An early warning system that combines machine learning and a rule-based approach for the prediction of cancer patients’ unplanned visits
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Author (Corporation)
Publication date
2023
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023)
Special issue
DOI of the original publication
Series
CEUR Workshop Proceedings
Series number
3433
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Sun SITE Central Europe
Place of publication / Event location
Aachen
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Keywords
Subject (DDC)
Event
AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering
Exhibition start date
Exhibition end date
Conference start date
27.03.2024
Conference end date
29.03.2024
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Diamond
License
Citation
Witschel, H. F., Laurenzi, E., Jüngling, S., Kadvany, Y., & Trojan, A. (2023). An early warning system that combines machine learning and a rule-based approach for the prediction of cancer patients’ unplanned visits. In A. Martin, H.-G. Fill, A. Gerber, K. Hinkelmann, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023). Sun SITE Central Europe. https://irf.fhnw.ch/handle/11654/48276