Addressing Opioid Epidemic through Data Analtyics
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".
Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 2609
Special Issue Editor
Interests: health informatics; health disparity; opioid-born health issues; health information technology; big data analytics
Special Issue Information
Dear Colleagues,
In 2018, the National Institute on Drug Abuse reported that 128 people die every day in the United States after overdosing on opioids. A recent rapid increase in opioid abuse and misuse has cost many lives, increased economic burdens, and caused social issues.
While opioid misuse affects all, victims of opioid overdoses are concentrated in specific populations, including people with less education, people in lower socioeconomic classes, older aged people, and different racial groups. As such, addressing the opioid epidemic means confronting and combating socioeconomic and health disparities.
Substance users commonly use polydrugs. Therefore, when investigating opioid overdoses, we may need to consider multi-drug effects in our analysis because interaction effects in polydrug use could result in a very different outcome from sole opioid use. In order to address this complex problem, research strategies should be innovative.
Big data analytics have opened up new pathways to conduct research that can serve as the basis for evidence-based treatments and improvements in patient care quality. While these emerging techniques have opened up new ways to discover knowledge, improve patient care, and offer preventive treatments, the healthcare field has not taken full advantage of these analytical methods.
This Special Issue focuses on addressing the opioid epidemic through these innovative analytical strategies. Special preference will be given to articles concerning the discovery of opioid-born health problems through data mining, machine learning, text mining, and image mining, among many other data analytics techniques.
Dr. Yong-Mi Kim
Guest Editor
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Keywords
- opioids
- health informatics
- data analytics
- prevention
- evidence-based treatments
- substance use
- health disparity
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