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

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Standard

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. / Thorsen-Meyer, Hans-Christian; Nielsen, Annelaura B.; Nielsen, Anna P.; Kaas-Hansen, Benjamin Skov; Toft, Palle; Schierbeck, Jens; Strøm, Thomas; Chmura, Piotr J.; Heimann, Marc; Dybdahl, Lars; Spangsege, Lasse; Hulsen, Patrick; Belling, Kirstine; Brunak, Søren; Perner, Anders.

I: The Lancet Digital Health, Bind 2, Nr. 4, 2020, s. e179–91.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Thorsen-Meyer, H-C, Nielsen, AB, Nielsen, AP, Kaas-Hansen, BS, Toft, P, Schierbeck, J, Strøm, T, Chmura, PJ, Heimann, M, Dybdahl, L, Spangsege, L, Hulsen, P, Belling, K, Brunak, S & Perner, A 2020, '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', The Lancet Digital Health, bind 2, nr. 4, s. e179–91. https://doi.org/10.1016/S2589-7500(20)30018-2

APA

Thorsen-Meyer, H-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Belling, K., Brunak, S., & Perner, A. (2020). 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. The Lancet Digital Health, 2(4), e179–91. https://doi.org/10.1016/S2589-7500(20)30018-2

Vancouver

Thorsen-Meyer H-C, Nielsen AB, Nielsen AP, Kaas-Hansen BS, Toft P, Schierbeck J o.a. 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. The Lancet Digital Health. 2020;2(4):e179–91. https://doi.org/10.1016/S2589-7500(20)30018-2

Author

Thorsen-Meyer, Hans-Christian ; Nielsen, Annelaura B. ; Nielsen, Anna P. ; Kaas-Hansen, Benjamin Skov ; Toft, Palle ; Schierbeck, Jens ; Strøm, Thomas ; Chmura, Piotr J. ; Heimann, Marc ; Dybdahl, Lars ; Spangsege, Lasse ; Hulsen, Patrick ; Belling, Kirstine ; Brunak, Søren ; Perner, Anders. / 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. I: The Lancet Digital Health. 2020 ; Bind 2, Nr. 4. s. e179–91.

Bibtex

@article{0d1cfe5a1cc241308de717a5733dd8d1,
title = "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",
author = "Hans-Christian Thorsen-Meyer and Nielsen, {Annelaura B.} and Nielsen, {Anna P.} and Kaas-Hansen, {Benjamin Skov} and Palle Toft and Jens Schierbeck and Thomas Str{\o}m and Chmura, {Piotr J.} and Marc Heimann and Lars Dybdahl and Lasse Spangsege and Patrick Hulsen and Kirstine Belling and S{\o}ren Brunak and Anders Perner",
year = "2020",
doi = "10.1016/S2589-7500(20)30018-2",
language = "English",
volume = "2",
pages = "e179–91",
journal = "The Lancet Digital Health",
issn = "2589-7500",
publisher = "Elsevier",
number = "4",

}

RIS

TY - JOUR

T1 - 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

AU - Thorsen-Meyer, Hans-Christian

AU - Nielsen, Annelaura B.

AU - Nielsen, Anna P.

AU - Kaas-Hansen, Benjamin Skov

AU - Toft, Palle

AU - Schierbeck, Jens

AU - Strøm, Thomas

AU - Chmura, Piotr J.

AU - Heimann, Marc

AU - Dybdahl, Lars

AU - Spangsege, Lasse

AU - Hulsen, Patrick

AU - Belling, Kirstine

AU - Brunak, Søren

AU - Perner, Anders

PY - 2020

Y1 - 2020

U2 - 10.1016/S2589-7500(20)30018-2

DO - 10.1016/S2589-7500(20)30018-2

M3 - Journal article

VL - 2

SP - e179–91

JO - The Lancet Digital Health

JF - The Lancet Digital Health

SN - 2589-7500

IS - 4

ER -

ID: 246673853