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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Nature Portfolio
2021
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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) |
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Computer applications to medicine. Medical informatics R858-859.7 |
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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 |
work_keys_str_mv |
AT daeykang statisticaluncertaintyquantificationtoaugmentclinicaldecisionsupportafirstimplementationinsleepmedicine AT pamelandeyoung statisticaluncertaintyquantificationtoaugmentclinicaldecisionsupportafirstimplementationinsleepmedicine AT justintantiongloc statisticaluncertaintyquantificationtoaugmentclinicaldecisionsupportafirstimplementationinsleepmedicine AT toddpcoleman statisticaluncertaintyquantificationtoaugmentclinicaldecisionsupportafirstimplementationinsleepmedicine AT robertlowens statisticaluncertaintyquantificationtoaugmentclinicaldecisionsupportafirstimplementationinsleepmedicine |
_version_ |
1718379918009565184 |