AI Article Synopsis

  • Overdose and underdose prescription errors can lead to severe health risks, highlighting the need for effective detection and prevention methods.
  • A study utilized a one-class support vector machine (OCSVM) to analyze prescription data from Kyushu University Hospital to identify these dosing errors based on patient age, weight, and dosage.
  • The OCSVM model proved highly effective, detecting 87.1% of clinical errors and outperforming other machine learning algorithms in identifying both synthetic overdose and underdose prescriptions.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604308PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260315PLOS

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