4 14

Cited 0 times in

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals

Authors
Jeong, E; Park, N; Choi, Y; Park, RW; Yoon, D
Citation
PloS one, 13(11):e0207749-e0207749, 2018
Journal Title
PloS one; Public Library of Science one; PLoS 1
ISSN
1932-6203
Abstract
BACKGROUND: The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results.
MATERIALS AND METHODS: To construct an ADR reference dataset, we extracted known drug-laboratory event pairs represented by a laboratory test from the EU-SPC and SIDER databases. All possible drug-laboratory event pairs, except known ones, are considered unknown. To detect a known drug-laboratory event pair, three existing algorithms-CERT, CLEAR, and PACE-were applied to 21-year inpatient EHR data. We also constructed ML models (based on random forest, L1 regularized logistic regression, support vector machine, and a neural network) that use the intermediate products of the CERT, CLEAR, and PACE algorithms as inputs and determine whether a drug-laboratory event pair is associated. For performance comparison, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-measure, and area under receiver operating characteristic (AUROC).
RESULTS: All measures of ML models outperformed those of existing algorithms with sensitivity of 0.593-0.793, specificity of 0.619-0.796, NPV of 0.645-0.727, PPV of 0.680-0.777, F1-measure of 0.629-0.709, and AUROC of 0.737-0.816. Features related to change or distribution of shape were considered important for detecting ADR signals.
CONCLUSIONS: Improved performance of ML models indicated that applying our model to EHR data is feasible and promising for detecting more accurate and comprehensive ADR signals.
MeSH terms
Drug-Related Side Effects and Adverse Reactions/diagnosis*Laboratories*Machine Learning*Software
DOI
10.1371/journal.pone.0207749
PMID
30462745
Appears in Collections:
Journal Papers > Research Organization > BK21
Journal Papers > School of Medicine / Graduate School of Medicine > Medical Informatics
AJOU Authors
최, 영박, 래웅윤, 덕용
Full Text Link
Files in This Item:
Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.pdfDownload
Export
RIS (EndNote)
XLS (Excel)
XML

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse