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An Ontology-Based Approach for Detecting Drug Abuse Epidemiology

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Drug addiction is a kind of brain disease that is associated with developing its habit which in turn affects human behavior. The impact of the addiction can be far more obliterating. Drug abuse affects human being as in Cardiovascular syndrome, stroke, melanoma, HIV/AIDS, hepatitis, kidney, lung and liver damage, neurological and hormonal effects and hence mortality. The ontology based system that has been developed on drug abuse is unfortunately only one that is named as PREDOSE. The main goal of PREDOSE was to extract the knowledge that is relevant to the illicit drug usage from websites and blogs. PREDOSE lacks the brand names and slang terms of drugs along with the risk factors associated with the illicit drugs. It does not provide the information of banned drugs in different countries. The extended version that is the main aim of this research is E-DAO that overcomes all the lacks of PREDOSE and thus provides efficient results. It facilitates the end users in many ways by providing the useful information related to drug abuse and its banned information. Using this new approach for extended ontology, users can query the knowledge base for information like getting the street names or slang terms for the drugs. Users can also get to know about different company names for a single chemical formula drug along with their side effects. Scarce information is updated and extended in E-DAO as numbers of classes were increased from 43 to 114. Moreover, the model becomes more time efficient by defining the semantic rules.
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Keywords: DRUG ABUSE; EPIDEMIOLOGY; KNOWLEDGE BASE; ONTOLOGY; OPIOID; VOCABULARY

Document Type: Research Article

Publication date: October 1, 2017

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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