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Comparative analysis of explainable machine learning prediction models for hospital mortality
Stenwig, Eline
ORCID iD:
0000-0002-3803-8106
Salvi, Giampiero
ORCID iD:
0000-0002-3323-5311
Salvo Rossi, Pierluigi
Skjærvold, Nils Kristian
2022 (English)
In:
BMC Medical Research Methodology, E-ISSN 1471-2288, Vol. 22, no 1, article id 53
Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Springer Nature , 2022. Vol. 22, no 1, article id 53
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN:
urn:nbn:se:kth:diva-324765
DOI:
10.1186/s12874-022-01540-w
ISI:
000761480900005
PubMedID:
35220950
Scopus ID:
2-s2.0-85125432454
OAI: oai:DiVA.org:kth-324765
DiVA, id:
diva2:1743463
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IGLU
Note
QC 20230404
Available from:
2023-03-15
Created:
2023-03-15
Last updated:
2024-01-17
Bibliographically approved
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fulltext
(2267 kB)
142 downloads
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FULLTEXT01.pdf
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c0381aa6e4a123e9e73d2333db39d89a1fed2d25741669b1a4ea6a02a75e61d8428bfafe2ce8233072eef1e9e49c289c8f0450ab08a60bc5c25f7ddd9c743aa9
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Salvi, Giampiero
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Stenwig, Eline
Salvi, Giampiero
Salvo Rossi, Pierluigi
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BMC Medical Research Methodology
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Computer Sciences
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Total: 142 downloads
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-324765
Direct link
http://kth.diva-portal.org/smash/record.jsf?pid=diva2:1743463
Cite
Citation style
apa
ieee
modern-language-association-8th-edition
vancouver
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apa
ieee
modern-language-association-8th-edition
vancouver
Other style
More styles
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de-DE
en-GB
en-US
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nn-NO
nn-NB
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