A novel feature-based approach to extract drug–drug interactions from biomedical text

Bui, Quoc-Chinh; Sloot, Peter M. A.; Mulligen, Erik M. van; Kors, Jan A.

doi:10.1093/bioinformatics/btu557

Cited by 76 publications

(47 citation statements)

References 18 publications

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“…Another study to extract information on drug-drug interactions (DDI) from biomedical text was proposed by Bui et al [3]. DDI pairs are mapped according to their syntactic structure followed by the generation of feature vectors for these DDI pairs.…”

Section: Related Workmentioning

confidence: 99%

“…DDI pairs are mapped according to their syntactic structure followed by the generation of feature vectors for these DDI pairs. These feature vectors are then used for the generation of a predictive model which classify the drug pair as interacting or not interacting [3].…”

Section: Related Workmentioning

confidence: 99%

“…So far many methods have been developed with the aim of extracting information on drug interactions [3,4]. A recent work by [2] has developed a literaturebased system to find out the similarity of a drug pair.…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mining Drug Properties for Decision Support in Dental Clinics

Goh

Tao

Zhang

et al. 2017

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

show abstract

“…In a previous study, [13] demonstrated that partitioning candidate DDI pairs based on their syntactic properties then using specific group of features for each partition improves the performance of the DDI extraction system. Following this strategy, we classify candidate pairs into different groups based on their positions into the sentence.…”

Section: ) Candidate Drug Pairs Partitioningmentioning

confidence: 99%

“…Trigger_DRUG_Position features: Like Bui et al [13] we determine the relative position of each trigger word within the phrase by checking the following cases:…”

Section: B) One-drug Featuresmentioning

confidence: 99%

A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction

Raihani¹,

Laachfoubi²

2017

ijacsa

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Abstract-Discovering drug-drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Developing text mining techniques for identifying DDIs has attracted a great deal of attention in the last few years. Unfortunately, state-of-the-art results didn't exceed the threshold of 0.7 F1 score, which calls for more efforts. In this work, we propose a new feature-based kernel method to extract and classify DDIs. Our approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that by using new groups of features non-linear kernels can achieve the best performance. When evaluated on the DDIExtraction 2013 challenge corpus, our system achieved an F1-score of 71.79%, as compared to 69.75% and 68.4% reported by the top two state-of-the-art systems.

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