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Computer Science > Machine Learning

arXiv:2109.06707 (cs)
COVID-19 e-print

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[Submitted on 14 Sep 2021 (v1), last revised 3 Dec 2021 (this version, v2)]

Title:A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients

Authors:Adam Izdebski, Patrick J. Thoral, Robbert C.A. Lalisang, Dean M. McHugh, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco A.A. Peters, Marlijn J.A Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G.C.A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G.M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P.C. de Jager, Stefaan H.A. Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert B. Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Harald J. Faber, A. Karakus, Robert Entjes, Paul de Jong, Thijs C.D. Rettig, Sesmu Arbous, Lucas M. Fleuren, Tariq A. Dam, Michele Tonutti, Daan P. de Bruin, Paul W.G. Elbers, Giovanni Cinà
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Abstract:Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article proposes a pragmatic methodology to obtain preliminary but robust estimation of treatment effect from observational studies, to provide front-line clinicians with a degree of confidence in their treatment strategy. Our study design is applied to an open problem, the estimation of treatment effect of the proning maneuver on COVID-19 Intensive Care patients.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2109.06707 [cs.LG]
  (or arXiv:2109.06707v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.06707

Submission history

From: Giovanni Cinà [view email]
[v1] Tue, 14 Sep 2021 14:14:37 UTC (348 KB)
[v2] Fri, 3 Dec 2021 11:07:30 UTC (374 KB)
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