The study presents a methodology that contributes to reduce the semantic gap in clinical decision support systems. The methodology integrates semantic information -- provided by external knowledge resources -- into unsupervised neural Information Retrieval (IR) models. The objective is to design and develop innovative methods that can be effective in real-case medical scenarios.
Knowledge Enhanced Representations for Clinical Decision Support
Stefano Marchesin
;Maristella Agosti
2019
Abstract
The study presents a methodology that contributes to reduce the semantic gap in clinical decision support systems. The methodology integrates semantic information -- provided by external knowledge resources -- into unsupervised neural Information Retrieval (IR) models. The objective is to design and develop innovative methods that can be effective in real-case medical scenarios.
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