A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication

Abstract Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such...

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Main Authors:	Negar Farzaneh, Craig A. Williamson, Jonathan Gryak, Kayvan Najarian
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Computer applications to medicine. Medical informatics R858-859.7
Online Access:	https://doaj.org/article/f57748af49dd4d1aa98ce4ff36c1c89a
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spelling	oai:doaj.org-article:f57748af49dd4d1aa98ce4ff36c1c89a2021-12-02T16:51:39ZA hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication10.1038/s41746-021-00445-02398-6352https://doaj.org/article/f57748af49dd4d1aa98ce4ff36c1c89a2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00445-0https://doaj.org/toc/2398-6352Abstract Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge “checks and balances”. Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.Negar FarzanehCraig A. WilliamsonJonathan GryakKayvan NajarianNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Negar Farzaneh Craig A. Williamson Jonathan Gryak Kayvan Najarian A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
description	Abstract Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge “checks and balances”. Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.
format	article
author	Negar Farzaneh Craig A. Williamson Jonathan Gryak Kayvan Najarian
author_facet	Negar Farzaneh Craig A. Williamson Jonathan Gryak Kayvan Najarian
author_sort	Negar Farzaneh
title	A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
title_short	A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
title_full	A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
title_fullStr	A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
title_full_unstemmed	A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
title_sort	hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f57748af49dd4d1aa98ce4ff36c1c89a
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A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication

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