Performance evaluation of a prescription medication image classification model: an observational cohort

Abstract Technology assistance of pharmacist verification tasks through the use of machine intelligence has the potential to detect dangerous and costly pharmacy dispensing errors. National Drug Codes (NDC) are unique numeric identifiers of prescription drug products for the United States Food and D...

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Autores principales:	Corey A. Lester, Jiazhao Li, Yuting Ding, Brigid Rowell, Jessie ‘Xi’ Yang, Raed Al Kontar
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/b13be2ddb1b343bc98c265391a61b6bd
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spelling	oai:doaj.org-article:b13be2ddb1b343bc98c265391a61b6bd2021-12-02T16:06:33ZPerformance evaluation of a prescription medication image classification model: an observational cohort10.1038/s41746-021-00483-82398-6352https://doaj.org/article/b13be2ddb1b343bc98c265391a61b6bd2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00483-8https://doaj.org/toc/2398-6352Abstract Technology assistance of pharmacist verification tasks through the use of machine intelligence has the potential to detect dangerous and costly pharmacy dispensing errors. National Drug Codes (NDC) are unique numeric identifiers of prescription drug products for the United States Food and Drug Administration. The physical form of the medication, often tablets and capsules, captures the unique features of the NDC product to help ensure patients receive the same medication product inside their prescription bottle as is found on the label from a pharmacy. We report and evaluate using an automated check to predict the shape, color, and NDC for images showing a pile of pills inside a prescription bottle. In a test set containing 65,274 images of 345 NDC classes, overall macro-average precision was 98.5%. Patterns of incorrect NDC predictions based on similar colors, shapes, and imprints of pills were identified and recommendations to improve the model are provided.Corey A. LesterJiazhao LiYuting DingBrigid RowellJessie ‘Xi’ YangRaed Al KontarNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-8 (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 Corey A. Lester Jiazhao Li Yuting Ding Brigid Rowell Jessie ‘Xi’ Yang Raed Al Kontar Performance evaluation of a prescription medication image classification model: an observational cohort
description	Abstract Technology assistance of pharmacist verification tasks through the use of machine intelligence has the potential to detect dangerous and costly pharmacy dispensing errors. National Drug Codes (NDC) are unique numeric identifiers of prescription drug products for the United States Food and Drug Administration. The physical form of the medication, often tablets and capsules, captures the unique features of the NDC product to help ensure patients receive the same medication product inside their prescription bottle as is found on the label from a pharmacy. We report and evaluate using an automated check to predict the shape, color, and NDC for images showing a pile of pills inside a prescription bottle. In a test set containing 65,274 images of 345 NDC classes, overall macro-average precision was 98.5%. Patterns of incorrect NDC predictions based on similar colors, shapes, and imprints of pills were identified and recommendations to improve the model are provided.
format	article
author	Corey A. Lester Jiazhao Li Yuting Ding Brigid Rowell Jessie ‘Xi’ Yang Raed Al Kontar
author_facet	Corey A. Lester Jiazhao Li Yuting Ding Brigid Rowell Jessie ‘Xi’ Yang Raed Al Kontar
author_sort	Corey A. Lester
title	Performance evaluation of a prescription medication image classification model: an observational cohort
title_short	Performance evaluation of a prescription medication image classification model: an observational cohort
title_full	Performance evaluation of a prescription medication image classification model: an observational cohort
title_fullStr	Performance evaluation of a prescription medication image classification model: an observational cohort
title_full_unstemmed	Performance evaluation of a prescription medication image classification model: an observational cohort
title_sort	performance evaluation of a prescription medication image classification model: an observational cohort
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b13be2ddb1b343bc98c265391a61b6bd
work_keys_str_mv	AT coreyalester performanceevaluationofaprescriptionmedicationimageclassificationmodelanobservationalcohort AT jiazhaoli performanceevaluationofaprescriptionmedicationimageclassificationmodelanobservationalcohort AT yutingding performanceevaluationofaprescriptionmedicationimageclassificationmodelanobservationalcohort AT brigidrowell performanceevaluationofaprescriptionmedicationimageclassificationmodelanobservationalcohort AT jessiexiyang performanceevaluationofaprescriptionmedicationimageclassificationmodelanobservationalcohort AT raedalkontar performanceevaluationofaprescriptionmedicationimageclassificationmodelanobservationalcohort
_version_	1718384983676026880

Performance evaluation of a prescription medication image classification model: an observational cohort

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