An Effective Approach for Multiclass Classification of Adverse Events Using Machine Learning

Indu Bala; Thu-Lan  Kelly; Renly Lim; Marianne H. Gillam; Lewis  Mitchell

doi:10.47852/bonviewJCCE32021924

Authors

Indu Bala School of Computer and Mathematical Sciences, The University of Adelaide, Australia https://orcid.org/0000-0002-7215-5269
Thu-Lan Kelly Clinical and Health Sciences, University of South Australia, Australia https://orcid.org/0000-0002-7691-9289
Renly Lim Clinical and Health Sciences, University of South Australia, Australia
Marianne H. Gillam Clinical and Health Sciences, University of South Australia, Australia
Lewis Mitchell School of Computer and Mathematical Sciences, The University of Adelaide, Australia

DOI:

https://doi.org/10.47852/bonviewJCCE32021924

Keywords:

post-market surveillance, adverse event, implantable medical device, machine learning, natural language processing

Abstract

Implantable medical devices are commonly used to treat various medical conditions. These devices, however, may cause serious adverse events, including repeated surgical intervention and death. Prolonged use of some implantable medical devices can shorten life expectancy and significantly decrease a person‘s quality of life. Large adverse event databases can be used to predict serious adverse events by training machine learning (ML) models on available data. However, the large volume of data and long free-text response make it challenging to use the databases effectively. This study focuses on one such dataset: the Australian Database of Adverse Event Notifications, comprising text written by patients, or healthcare professionals, or pharmaceutical industry. The study focuses on predicting three significant events: Injury, No Injury, and Death, based on the adverse events reported about the implanted device. A new ML approach called the random regression voting classifier, which combines random forest (RF) and logistic regression (LR), is proposed. The model’s efficiency is evaluated through experiments using techniques, such as Bag of Words, Term-Frequency-Inverse-Document Frequency, and Global Vector, and is compared to existing ML models such as decision tree, RF, kernel support vector machine, Naive Bayes, LR, and XGboost. The results demonstrate a higher performance in predicting adverse events than other considered approaches. The various experimental analyses showed that the proposed approach performed better than other ML models.

Received: 23 October 2023 | Revised: 27 November 2023 | Accepted: 13 December 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in the Therapeutic Goods Administration, the Department of Health and Aged Care, Austrilian Government at https://www.tga.gov.au/safety/safety/safety-monitoring-daen-database-adverse-event-notifications/database-adverse-event-notifications-daen.