electronic address: gaos@ornl.gov., Qiu JX; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA., Alawad M; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA., Hinkle JD; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA., Schaefferkoetter N; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA., Yoon HJ; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA., Christian B; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA., Fearn PA; Surveillance Informatics Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA., Penberthy L; Surveillance Informatics Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA., Wu XC; Louisiana Tumor Registry, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA, USA., Coyle L; Information Management Services Inc, Calverton, MD, USA., Tourassi G; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA. Electronic address: tourassig@ornl.gov., Ramanathan A; Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA. Electronic address: ramanathana@ornl.gov.
المصدر: Artificial intelligence in medicine [Artif Intell Med] 2019 Nov; Vol. 101, pp. 101726. Date of Electronic Publication: 2019 Oct 15.
نوع المنشور: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Elsevier Science Publishing Country of Publication: Netherlands NLM ID: 8915031 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2860 (Electronic) Linking ISSN: 09333657 NLM ISO Abbreviation: Artif Intell Med Subsets: MEDLINE
أسماء مطبوعة: Publication: Amsterdam : Elsevier Science Publishing
Original Publication: Tecklenburg, Federal Republic of Germany : Burgverlag, c1989-
مواضيع طبية MeSH: Neoplasms/*pathology, Deep Learning ; Humans ; Natural Language Processing ; Neoplasms/classification ; Neural Networks, Computer
مستخلص: 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.
(Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)
فهرسة مساهمة: Keywords: Cancer pathology reports; Clinical reports; Deep learning; Natural language processing; Text classification
تواريخ الأحداث: Date Created: 20191210 Date Completed: 20200921 Latest Revision: 20200921
رمز التحديث: 20240628
DOI: 10.1016/j.artmed.2019.101726
PMID: 31813492
قاعدة البيانات: MEDLINE
الوصف
تدمد:1873-2860
DOI:10.1016/j.artmed.2019.101726