Data Driven Classification of Opioid Patients Using Machine Learning-An Investigation

Saddam Al Amin, Md Saddam Hossain Mukta*, Md Sezan Mahmud Saikat, Md Ismail Hossain, Md Adnanul Islam, Mohiuddin Ahmed, Sami Azam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The opioid crisis has led to an increased number of drug overdoses in recent years. Several approaches have been established to predict opioid prescription by health practitioners. However, due to the complex nature of the problem, the accuracy of such methods is not yet satisfactory. Dependable and reliable classification of opioid dependent patients from well-grounded data sources is essential. Majority of the previous studies do not focus on the users' mental health association for opioid intake classification. These studies do not also employ the latest deep learning based techniques such as attention and knowledge distillation mechanism to find better insights. This paper investigates the opioid classification problem by using machine learning and deep learning based techniques. We used structured and unstructured data from the MIMIC-III database to identify intentional and unintentional intake of opioid drugs. We selected 455 patient instances and used traditional machine learning and deep learning to predict intentional and accidental users. We obtained 95% and 64% test accuracy to predict the intentional and accidental users from the structured and unstructured datasets, respectively. We also achieve a distilled knowledge based test accuracy of 76.44% from the integrated above two models. Our research includes an ablation analysis and new insights related to opioid patients are extracted.

Original languageEnglish
Pages (from-to)396-409
Number of pages14
JournalIEEE Access
Volume11
DOIs
StatePublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • MIMIC-III database
  • Opioid intake
  • deep learning
  • machine learning
  • mental illness

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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