Analysis of Comorbidities of Alcohol Use Disorder

• Published in: 2021 IEEE Symposium on Computers and Communications (ISCC) pp. 1 - 7
• Main Authors: Ebrahimi, Ali, Wiil, Uffe Kock, Mansourvar, Marjan, Naemi, Amin, Andersen, Kjeld, Nielsen, Anette Sogaard
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.09.2021
• Subjects: Alcohol use disorder; Alcoholic beverages; Clinical diagnosis; Codes; Comorbidity; Computers; Electronic health records; Machine learning; Machine learning algorithms; Psychology; Social network analysis; Social networking (online)
• ISSN: 2642-7389
• DOI: 10.1109/ISCC53001.2021.9631512

Alcohol Use Disorder (AUD) is a clinical diagnosis based on signs and symptoms that are related to excessive alcohol use and it increases the risk of many clinical conditions, psychological instabilities, and social issues. In this study, we aim to identify the comorbidities of Hazardous and Harmful drinkers. We obtained comorbidity networks for Hazardous, and Harmful drinkers using social network analysis techniques. Each network consists of several nodes that represent the diagnostic codes that patients were given during their hospitalization. To precisely identify the most exclusive comorbidities in each drinking group, we proposed a four-step process based on a machine learning algorithm and aggregation functions. Our findings show that the majority of the identified comorbidities in the Harmful drinking group are related to ICD-10 chapters XI, XIX, and XIII. The comorbidities of the Hazardous drinking group, however, did not present a similarly clear pattern.