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P. M. Florvaag, H. Kjeldsberg and S. K. Mitusch

Smittestopp for Android and iOS

In Smittestopp − A Case Study on Digital Contact Tracing, 11-27. Vol. 11. Cham: Springer International Publishing, 2022.
Book Chapter
2022
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M. Maleckar, L. Myklebust, J. J. Uv, P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrøm, K. Ahtarovski et al.

Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Frontiers in physiology 12 (2021): 745349.
Journal Article
2021
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A. Elmokashfi, J. Sundnes, A. Kvalbein, V. Naumova, S. Reinemo, P. M. Florvaag, H. K. Stensland and O. Lysne

Nationwide rollout reveals efficacy of epidemic control through digital contact tracing

Nature Communications 12 (2021).
Journal Article
2021
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P. M. Florvaag, V. Naumova and K. Valen-Sendstad

Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful

In Computational and Mathematical Biomedical Engineering. Sendai, Japan: CMBE, 2019.
Proceedings, refereed
2019
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2022

Book chapters

P. M. Florvaag, H. Kjeldsberg and S. K. Mitusch

Smittestopp for Android and iOS

In Smittestopp − A Case Study on Digital Contact Tracing, 11-27. Vol. 11. Cham: Springer International Publishing, 2022.
Status: Published
2021

Journal articles

A. Elmokashfi, J. Sundnes, A. Kvalbein, V. Naumova, S. Reinemo, P. M. Florvaag, H. K. Stensland and O. Lysne

Nationwide rollout reveals efficacy of epidemic control through digital contact tracing

Nature Communications 12 (2021).
Status: Published
M. Maleckar, L. Myklebust, J. J. Uv, P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrøm, K. Ahtarovski et al.

Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Frontiers in physiology 12 (2021): 745349.
Status: Published

Posters

M. Maleckar, P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrom, K. A. Ahtarovski, T. Jespersen, J. Tfelt-Hansen et al.

B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients

In Heart Rhythm. Vol. 18. Boston, MA: Elsevier, 2021.
Status: Published
2019

Proceedings, refereed

P. M. Florvaag, V. Naumova and K. Valen-Sendstad

Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful

In Computational and Mathematical Biomedical Engineering. Sendai, Japan: CMBE, 2019.
Status: Published

Talks, contributed

P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrom, K. A. Ahtarovski, T. Jespersen, J. Tfelt-Hansen, V. Naumova et al.

A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients

In Computing in Cardiology, Singapore, 2019.
Status: Published