Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a graph-based method to ight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations beten concepts. This representation naturally captures dependencies beten concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations.#R##N# #R##N# apply existing graph-based term ighting methods to ight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as ll as encapsulates terms belonging to a single medical entity into a single concept. In addition,  further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain.#R##N# #R##N#Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches.
['Bevan Koopman', 'Guido Zuccon', 'Peter Bruza', 'Laurianne Sitbon', 'Michael Lawley']
I am writing a research paper on a idea Graph-based concept weighting for medical information retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Graph-based concept weighting for medical information retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a graph-based method to ight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations beten concepts. This representation naturally captures dependencies beten concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations.#R##N# #R##N# apply existing graph-based term ighting methods to ight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as ll as encapsulates terms belonging to a single medical entity into a single concept. In addition,  further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain.#R##N# #R##N#Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches."}]