Benjamin Skov Kaas-Hansen
Undervisningsassistent
Biostatistisk afdeling
Øster Farimagsgade 5 opg. B
1353 København K
ORCID: 0000-0003-1023-0371
Flest downloads
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226 downloadsUdgivet
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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79 downloadsUdgivet
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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62 downloadsUdgivet
Different Original and Biosimilar TNF Inhibitors Similarly Reduce Joint Destruction in Rheumatoid Arthritis-A Network Meta-Analysis of 36 Randomized Controlled Trials
Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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39 downloadsUdgivet
Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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27 downloadsUdgivet
Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
ID: 185059892
Flest downloads
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226
downloads
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Udgivet -
79
downloads
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Udgivet -
62
downloads
Different Original and Biosimilar TNF Inhibitors Similarly Reduce Joint Destruction in Rheumatoid Arthritis-A Network Meta-Analysis of 36 Randomized Controlled Trials
Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
Udgivet