In 2021,PLoS One included an article by Nagata, Kenichiro; Tsuji, Toshikazu; Suetsugu, Kimitaka; Muraoka, Kayoko; Watanabe, Hiroyuki; Kanaya, Akiko; Egashira, Nobuaki; Ieiri, Ichiro. Product Details of 23828-92-4. The article was titled ãDetection of overdose and underdose prescriptions-An unsupervised machine learning approachã? The information in the text is summarized as follows:
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. In the part of experimental materials, we found many familiar compounds, such as trans-4-((2-Amino-3,5-dibromobenzyl)amino)cyclohexanol hydrochloride(cas: 23828-92-4Product Details of 23828-92-4)
trans-4-((2-Amino-3,5-dibromobenzyl)amino)cyclohexanol hydrochloride(cas: 23828-92-4) is a medication indicated to alleviate chest congestion associated with conditions that include bronchitis, pneumonia, bronchospasm asthma, cough, and allergy.Product Details of 23828-92-4 Preclinically, ambroxol, the active ingredient of Mucosolvan, has been shown to increase respiratory tract secretion.
Referemce:
Alcohol – Wikipedia,
Alcohols – Chemistry LibreTexts