A Predictive Machine Learning Model to Determine Alcohol Use Disorder

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

doi:10.1109/ISCC50000.2020.9219685

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 proceeding › Article in proceedings › Research › peer-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 language	English
Title of host publication	2020 IEEE Symposium on Computers and Communications (ISCC)
Number of pages	7
Publisher	IEEE
Publication date	2020
ISBN (Electronic)	9781728180861
DOIs	https://doi.org/10.1109/ISCC50000.2020.9219685
Publication status	Published - 2020
Event	2020 IEEE Symposium on Computers and Communications, ISCC 2020 - Rennes, France Duration: 7. Jul 2020 → 10. Jul 2020

Conference

Conference	2020 IEEE Symposium on Computers and Communications, ISCC 2020
Country/Territory	France
City	Rennes
Period	07/07/2020 → 10/07/2020

Series	Proceedings - IEEE Symposium on Computers and Communications
Volume	2020-July
ISSN	1530-1346

Keywords

Alcohol Use Disorder
Classification
Predictive Model
Supervised Machine Learning

Documents & Links

10.1109/ISCC50000.2020.9219685

Open Access VersionAccepted manuscript, 475 KB

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RELIP
Ebrahimi, A. (PhD student), Wiil, U. K. (Supervisor), Andersen, K. (Project participant), Christiansen, R. (Project participant) & Nielsen, A. S. (Project participant)
01/05/2018 → 31/12/2021
Project: PhD Project

Cite this

@inproceedings{6c83962f64f74975b0ed7a145f076911,

title = "A Predictive Machine Learning Model to Determine Alcohol Use Disorder",

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.",

keywords = "Alcohol Use Disorder, Classification, Predictive Model, Supervised Machine Learning",

author = "Ali Ebrahimi and Wiil, {Uffe Kock} and Kjeld Andersen and Marjan Mansourvar and Nielsen, {Anette Sogaard}",

year = "2020",

doi = "10.1109/ISCC50000.2020.9219685",

language = "English",

series = "Proceedings - IEEE Symposium on Computers and Communications",

publisher = "IEEE",

booktitle = "2020 IEEE Symposium on Computers and Communications (ISCC)",

address = "United States",

note = "2020 IEEE Symposium on Computers and Communications, ISCC 2020 ; Conference date: 07-07-2020 Through 10-07-2020",

}

Ebrahimi, A , Wiil, UK , Andersen, K, Mansourvar, M & Nielsen, AS 2020, A Predictive Machine Learning Model to Determine Alcohol Use Disorder. in 2020 IEEE Symposium on Computers and Communications (ISCC). IEEE, Proceedings - IEEE Symposium on Computers and Communications, vol. 2020-July, 2020 IEEE Symposium on Computers and Communications, ISCC 2020, Rennes, France, 07/07/2020. https://doi.org/10.1109/ISCC50000.2020.9219685

A Predictive Machine Learning Model to Determine Alcohol Use Disorder. / Ebrahimi, Ali ; Wiil, Uffe Kock ; Andersen, Kjeld et al.
2020 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2020. (Proceedings - IEEE Symposium on Computers and Communications, Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - A Predictive Machine Learning Model to Determine Alcohol Use Disorder

AU - Ebrahimi, Ali

AU - Wiil, Uffe Kock

AU - Andersen, Kjeld

AU - Mansourvar, Marjan

AU - Nielsen, Anette Sogaard

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Alcohol Use Disorder

KW - Classification

KW - Predictive Model

KW - Supervised Machine Learning

U2 - 10.1109/ISCC50000.2020.9219685

DO - 10.1109/ISCC50000.2020.9219685

M3 - Article in proceedings

AN - SCOPUS:85094103327

T3 - Proceedings - IEEE Symposium on Computers and Communications

BT - 2020 IEEE Symposium on Computers and Communications (ISCC)

PB - IEEE

T2 - 2020 IEEE Symposium on Computers and Communications, ISCC 2020

Y2 - 7 July 2020 through 10 July 2020

ER -

A Predictive Machine Learning Model to Determine Alcohol Use Disorder

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Cite this