FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2147036606> ?p ?o ?g. }

• W2147036606 abstract "A drug-drug interaction (DDI) occurs when the effects of a drug are modified by the presence of other drugs. DDIs can decrease therapeutic benefit or efficacy of treatments and this could have very harmful consequences in the patient’s health that could even cause the patient’s death. Knowing the interactions between prescribed drugs is of great clinical importance; it is very important to keep databases up-to-date with respect to new DDI. In this thesis we aim to build a system to assist healthcare professionals to be updated about published drug-drug interactions. The goal of this thesis is to study a method based on maximal frequent sequences (MFS) and machine learning techniques in order to automatically detect interactions between drugs in pharmacological and medical literature. With the study of these methods, the information technology community will assist healthcare community to update their drug interactions database in a fast and semi-automatic way. In a first solution, we classify pharmacological sentences depending on whether or not they are describing a drug-drug interaction. This would enable to automatically find sentences containing drug-drug interactions. This solution is completely based on maximal frequent sequences extracted from a set of test documents. In a second solution based on machine learning, we go further in the search and perform DDI extraction, determining whether two specific drugs appearing in a sentence interact or not. This can be used as an assisting tool to populate databases with drug-drug interactions. The machine learning classifier is trained with several features: bag of words, word categories, MFS, token and char level features, as well as drug level features. We used a Random Forest classifier. With this system we participated at the DDIExtraction 2011 competition, where we obtained 6th position. Finally, we introduce Maximal Frequent Discriminative Sequences (MFDS), a new method for sequential pattern discovery that extends the concept of MFS to adapt it to classification tasks." @default.
• W2147036606 created "2016-06-24" @default.
• W2147036606 creator A5062221059 @default.
• W2147036606 date "2012-05-02" @default.
• W2147036606 modified "2023-09-27" @default.
• W2147036606 title "Maximal Frequent Sequences Applied to Drug-Drug Interaction Extraction" @default.
• W2147036606 cites W125285667 @default.
• W2147036606 cites W1480489870 @default.
• W2147036606 cites W1500125366 @default.
• W2147036606 cites W1506285740 @default.
• W2147036606 cites W1531251878 @default.
• W2147036606 cites W1550258693 @default.
• W2147036606 cites W1553696291 @default.
• W2147036606 cites W1557970708 @default.
• W2147036606 cites W1558781751 @default.
• W2147036606 cites W1584003909 @default.
• W2147036606 cites W1585780903 @default.
• W2147036606 cites W1586190625 @default.
• W2147036606 cites W1602390003 @default.
• W2147036606 cites W1809421818 @default.
• W2147036606 cites W1976526581 @default.
• W2147036606 cites W1977223481 @default.
• W2147036606 cites W2009313526 @default.
• W2147036606 cites W20184837 @default.
• W2147036606 cites W2026447576 @default.
• W2147036606 cites W2044453906 @default.
• W2147036606 cites W20722260 @default.
• W2147036606 cites W2078531432 @default.
• W2147036606 cites W2106618845 @default.
• W2147036606 cites W2108697621 @default.
• W2147036606 cites W2111461166 @default.
• W2147036606 cites W2117173011 @default.
• W2147036606 cites W2121844933 @default.
• W2147036606 cites W2125227861 @default.
• W2147036606 cites W2127976940 @default.
• W2147036606 cites W2128833260 @default.
• W2147036606 cites W2129932701 @default.
• W2147036606 cites W2136197452 @default.
• W2147036606 cites W2140604557 @default.
• W2147036606 cites W2143182259 @default.
• W2147036606 cites W2157628440 @default.
• W2147036606 cites W2168196587 @default.
• W2147036606 cites W2184865040 @default.
• W2147036606 cites W2399151302 @default.
• W2147036606 cites W2911964244 @default.
• W2147036606 cites W2955334247 @default.
• W2147036606 cites W2974147309 @default.
• W2147036606 cites W2991491469 @default.
• W2147036606 cites W30792339 @default.
• W2147036606 cites W31959178 @default.
• W2147036606 cites W563209854 @default.
• W2147036606 cites W2291969356 @default.
• W2147036606 cites W2509544553 @default.
• W2147036606 cites W2522435748 @default.
• W2147036606 hasPublicationYear "2012" @default.
• W2147036606 type Work @default.
• W2147036606 sameAs 2147036606 @default.
• W2147036606 citedByCount "0" @default.
• W2147036606 crossrefType "journal-article" @default.
• W2147036606 hasAuthorship W2147036606A5062221059 @default.
• W2147036606 hasConcept C103637391 @default.
• W2147036606 hasConcept C119857082 @default.
• W2147036606 hasConcept C154945302 @default.
• W2147036606 hasConcept C155261790 @default.
• W2147036606 hasConcept C177264268 @default.
• W2147036606 hasConcept C199360897 @default.
• W2147036606 hasConcept C204321447 @default.
• W2147036606 hasConcept C2780035454 @default.
• W2147036606 hasConcept C2910466267 @default.
• W2147036606 hasConcept C41008148 @default.
• W2147036606 hasConcept C71924100 @default.
• W2147036606 hasConcept C95623464 @default.
• W2147036606 hasConcept C97320921 @default.
• W2147036606 hasConcept C98274493 @default.
• W2147036606 hasConceptScore W2147036606C103637391 @default.
• W2147036606 hasConceptScore W2147036606C119857082 @default.
• W2147036606 hasConceptScore W2147036606C154945302 @default.
• W2147036606 hasConceptScore W2147036606C155261790 @default.
• W2147036606 hasConceptScore W2147036606C177264268 @default.
• W2147036606 hasConceptScore W2147036606C199360897 @default.
• W2147036606 hasConceptScore W2147036606C204321447 @default.
• W2147036606 hasConceptScore W2147036606C2780035454 @default.
• W2147036606 hasConceptScore W2147036606C2910466267 @default.
• W2147036606 hasConceptScore W2147036606C41008148 @default.
• W2147036606 hasConceptScore W2147036606C71924100 @default.
• W2147036606 hasConceptScore W2147036606C95623464 @default.
• W2147036606 hasConceptScore W2147036606C97320921 @default.
• W2147036606 hasConceptScore W2147036606C98274493 @default.
• W2147036606 hasLocation W21470366061 @default.
• W2147036606 hasOpenAccess W2147036606 @default.
• W2147036606 hasPrimaryLocation W21470366061 @default.
• W2147036606 isParatext "false" @default.
• W2147036606 isRetracted "false" @default.
• W2147036606 magId "2147036606" @default.
• W2147036606 workType "article" @default.