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
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Lenguaje:EN
Publicado: Nature Portfolio 2021
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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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