Deep Neural Network to Identify Patients with Alcohol Use Disorder

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Abstrakt

This paper presents an application of deep neural networks (DNN) to identify patients with Alcohol Use Disorder based on historical electronic health records. Our methodology consists of four stages including data collection, preprocessing, predictive model development, and validation. Data are collected from two sources and labeled into three classes including Normal, Hazardous, and Harmful drinkers. Moreover, problems such as imbalanced classes, noise, and categorical variables were handled. A four-layer fully-connected feedforward DNN architecture was designed and developed to predict Normal, Hazardous, and Harmful drinkers. Results show that our proposed method could successfully classify about 96%, 82%, and 89% of Normal, Hazardous, and Harmful drinkers, respectively, which is better than classical machine learning approaches.

OriginalsprogEngelsk
TitelPublic Health and Informatics
RedaktørerJohn Mantas, Lăcrămioara Stoicu-Tivadar, Catherine Chronaki, Arie Hasman, Patrick Weber, Parisis Gallos, Mihaela Crişan-Vida, Emmanouil Zoulias, Oana Sorina Chirila
Vol/bind281
Publikationsdato27. maj 2021
Sider238-242
ISBN (Trykt)978-1-64368-184-9
ISBN (Elektronisk)978-1-64368-185-6
DOI
StatusUdgivet - 27. maj 2021
BegivenhedMIE 2021: 31st Medical Informatics Europe Conference Online -
Varighed: 29. maj 202131. maj 2021

Konference

KonferenceMIE 2021: 31st Medical Informatics Europe Conference Online
Periode29/05/202131/05/2021
NavnStudies in Health Technology and Informatics
Vol/bind281
ISSN0926-9630

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