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Demystifying the black box: the importance of interpretability of predictive models in neurocritical care.

Moss, Laura; Corsar, David; Shaw, Martin; Piper, Ian; Hawthorne, Christopher


Laura Moss

Martin Shaw

Ian Piper

Christopher Hawthorne


Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models with high levels of predictive accuracy. However, although these recent models appear clinically promising, their interpretability has often not been considered and they tend to be black box models, making it extremely difficult to understand how the model came to its conclusion. Interpretable machine learning methods have the potential to provide the means to overcome some of these issues but are largely unexplored within the neurocritical care domain. This article examines existing models used in neurocritical care from the perspective of interpretability. Further, the use of interpretable machine learning will be explored, in particular the potential benefits and drawbacks that the techniques may have when applied to neurocritical care data. Finding a solution to the lack of model explanation, transparency, and accountability is important because these issues have the potential to contribute to model trust and clinical acceptance, and, increasingly, regulation is stipulating a right to explanation for decisions made by models and algorithms. To ensure that the prospective gains from sophisticated predictive models to neurocritical care provision can be realized, it is imperative that interpretability of these models is fully considered.


MOSS, L., CORSAR, D., SHAW, M., PIPER, I. and HAWTHORNE, C. 2022. Demystifying the black box: the importance of interpretability of predictive models in neurocritical care. Neurocritical care [online], 37(Supplement 2): big data in neurocritical care, pages 185-191. Available from:

Journal Article Type Article
Acceptance Date Mar 29, 2022
Online Publication Date May 6, 2022
Publication Date Aug 31, 2022
Deposit Date May 9, 2022
Publicly Available Date May 9, 2022
Journal Neurocritical care
Print ISSN 1541-6933
Electronic ISSN 1556-0961
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 37
Issue Supplement 2
Pages 185-191
Keywords Machine learning; Algorithms; Critical care; Artificial intelligence; Clinical decision-making
Public URL


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