Towards the Development of a Substance Abuse Index (SEI) through Informatics

Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other ext...

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Autores principales:	Nikhila Guttha, Zhuqi Miao, Rittika Shamsuddin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	substance abuse medicinal drug dependence substance effect index machine learning standard measure logistic regression Medicine R
Acceso en línea:	https://doaj.org/article/3298dbe2ff1c4457afc54eb5c02fc9f1
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spelling	oai:doaj.org-article:3298dbe2ff1c4457afc54eb5c02fc9f12021-11-25T17:46:46ZTowards the Development of a Substance Abuse Index (SEI) through Informatics10.3390/healthcare91115962227-9032https://doaj.org/article/3298dbe2ff1c4457afc54eb5c02fc9f12021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9032/9/11/1596https://doaj.org/toc/2227-9032Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other external influences (e.g., pharmaceutical advertising campaigns). However, currently there is no comprehensive computational or machine learning framework that allows systematic studies of substance abuse and its factors with majority of the works using subjective surveys questionnaires and focusing on classification techniques. Lacking standardized methods and/or measures to prescribe medication and to study substance abuse makes it difficult to advance through collective research efforts. Thus, in this paper, we propose to test the feasibility of developing a, objective substance effect index, SEI, that can measure the tendency of an individual towards substance abuse. To that end, we combine the benefits of Electronics Medical Records (EMR) with machine learning technology by defining SEI as a function of EMR data and using logistics regression to obtain a closed form expression for SEI. We conduct various evaluations to validate the proposed model, and the results show that further work towards the development of SEI will not only provide researchers with standard computational measure for substance abuse, but may also allow them to study certain attribute interactions to gain further insights into substance abuse tendencies.Nikhila GutthaZhuqi MiaoRittika ShamsuddinMDPI AGarticlesubstance abusemedicinal drug dependencesubstance effect indexmachine learningstandard measurelogistic regressionMedicineRENHealthcare, Vol 9, Iss 1596, p 1596 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	substance abuse medicinal drug dependence substance effect index machine learning standard measure logistic regression Medicine R
spellingShingle	substance abuse medicinal drug dependence substance effect index machine learning standard measure logistic regression Medicine R Nikhila Guttha Zhuqi Miao Rittika Shamsuddin Towards the Development of a Substance Abuse Index (SEI) through Informatics
description	Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other external influences (e.g., pharmaceutical advertising campaigns). However, currently there is no comprehensive computational or machine learning framework that allows systematic studies of substance abuse and its factors with majority of the works using subjective surveys questionnaires and focusing on classification techniques. Lacking standardized methods and/or measures to prescribe medication and to study substance abuse makes it difficult to advance through collective research efforts. Thus, in this paper, we propose to test the feasibility of developing a, objective substance effect index, SEI, that can measure the tendency of an individual towards substance abuse. To that end, we combine the benefits of Electronics Medical Records (EMR) with machine learning technology by defining SEI as a function of EMR data and using logistics regression to obtain a closed form expression for SEI. We conduct various evaluations to validate the proposed model, and the results show that further work towards the development of SEI will not only provide researchers with standard computational measure for substance abuse, but may also allow them to study certain attribute interactions to gain further insights into substance abuse tendencies.
format	article
author	Nikhila Guttha Zhuqi Miao Rittika Shamsuddin
author_facet	Nikhila Guttha Zhuqi Miao Rittika Shamsuddin
author_sort	Nikhila Guttha
title	Towards the Development of a Substance Abuse Index (SEI) through Informatics
title_short	Towards the Development of a Substance Abuse Index (SEI) through Informatics
title_full	Towards the Development of a Substance Abuse Index (SEI) through Informatics
title_fullStr	Towards the Development of a Substance Abuse Index (SEI) through Informatics
title_full_unstemmed	Towards the Development of a Substance Abuse Index (SEI) through Informatics
title_sort	towards the development of a substance abuse index (sei) through informatics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3298dbe2ff1c4457afc54eb5c02fc9f1
work_keys_str_mv	AT nikhilaguttha towardsthedevelopmentofasubstanceabuseindexseithroughinformatics AT zhuqimiao towardsthedevelopmentofasubstanceabuseindexseithroughinformatics AT rittikashamsuddin towardsthedevelopmentofasubstanceabuseindexseithroughinformatics
_version_	1718412035314679808

Towards the Development of a Substance Abuse Index (SEI) through Informatics

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