Interpretable machine learning model for prediction of overall survival in laryngeal cancer
Approved
Classifications
MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal
Publication channel information
Title of journal/series
Acta oto-laryngologica
ISSN (print)
0001-6489
ISSN (electronic)
1651-2251
ISSN (linking)
0001-6489
Publisher
Taylor & Francis
Publication forum ID
50329
Publication forum level
1
Publication appears in FT-list
No
SNIP-level of the publication
0.88
Country of publication
Norway
Internationality
Yes
Detailed publication information
Publication year
2024
Reporting year
2024
Journal/series issue number
Published online: 27 Jan 2024
Page numbers
1-7
DOI
10.1080/00016489.2023.2301648
Language of publication
English
Co-publication information
International co-publication
Yes
Co-publication with a company
No
Availability
Link to online publication
Link to self-archived version
Classification and additional information
MinEdu field of science classification
3111 Biomedicine, 318 Medical biotechnology
Keywords
deep learning; DeepTables; laryngeal cancer; laryngeal squamous cell carcinoma; Machine learning; overall survival; sEER; stacked ensemble; voting ensemble; XGBoost
Funding information
Funding information in the publication
The Sigrid Jusélius Foundation. State funding for the Helsinki University Hospital. Finska Läkaresällskapet.
Funders
Funder
Sigrid Jusélius Foundation
Name of funding
-
Funding decision
-
Source database ID
WoS ID
WOS:001150575300001
Scopus ID
2-s2.0-85183912178