Multi-sense agent for proactive screening of alcohol addiction
Date
2019-08
Authors
Muchiri, Wallace
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Addiction is a complex condition, a brain disease that is manifested by compulsive
substance use despite harmful consequence(s). People with addiction (severe substance
use disorder) have an intense focus on using certain substance(s), such as alcohol, drugs
or pornography as seen in recent studies, to the point that it takes over their life. Diagnosis
of addiction to any substance is usually done in a reactive manner whereby the person is
identified once the external symptoms manifest themselves due to the fact that the vast
majority of individuals do not seek treatment for their condition. This can be attributed
partly to the failure to diagnose early by primary care physicians, the stigma by the society
and self-denial by the potential addict since they choose not to seek help until they hit
rock-bottom (Zhang & Ho, 2016) thus aggravating the already unwanted situation.
Recently there is a shift to the use of ICT techniques such as mobile devices to detect
various health symptoms proactively with researchers developing objective mobile datadriven
biomarkers for many healthcare conditions such as depression which is highly
related to addiction. Furthermore the development of machine learning decision support
systems such as the SimSensei Kiosk which is used alongside qualified psychologists in
diagnosis of post-traumatic stress disorder have validated the fact that it is possible to
successfully implement autonomous diagnosis systems. The aim of this study is to
propose an autonomous agent based on the multi-sense framework that can be used on
demand for proactive diagnosis of alcohol addiction.
Description
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenya
Keywords
Machine learning, Multi-sense framework, Computer vision