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

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

doi:10.1016/S2589-7500(20)30018-2

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

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

Research output: Contribution to journal › Journal article › Research › peer-review

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Original language	English
Journal	The Lancet Digital Health
Volume	2
Issue number	4
Pages (from-to)	e179–91
Number of pages	13
DOIs	https://doi.org/10.1016/S2589-7500(20)30018-2
Publication status	Published - 2020

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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 recordsFinal published version, 4.2 MBLicence: CC BY

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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

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.

In: The Lancet Digital Health, Vol. 2, No. 4, 2020, p. e179–91.

Research output: Contribution to journal › Journal article › Research › peer-review

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, vol. 2, no. 4, pp. e179–91. https://doi.org/10.1016/S2589-7500(20)30018-2

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. In: The Lancet Digital Health. 2020 ; Vol. 2, No. 4. pp. e179–91.

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