Dramatyping: A generic algorithm for detecting reasonable temporal correlations between drug administration and lab value alterations
- Published
- Accepted
- Subject Areas
- Drugs and Devices, Pharmacology, Computational Science
- Keywords
- Algorithm, Temporal Correlation, Observable Drug Event, Dramatyping, Adverse Drug Reaction
- Copyright
- © 2016 Newe
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Dramatyping: A generic algorithm for detecting reasonable temporal correlations between drug administration and lab value alterations. PeerJ Preprints 4:e1846v1 https://doi.org/10.7287/peerj.preprints.1846v1
Abstract
According to the World Health Organization, one of the criteria for the standardized assessment of case causality in adverse drug reactions is the temporal relationship between the intake of a drug and the occurrence of a reaction or a laboratory test abnormality. This article presents and describes an algorithm for the detection of a reasonable temporal correlation between the administration of a drug and the alteration of a laboratory value course. The algorithm is designed to process normalized lab values and is therefore universally applicable. It has a sensitivity of 0.932 for the detection of lab value courses that show changes in temporal correlation with the administration of a drug and it has a specificity of 0.967 for the detection of lab value courses that show no changes. Therefore the algorithm is appropriate to screen the data of electronic health records and to support human experts in revealing adverse drug reactions. A reference implementation in Python programming language is available.
Author Comment
This manuscript has been accepted for publication in PeerJ.
Supplemental Information
Python source code of the reference implemenation
This is the Python source code of the reference implementation of the algorithm. It requires SciPy version 0.16.1 and numpy version 1.9.3. The Ground Truth dataset from (Newe et. al, 2015) is also included as an example input dataset. The output resulting from that dataset is included as well.