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Ben van Calster

KU Leuven - Department of Development and Regeneration

Belgium

K.U. Leuven Research & Development

Minderbroedersstraat 8A P.B. 5105

Leuven, 3000

Belgium

  • KU Leuven - Department of Development and Regeneration

  • K.U. Leuven Research & Development

SCHOLARLY PAPERS

3

DOWNLOADS

331

TOTAL CITATIONS

1

Scholarly Papers (3)

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1.

Larger Sample Sizes are Needed When Developing a Clinical Prediction Model Using Machine Learning in Oncology: Methodological Systematic Review

Number of pages: 40 Posted: 08 May 2024
Biruk Tsegaye, Kym I. E. Snell, Lucinda Archer, Shona Kirtley, Richard Riley, Matthew Sperrin, Ben van Calster, Gary Collins and Paula Dhiman
University of Oxford, University of Birmingham, University of Birmingham, University of Oxford - Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Birmingham, The University of Manchester, KU Leuven - Department of Development and Regeneration, University of Oxford and University of Oxford - Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS)
Downloads 169 (444,808)
Citation 1

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Keywords: machine learning, oncology, sample size, prediction model, systematic review, methodology

2.

Machine Learning for Prediction of Mortality and Unfavorable Outcome in Traumatic Brain Injury: A CENTER-TBI Study

Number of pages: 31 Posted: 27 Aug 2019
Benjamin Gravesteijn, Daan Nieboer, Ari Ercole, Hester F. Lingsma, David Nelson, Ben van Calster, Ewout W. Steyerberg and CENTER-TBI Collaborators
Leiden University - Department of Biomedical Data Sciences, Erasmus University Rotterdam (EUR) - Department of Public Health, University of Cambridge - Division of Anaesthesia, Erasmus University Rotterdam (EUR) - Department of Public Health, Karolinska Institutet - Department of Physiology and Pharmacology, KU Leuven - Department of Development and Regeneration, Erasmus University Rotterdam (EUR) - Department of Public Health and Independent
Downloads 120 (596,393)

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Machine learning; Prognosis; Traumatic brain injury; Prediction; Data science; Cohort study

3.

Calibmsm: An R Package for Calibration Plots of the Transition Probabilities in a Multistate Model

Number of pages: 29 Posted: 21 Jun 2024
Alexander Pate, Matthew Sperrin, Richard Riley, Ben van Calster and Glen P. Martin
The University of Manchester, The University of Manchester, University of Birmingham, KU Leuven - Department of Development and Regeneration and The University of Manchester
Downloads 42 (1,157,785)

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Clinical prediction models, calibration, model evaluation, multistate, multi-state, R