A Predictive Machine Learning Model to Determine Alcohol Use Disorder

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

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Abstrakt

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.

OriginalsprogEngelsk
Titel2020 IEEE Symposium on Computers and Communications (ISCC)
Antal sider7
ForlagIEEE
Publikationsdato2020
ISBN (Elektronisk)9781728180861
DOI
StatusUdgivet - 2020
Begivenhed2020 IEEE Symposium on Computers and Communications, ISCC 2020 - Rennes, Frankrig
Varighed: 7. jul. 202010. jul. 2020

Konference

Konference2020 IEEE Symposium on Computers and Communications, ISCC 2020
Land/OmrådeFrankrig
ByRennes
Periode07/07/202010/07/2020
NavnProceedings - IEEE Symposium on Computers and Communications
Vol/bind2020-July
ISSN1530-1346

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