Author:
Nishioka Satoshi,Asano Masaki,Yada Shuntaro,Aramaki Eiji,Yajima Hiroshi,Yanagisawa Yuki,Sayama Kyoko,Kizaki Hayato,Hori Satoko
Abstract
AbstractAdverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients’ activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients’ narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 blog posts, 191 and 702 articles were identified as describing AEs limiting ADL or not limiting ADL, respectively. Among tested DL modes and approaches, T5 showed the best F1 scores to identify articles with AE limiting ADL or all AE: 0.557 and 0.811, respectively. The most frequent AE signals were “pain or numbness”, “fatigue” and “nausea”. Our results suggest that this AE monitoring scheme focusing on patients’ ADL has potential to reinforce current AE management provided by medical staff.
Publisher
Springer Science and Business Media LLC
Reference53 articles.
1. Mattiuzzi, C. & Lippi, G. Current cancer epidemiology glossary. J. Epidemiol. Glob. Health 9, 217–222 (2019).
2. Lin, C., Clark, R., Tu, P., Bosworth, H. B. & Zullig, L. L. Breast cancer oral anti-cancer medication adherence: A systematic review of psychosocial motivators and barriers. Breast Cancer Res. Treat. 165, 247–260 (2017).
3. Ministry of Health, Labour, and Welfare; the manual for handling disorders due to adverse drug reactions. https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/iyakuhin/topics/tp061122-1.html.
4. Anderson, W. Guidelines for the management of chemotherapy and systemic anticancer therapy induced toxicities within primary care. Northen Cancer Alliace 0–21 (2018).
5. Liu, S. & Kurzrock, R. Understanding toxicities of targeted agents: Implications for anti-tumor activity and management. Semin. Oncol. 42, 863–875 (2015).