Detection of overdose and underdose prescriptions-An unsupervised machine learning approach was written by Nagata, Kenichiro;Tsuji, Toshikazu;Suetsugu, Kimitaka;Muraoka, Kayoko;Watanabe, Hiroyuki;Kanaya, Akiko;Egashira, Nobuaki;Ieiri, Ichiro. And the article was included in PLoS One in 2021.Formula: C18H20N3NaO3S The following contents are mentioned in the article:
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 Jan. 1, 2014 and Dec. 31, 2019. We constructed an OCSVM model for each of the 21 candidate drugs using three features: age, weight, and dose. Clin. 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 min. 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 anal. with other unsupervised outlier detection algorithms (local outlier factor, isolation forest, and robust covariance). Twenty-seven out of 31 clin. 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 anal., OCSVM showed the best performance. Our models detected the majority of clin. overdose and underdose prescriptions and demonstrated high performance in synthetic data anal. OCSVM models, constructed using features such as age, weight, and dose, are useful for detecting overdose and underdose prescriptions. This study involved multiple reactions and reactants, such as Sodium 2-(((4-(3-methoxypropoxy)-3-methylpyridin-2-yl)methyl)sulfinyl)benzo[d]imidazol-1-ide (cas: 117976-90-6Formula: C18H20N3NaO3S).
Sodium 2-(((4-(3-methoxypropoxy)-3-methylpyridin-2-yl)methyl)sulfinyl)benzo[d]imidazol-1-ide (cas: 117976-90-6) belongs to imidazole derivatives. Imidazole derivatives generally have good solubility in protic solvents. Simple imidazole derivatives, such as 1H-imidazole, 2-methyl-1H-imidazole, and 1,2-dimethylimidazole, have very high solubility in water. Imidazole based anticancer drug find applications in cancer chemotherapy. It is used as buffer component for purification of the histidine tagged recombinant proteins in immobilized metal-affinity chromatography (IMAC).Formula: C18H20N3NaO3S
Referemce:
Imidazole – Wikipedia,
Imidazole | C3H4N2 – PubChem