A Predictive Machine Learning Model to Determine Alcohol Use Disorder

Ali Ebrahimi*, Uffe Kock Wiil, Kjeld Andersen, Marjan Mansourvar, Anette Sogaard Nielsen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Prediction of alcohol use disorder (AUD) may reduce the number of deaths caused by alcohol-related diseases. However, prediction of AUD based on patients' historical clinical data is still an open research objective. This study proposes a method to predict AUD from electronic health record (EHR) data through supervised machine learning. The study creates a dataset based on the combination of EHR data with patient reported data from 2,571 patients in the Region of Southern Denmark. After that, the dataset is labeled into two categories, AUD positive (457) and AUD negative (2,114). This unique dataset is used to validate the proposed method for prediction of AUD using machine learning methods based on historical clinical data from EHRs.

Original languageEnglish
Title of host publication2020 IEEE Symposium on Computers and Communications (ISCC)
Number of pages7
PublisherIEEE
Publication date2020
ISBN (Electronic)9781728180861
DOIs
Publication statusPublished - 2020
Event2020 IEEE Symposium on Computers and Communications, ISCC 2020 - Rennes, France
Duration: 7. Jul 202010. Jul 2020

Conference

Conference2020 IEEE Symposium on Computers and Communications, ISCC 2020
CountryFrance
CityRennes
Period07/07/202010/07/2020
SeriesProceedings - IEEE Symposium on Computers and Communications
Volume2020-July
ISSN1530-1346

Keywords

  • Alcohol Use Disorder
  • Classification
  • Predictive Model
  • Supervised Machine Learning

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