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.
Proceedings of the 10th Italian Information Retrieval Workshop, Padova, Italy, September 16-18, 2019
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3333687
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