Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice.

Masaki Kobayashi,Yoh Takekuma,Shungo Imai,Hitoshi Kashiwagi,Ken Iseki,Takayuki Miyai,Le Hoang Son,Mitsuru Sugawara

doi:10.1371/journal.pone.0236789

Masaki Kobayashi, Yoh Takekuma + Show 6 more

Open Access

https://doi.org/10.1371/journal.pone.0236789

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Journal: PloS one	Publication Date: Jul 29, 2020
Citations: 21	License type: CC BY 4.0

Affiliation: Hokkaido University, Hokkaido University Hospital

Abstract

Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction of adverse drug reactions. We aimed to validate the usefulness of artificial neural networks for the prediction of adverse drug reactions and focused on vancomycin -induced nephrotoxicity. For constructing an artificial neural network, a multilayer perceptron algorithm was employed. A 10-fold cross validation method was adopted for evaluating the resultant artificial neural network. In total, 1141 patients who received vancomycin at Hokkaido University Hospital from November 2011 to February 2019 were enrolled. Among these patients, 179 (15.7%) developed vancomycin -induced nephrotoxicity. The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration ≥ 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs. The predictive accuracy of the artificial neural network was 86.3% and that of the multiple logistic regression model (conventional statistical method) was 85.1%. Moreover, area under the receiver operating characteristic curve (AUROC) of the artificial neural network was 0.83. In the 10-fold cross-validation, the accuracy obtained was 86.0% and AUROC was 0.82. The artificial neural network model predicting the vancomycin -induced nephrotoxicity showed good predictive performance. This appears to be the first report of the usefulness of artificial neural networks for an adverse drug reactions risk prediction model.

Highlights

The process of data mining is defined as the use of techniques to identify hidden correlations and patterns from complex datasets
The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs
To the best of our knowledge, this is the first study to validate the usefulness of Artificial neural networks (ANNs) applied to a risk prediction model of adverse drug reactions (ADRs) for individual patients in clinical practice by constructing a risk prediction model of VIN

Summary

Introduction

The process of data mining is defined as the use of techniques to identify hidden correlations and patterns from complex datasets. Validation of the usefulness of artificial neural networks for prediction of adverse drug reactions constructing predictive models based on the discovery of underlying patterns and relationships in large datasets [1]. Artificial neural networks (ANNs) are among the main tools used for data mining They have a complex computational structure that is inspired by the human brain and nervous system [2]. Such an ANN would be very useful, so it is important to validate its usefulness when applied to risk prediction models for clinical practice

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