We propose a methodology for semi-automatic derivation of knowledge from medical records by means of both statistical and lexical approaches. Moreover, we propose a statistic based methodology for the peculiar lexicon extracted quality evaluation. The evaluation is performed by means of a semantic distance, based on chi-square statistical measure, between the lexicon extracted and the corpus composed by the set of medical record analyzed. The methodology can be used for automatic medical record sections identification in order to improve the interactions among different actors belonging to the health care domain.
Evaluating peculiar lexicon for medical record sections identification / Amato, Flora; Mazzeo, Antonino; Romano, Sara; Scippacercola, Sergio. - (2011). (Intervento presentato al convegno Innovation and Society 2011 tenutosi a Florence (Italy) nel 30 May - 1 June 2011).
Evaluating peculiar lexicon for medical record sections identification
AMATO, FLORA;MAZZEO, ANTONINO;ROMANO, SARA;SCIPPACERCOLA, SERGIO
2011
Abstract
We propose a methodology for semi-automatic derivation of knowledge from medical records by means of both statistical and lexical approaches. Moreover, we propose a statistic based methodology for the peculiar lexicon extracted quality evaluation. The evaluation is performed by means of a semantic distance, based on chi-square statistical measure, between the lexicon extracted and the corpus composed by the set of medical record analyzed. The methodology can be used for automatic medical record sections identification in order to improve the interactions among different actors belonging to the health care domain.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
