Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

Tari, Luis; Anwar, Saadat; Liang, Shanshan; Cai, James J.; Baral, Chitta

doi:10.1093/bioinformatics/btq382

Cited by 133 publications

(143 citation statements)

References 14 publications

(16 reference statements)

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“…In contrast, the work by [16] achieved 45% with predictions based on drug metabolism. In terms of accuracy, which measures the percentage of correct predictions combining both the similar and dissimilar predictions, our system comes out at over 80% compared to 69% where drug predictions are based on the relationship between drug targets [18].…”

Section: Resultsmentioning

confidence: 85%

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Mining Drug Properties for Decision Support in Dental Clinics

Goh

Tao

Zhang

et al. 2017

Advances in Knowledge Discovery and Data Mining

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Abstract. With poly-pharmacy becoming more common, it is important for health providers to be aware of the drug profile of patients before prescribing. Although there are many methods on extracting information on drug interactions, they do not integrate with the patients' medical history. This paper describes state-of-the art approaches in extracting the term frequencies of drug properties, and using this knowledge to decide if a drug is suitable for prescription after considering if there is any drug allergy the patient may have and the drugs that the patient is currently taking. An experiment is conducted to evaluate the accuracy of associating the similarity ratio in terms of their term frequencies to the similarity between them. Experimental evaluation of our model yields an accuracy of over 80% which is superior to models that use other methods. Since a drug is to be avoided if it is similar to a drug that patient is allergic to, our model will help dentist decide if a drug is suitable for prescription to the patient. Hence such an approach, when integrated within the clinical workflow will reduce prescription errors thereby increasing the health outcome of the patients.

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Section: Resultsmentioning

confidence: 85%

“…For example, [16] has developed a method that combines text mining and automated reasoning to predict enzyme-specific DDIs. [18] also uses text mining techniques to create features based on relevant information such as genes and disease names extracted from drug databases to augment limited domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

Mining Drug Properties for Decision Support in Dental Clinics

Goh

Tao

Zhang

et al. 2017

Advances in Knowledge Discovery and Data Mining

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show abstract

“…Luis Tari, Saadat Anwar, Shanshan Liang & James Cai1 etc (2010) discover drug-drug interactions (DDI) through an innovative method which integrates text-mining tool [1]. Using this approach, they can not only detect the explicit interactions but also the potential interactions that could be derived through the use of logic representation of the domain knowledge and automated reasoning.…”

Section: Text Mining In Journals and Databasesmentioning

confidence: 99%

A Survey of Current Work in Medical Text Mining---Data Source Perspective

Li¹,

Anmeng²,

Wang³

2017

IJBSE

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This paper discusses the application of text mining in medical field at home and abroad based on overview and analysis of current literature data. Foreign researchers have specific text mining tools, and they use it in the search engine data and electronic medical record. In addition, it is also used to predict side effects between drugs. In China, text mining based on medical literature data occupies a large part. On the one hand, they can monitor the self disclosure of health information. On the other hand, they explore whether online information can help individuals get out of the disease. With the development of information technology, text mining will become more and more widely applied in the medical field in the future.

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“…Several other tools specialize in pharmacogenomics and can extract information related to drugs, such as drug adverse events [38], drug contraindications [120], and drug-drug interactions [135,140]. Moreover, the medical library science community has conducted much research work on developing search filters for drug adverse events [50].…”

Section: Challenges For Compiling Sca Contraindication Informationmentioning

confidence: 99%

Open Issues in Intelligent Personal Health Record – An Updated Status Report for 2012

Luo

2013

J Med Syst

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To improve the capability and usability of the personal health record (PHR) as a tool to empower consumers in the management of their own health, we have proposed the concept of an intelligent PHR (iPHR) and built a prototype iPHR system with four functions. These four functions use various health knowledge and computer science techniques to automatically provide users with personalized healthcare information to facilitate their wellbeing. This paper discusses several open issues in iPHR, including two enhancements to an existing function and two potential new functions. The two enhancements are for automatically compiling relevant self-care activities for each health issue and automatically identifying contraindicated self-care activities, respectively. One potential new function is personalized search for individual healthcare providers. Another potential new function is personalized local search for health-related services to help maintain patients in their homes. We include some preliminary thoughts on how to address these open issues with the hope to stimulate future research work on iPHR.Keywords Personal health record  Self-care activity  Medical information extraction  Automatic contraindication identification  Personalized search for individual healthcare providers  Personalized local search for health-related services

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Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

Cited by 133 publications

References 14 publications

Mining Drug Properties for Decision Support in Dental Clinics

Mining Drug Properties for Decision Support in Dental Clinics

A Survey of Current Work in Medical Text Mining---Data Source Perspective

Open Issues in Intelligent Personal Health Record – An Updated Status Report for 2012

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