Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination

Ramani Routray (), Niki Tetarenko, Claire Abu-Assal, Ruta Mockute, Bruno Assuncao, Hanqing Chen, Shenghua Bao, Karolina Danysz, Sameen Desai, Salvatore Cicirello, Willis Van, Sharon Hensley Alford, Vivek Krishnamurthy and Edward Mingle
Additional contact information
Ramani Routray: IBM Watson Health
Niki Tetarenko: Celgene
Claire Abu-Assal: IBM Watson Health
Ruta Mockute: Celgene
Bruno Assuncao: Celgene
Hanqing Chen: IBM Watson Health
Shenghua Bao: IBM Watson Health
Karolina Danysz: Celgene
Sameen Desai: Celgene
Salvatore Cicirello: Celgene
Willis Van: IBM Watson Health
Sharon Hensley Alford: IBM Watson Health
Vivek Krishnamurthy: IBM Watson Health
Edward Mingle: Celgene

Drug Safety, 2020, vol. 43, issue 1, No 7, 57-66

Abstract: Abstract Introduction Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. Adverse event seriousness is a key factor in defining reporting timelines and is often performed manually by pharmacovigilance experts. The dramatic increase in the volume of safety reports necessitates exploration of scalable solutions that also meet reporting timeline requirements. Objective The aim of this study was to develop an augmented intelligence methodology for automatically identifying adverse event seriousness in spontaneous, solicited, and medical literature safety reports. Deep learning models were evaluated for accuracy and/or the F1 score against a ground truth labeled by pharmacovigilance experts. Methods Using a stratified random sample of safety reports received by Celgene, we developed three neural networks for addressing identification of adverse event seriousness: (1) a binary adverse-event level seriousness classifier; (2) a classifier for determining seriousness categorization at the adverse-event level; and (3) an annotator for identifying seriousness criteria terms to provide supporting evidence at the document level. Results The seriousness classifier achieved an accuracy of 83.0% in post-marketing reports, 92.9% in solicited reports, and 86.3% in medical literature reports. F1 scores for seriousness categorization were 77.7 for death, 78.9 for hospitalization, and 75.5 for important medical events. The seriousness annotator achieved an F1 score of 89.9 in solicited reports, and 75.2 in medical literature reports. Conclusions The results of this study indicate that a neural network approach can provide an accurate and scalable solution for potentially augmenting pharmacovigilance practitioner determination of adverse event seriousness in spontaneous, solicited, and medical literature reports.

Date: 2020
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DOI: 10.1007/s40264-019-00869-4

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