Advanced search
2 files | 4.64 MB Add to list

Predicting future state for adaptive clinical pathway management

Author
Organization
Abstract
Clinical decision support systems are assisting physicians in providing care to patients. However, in the context of clinical pathway management such systems are rather limited as they only take the current state of the patient into account and ignore the possible evolvement of that state in the future. In the past decade, the availability of big data in the healthcare domain did open a new era for clinical decision support. Machine learning technologies are now widely used in the clinical domain, nevertheless, mostly as a tool for disease prediction. A tool that not only predicts future states, but also enables adaptive clinical pathway management based on these predictions is still in need. This paper introduces weighted state transition logic, a logic to model state changes based on actions planned in clinical pathways. Weighted state transition logic extends linear logic by taking weights - numerical values indicating the quality of an action or an entire clinical pathway - into account. It allows us to predict the future states of a patient and it enables adaptive clinical pathway management based on these predictions. We provide an implementation of weighted state transition logic using semantic web technologies, which makes it easy to integrate semantic data and rules as background knowledge. Executed by a semantic reasoner, it is possible to generate a clinical pathway towards a target state, as well as to detect potential conflicts in the future when multiple pathways are coexisting. The transitions from the current state to the predicted future state are traceable, which builds trust from human users on the generated pathway.
Keywords
Adaptive clinical pathway management, Clinical decision support, Personalized care, Weighted state transition logic

Downloads

  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.49 MB
  • DS417 acc.pdf
    • full text (Accepted manuscript)
    • |
    • open access
    • |
    • PDF
    • |
    • 3.14 MB

Citation

Please use this url to cite or link to this publication:

MLA
Sun, Hong, et al. “Predicting Future State for Adaptive Clinical Pathway Management.” JOURNAL OF BIOMEDICAL INFORMATICS, vol. 117, 2021, doi:10.1016/j.jbi.2021.103750.
APA
Sun, H., Arndt, D., De Roo, J., & Mannens, E. (2021). Predicting future state for adaptive clinical pathway management. JOURNAL OF BIOMEDICAL INFORMATICS, 117. https://doi.org/10.1016/j.jbi.2021.103750
Chicago author-date
Sun, Hong, Dörthe Arndt, Jos De Roo, and Erik Mannens. 2021. “Predicting Future State for Adaptive Clinical Pathway Management.” JOURNAL OF BIOMEDICAL INFORMATICS 117. https://doi.org/10.1016/j.jbi.2021.103750.
Chicago author-date (all authors)
Sun, Hong, Dörthe Arndt, Jos De Roo, and Erik Mannens. 2021. “Predicting Future State for Adaptive Clinical Pathway Management.” JOURNAL OF BIOMEDICAL INFORMATICS 117. doi:10.1016/j.jbi.2021.103750.
Vancouver
1.
Sun H, Arndt D, De Roo J, Mannens E. Predicting future state for adaptive clinical pathway management. JOURNAL OF BIOMEDICAL INFORMATICS. 2021;117.
IEEE
[1]
H. Sun, D. Arndt, J. De Roo, and E. Mannens, “Predicting future state for adaptive clinical pathway management,” JOURNAL OF BIOMEDICAL INFORMATICS, vol. 117, 2021.
@article{8715297,
  abstract     = {{Clinical decision support systems are assisting physicians in providing care to patients. However, in the context of clinical pathway management such systems are rather limited as they only take the current state of the patient into account and ignore the possible evolvement of that state in the future. In the past decade, the availability of big data in the healthcare domain did open a new era for clinical decision support. Machine learning technologies are now widely used in the clinical domain, nevertheless, mostly as a tool for disease prediction. A tool that not only predicts future states, but also enables adaptive clinical pathway management based on these predictions is still in need. This paper introduces weighted state transition logic, a logic to model state changes based on actions planned in clinical pathways. Weighted state transition logic extends linear logic by taking weights - numerical values indicating the quality of an action or an entire clinical pathway - into account. It allows us to predict the future states of a patient and it enables adaptive clinical pathway management based on these predictions. We provide an implementation of weighted state transition logic using semantic web technologies, which makes it easy to integrate semantic data and rules as background knowledge. Executed by a semantic reasoner, it is possible to generate a clinical pathway towards a target state, as well as to detect potential conflicts in the future when multiple pathways are coexisting. The transitions from the current state to the predicted future state are traceable, which builds trust from human users on the generated pathway.}},
  articleno    = {{103750}},
  author       = {{Sun, Hong and Arndt, Dörthe and De Roo, Jos and Mannens, Erik}},
  issn         = {{1532-0464}},
  journal      = {{JOURNAL OF BIOMEDICAL INFORMATICS}},
  keywords     = {{Adaptive clinical pathway management,Clinical decision support,Personalized care,Weighted state transition logic}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{Predicting future state for adaptive clinical pathway management}},
  url          = {{http://doi.org/10.1016/j.jbi.2021.103750}},
  volume       = {{117}},
  year         = {{2021}},
}

Altmetric
View in Altmetric
Web of Science
Times cited: