http://www.cnr.it/ontology/cnr/individuo/prodotto/ID277187

Variable-constraint classification and quantification of radiology reports under the ACR Index (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Variable-constraint classification and quantification of radiology reports under the ACR Index (Articolo in rivista) (literal)

Anno

2013-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1016/j.eswa.2012.12.052 (literal)

Alternative label

Baccianella S., Esuli A., Sebastiani F. (2013)
Variable-constraint classification and quantification of radiology reports under the ACR Index
in Expert systems with applications
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Baccianella S., Esuli A., Sebastiani F. (literal)

Pagina inizio

3441 (literal)

Pagina fine

3449 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://www.sciencedirect.com/science/article/pii/S0957417412012936 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

40 (literal)

Rivista

Expert systems with applications (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

9 (literal)

Note

ISI Web of Science (WOS) (literal)
Scopu (literal)
PuMa (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy (literal)

Titolo

Variable-constraint classification and quantification of radiology reports under the ACR Index (literal)

Abstract

We apply hierarchical supervised learning technology to the problem of assigning codes from the well-known ACR Index (a \"double-hierarchy\" classification scheme from the American College of Radiology) to radiology reports. This task is actually two classification tasks in one: the former uses a first hierarchy of codes describing anatomic locations, and the latter uses a second hierarchy of codes describing pathologies, where the two hierarchies are closely intertwined. A requirement of each such classification task is that the document be placed in exactly one node of depth >= 2 of the \"anatomic location\" hierarchy and in exactly one node of depth >= 3 of the \"pathology\" hierarchy; this makes our task a (fairly uncommon) variable-constraint classification task, since at the first levels of the hierarchy (2 for anatomic location, 3 for pathology) we need to use a standard \"exactly 1 class per document\" constraint, while at the lower levels we need to use an \"at most 1 class per document\" constraint. We have used a large dataset of about 250,000 radiology reports written in Italian and an adaptation of our TreeBoost.MH learning algorithm to variable-constraint classification. Notwithstanding the extreme difficulty of the task (given by the fact that the two codes had to be picked out of a pool of 719 codes for anatomic location and 5,269 codes for pathology, respectively) our system displayed good accuracy, indicating that it may represent a viable tool for semi-automated classification of medical reports. We also analyzed the quantification accuracy of our system (i.e., the ability of the system at correctly estimating the frequency of the individual codes), a concern of special interest in epidemiology; the results show that our system has excellent quantification accuracy, making this system a valuable tool for the fully automated coding of radiology reports for epidemiological purposes. (literal)

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