View in EDS

Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.

Saved in:
Bibliographic Details
Title: Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.
Authors: Hsu SY; Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan., Jhu JY; Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan., Gao JW; Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan., Huang CH; Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan., Tsai CL; Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan. chulintsai@ntuh.gov.tw., Fu LC; Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan. lichen@ntu.edu.tw.
Source: BioData mining [BioData Min] 2025 Dec 20; Vol. 18 (1), pp. 88. Date of Electronic Publication: 2025 Dec 20.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101319161 Publication Model: Electronic Cited Medium: Print ISSN: 1756-0381 (Print) Linking ISSN: 17560381 NLM ISO Abbreviation: BioData Min Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1756-0381
DOI:10.1186/s13040-025-00509-x