Detection of overdose and underdose prescriptions—An unsupervised machine learning approach

Overdose prescription errors sometimes cause serious life-threatening adverse drug events, while underdose errors lead to diminished therapeutic effects. Therefore, it is important to detect and prevent these errors. In the present study, we used the one-class support vector machine (OCSVM), one of...

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Autores principales:	Kenichiro Nagata, Toshikazu Tsuji, Kimitaka Suetsugu, Kayoko Muraoka, Hiroyuki Watanabe, Akiko Kanaya, Nobuaki Egashira, Ichiro Ieiri
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/034f183e0e32452bbf69d91f2d2d3d21
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spelling	oai:doaj.org-article:034f183e0e32452bbf69d91f2d2d3d212021-11-25T06:19:31ZDetection of overdose and underdose prescriptions—An unsupervised machine learning approach1932-6203https://doaj.org/article/034f183e0e32452bbf69d91f2d2d3d212021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604308/?tool=EBIhttps://doaj.org/toc/1932-6203Overdose prescription errors sometimes cause serious life-threatening adverse drug events, while underdose errors lead to diminished therapeutic effects. Therefore, it is important to detect and prevent these errors. In the present study, we used the one-class support vector machine (OCSVM), one of the most common unsupervised machine learning algorithms for anomaly detection, to identify overdose and underdose prescriptions. We extracted prescription data from electronic health records in Kyushu University Hospital between January 1, 2014 and December 31, 2019. We constructed an OCSVM model for each of the 21 candidate drugs using three features: age, weight, and dose. Clinical overdose and underdose prescriptions, which were identified and rectified by pharmacists before administration, were collected. Synthetic overdose and underdose prescriptions were created using the maximum and minimum doses, defined by drug labels or the UpToDate database. We applied these prescription data to the OCSVM model and evaluated its detection performance. We also performed comparative analysis with other unsupervised outlier detection algorithms (local outlier factor, isolation forest, and robust covariance). Twenty-seven out of 31 clinical overdose and underdose prescriptions (87.1%) were detected as abnormal by the model. The constructed OCSVM models showed high performance for detecting synthetic overdose prescriptions (precision 0.986, recall 0.964, and F-measure 0.973) and synthetic underdose prescriptions (precision 0.980, recall 0.794, and F-measure 0.839). In comparative analysis, OCSVM showed the best performance. Our models detected the majority of clinical overdose and underdose prescriptions and demonstrated high performance in synthetic data analysis. OCSVM models, constructed using features such as age, weight, and dose, are useful for detecting overdose and underdose prescriptions.Kenichiro NagataToshikazu TsujiKimitaka SuetsuguKayoko MuraokaHiroyuki WatanabeAkiko KanayaNobuaki EgashiraIchiro IeiriPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kenichiro Nagata Toshikazu Tsuji Kimitaka Suetsugu Kayoko Muraoka Hiroyuki Watanabe Akiko Kanaya Nobuaki Egashira Ichiro Ieiri Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
description	Overdose prescription errors sometimes cause serious life-threatening adverse drug events, while underdose errors lead to diminished therapeutic effects. Therefore, it is important to detect and prevent these errors. In the present study, we used the one-class support vector machine (OCSVM), one of the most common unsupervised machine learning algorithms for anomaly detection, to identify overdose and underdose prescriptions. We extracted prescription data from electronic health records in Kyushu University Hospital between January 1, 2014 and December 31, 2019. We constructed an OCSVM model for each of the 21 candidate drugs using three features: age, weight, and dose. Clinical overdose and underdose prescriptions, which were identified and rectified by pharmacists before administration, were collected. Synthetic overdose and underdose prescriptions were created using the maximum and minimum doses, defined by drug labels or the UpToDate database. We applied these prescription data to the OCSVM model and evaluated its detection performance. We also performed comparative analysis with other unsupervised outlier detection algorithms (local outlier factor, isolation forest, and robust covariance). Twenty-seven out of 31 clinical overdose and underdose prescriptions (87.1%) were detected as abnormal by the model. The constructed OCSVM models showed high performance for detecting synthetic overdose prescriptions (precision 0.986, recall 0.964, and F-measure 0.973) and synthetic underdose prescriptions (precision 0.980, recall 0.794, and F-measure 0.839). In comparative analysis, OCSVM showed the best performance. Our models detected the majority of clinical overdose and underdose prescriptions and demonstrated high performance in synthetic data analysis. OCSVM models, constructed using features such as age, weight, and dose, are useful for detecting overdose and underdose prescriptions.
format	article
author	Kenichiro Nagata Toshikazu Tsuji Kimitaka Suetsugu Kayoko Muraoka Hiroyuki Watanabe Akiko Kanaya Nobuaki Egashira Ichiro Ieiri
author_facet	Kenichiro Nagata Toshikazu Tsuji Kimitaka Suetsugu Kayoko Muraoka Hiroyuki Watanabe Akiko Kanaya Nobuaki Egashira Ichiro Ieiri
author_sort	Kenichiro Nagata
title	Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
title_short	Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
title_full	Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
title_fullStr	Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
title_full_unstemmed	Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
title_sort	detection of overdose and underdose prescriptions—an unsupervised machine learning approach
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/034f183e0e32452bbf69d91f2d2d3d21
work_keys_str_mv	AT kenichironagata detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach AT toshikazutsuji detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach AT kimitakasuetsugu detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach AT kayokomuraoka detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach AT hiroyukiwatanabe detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach AT akikokanaya detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach AT nobuakiegashira detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach AT ichiroieiri detectionofoverdoseandunderdoseprescriptionsanunsupervisedmachinelearningapproach
_version_	1718413843210698752

Detection of overdose and underdose prescriptions—An unsupervised machine learning approach

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