Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction
- Abstract
- We developed an attention model to predict future adverse glycemic events 30 min in advance based on the observation of past glycemic values over a 35 min period. The proposed model effectively encodes insulin administration and meal intake time using Time2Vec (T2V) for glucose prediction. The proposed impartial feature selection algorithm is designed to distribute rewards proportionally according to agent contributions. Agent contributions are calculated by a step-by-step negation of updated agents. Thus, the proposed feature selection algorithm optimizes features from electronic medical records to improve performance. For evaluation, we collected continuous glucose monitoring data from 102 patients with type 2 diabetes admitted to Cheonan Hospital, Soonchunhyang University. Using our proposed model, we achieved F1-scores of 89.0%, 60.6%, and 89.8% for normoglycemia, hypoglycemia, and hyperglycemia, respectively.
- All Author(s)
- Seo-Hee Kim
; Dae-Yeon Kim
; Sung-Wan Chun
; Jaeyun Kim
; Jiyoung Woo
- Issued Date
- 2024
- Type
- Article
- Keyword
- Attention mechanism; Deep learning; Feature selection; Multi-agent learning; Reinforcement learning
- Publisher
- Elsevier
- ISSN
- 0010-4825
; 1879-0534
- Citation Title
- Computers in biology and medicine
- Citation Volume
- 173
- Citation Start Page
- 108257
- Citation End Page
- 108257
- Language(ISO)
- eng
- DOI
- 10.1016/j.compbiomed.2024.108257
- URI
- http://schca-ir.schmc.ac.kr/handle/2022.oak/3447
- 공개 및 라이선스
-
- 파일 목록
-
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.