Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine

Abstract Machine learning has the potential to change the practice of medicine, particularly in areas that require pattern recognition (e.g. radiology). Although automated classification is unlikely to be perfect, few modern machine learning tools have the ability to assess their own classification...

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Autores principales:	Dae Y. Kang, Pamela N. DeYoung, Justin Tantiongloc, Todd P. Coleman, Robert L. Owens
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/f525a8c418a8454896bb376251306434
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spelling	oai:doaj.org-article:f525a8c418a8454896bb3762513064342021-12-02T17:37:28ZStatistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine10.1038/s41746-021-00515-32398-6352https://doaj.org/article/f525a8c418a8454896bb3762513064342021-09-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00515-3https://doaj.org/toc/2398-6352Abstract Machine learning has the potential to change the practice of medicine, particularly in areas that require pattern recognition (e.g. radiology). Although automated classification is unlikely to be perfect, few modern machine learning tools have the ability to assess their own classification confidence to recognize uncertainty that might need human review. Using automated single-channel sleep staging as a first implementation, we demonstrated that uncertainty information (as quantified using Shannon entropy) can be utilized in a “human in the loop” methodology to promote targeted review of uncertain sleep stage classifications on an epoch-by-epoch basis. Across 20 sleep studies, this feedback methodology proved capable of improving scoring agreement with the gold standard over automated scoring alone (average improvement in Cohen’s Kappa of 0.28), in a fraction of the scoring time compared to full manual review (60% reduction). In summary, our uncertainty-based clinician-in-the-loop framework promotes the improvement of medical classification accuracy/confidence in a cost-effective and economically resourceful manner.Dae Y. KangPamela N. DeYoungJustin TantionglocTodd P. ColemanRobert L. OwensNature 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 Dae Y. Kang Pamela N. DeYoung Justin Tantiongloc Todd P. Coleman Robert L. Owens Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine
description	Abstract Machine learning has the potential to change the practice of medicine, particularly in areas that require pattern recognition (e.g. radiology). Although automated classification is unlikely to be perfect, few modern machine learning tools have the ability to assess their own classification confidence to recognize uncertainty that might need human review. Using automated single-channel sleep staging as a first implementation, we demonstrated that uncertainty information (as quantified using Shannon entropy) can be utilized in a “human in the loop” methodology to promote targeted review of uncertain sleep stage classifications on an epoch-by-epoch basis. Across 20 sleep studies, this feedback methodology proved capable of improving scoring agreement with the gold standard over automated scoring alone (average improvement in Cohen’s Kappa of 0.28), in a fraction of the scoring time compared to full manual review (60% reduction). In summary, our uncertainty-based clinician-in-the-loop framework promotes the improvement of medical classification accuracy/confidence in a cost-effective and economically resourceful manner.
format	article
author	Dae Y. Kang Pamela N. DeYoung Justin Tantiongloc Todd P. Coleman Robert L. Owens
author_facet	Dae Y. Kang Pamela N. DeYoung Justin Tantiongloc Todd P. Coleman Robert L. Owens
author_sort	Dae Y. Kang
title	Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine
title_short	Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine
title_full	Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine
title_fullStr	Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine
title_full_unstemmed	Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine
title_sort	statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f525a8c418a8454896bb376251306434
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Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine

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