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Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model
- Publication Year :
- 2020
-
Abstract
- The objective of this work was to assess the clinical performance of an unsupervised machine learning model aimed at identifying unusual medication orders and pharmacological profiles. We conducted a prospective study between April 2020 and August 2020 where 25 clinical pharmacists dichotomously (typical or atypical) rated 12,471 medication orders and 1,356 pharmacological profiles. Based on AUPR, performance was poor for orders, but satisfactory for profiles. Pharmacists considered the model a useful screening tool.<br />Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract
- Subjects :
- Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2011.01925
- Document Type :
- Working Paper