Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses

dc.creator	Jara,José Luis
dc.creator	Chacón,Max
dc.creator	Zelaya,Gonzalo
dc.date	2011-12-01
dc.date.accessioned	2019-04-24T21:28:16Z
dc.date.available	2019-04-24T21:28:16Z
dc.identifier	https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-33052011000300006
dc.identifier.uri	http://revistaschilenas.uchile.cl/handle/2250/58745
dc.description	Diagnoses are a valuable source of information for evaluating a health system. However, they are not used extensively by information systems because diagnoses are normally written in natural language. This work empirically evaluates three machine learning methods to automatically assign codes from the International Classification of Diseases (10th Revision) to 3,335 distinct diagnoses of neoplasms obtained from UMLS®. This evaluation is conducted on three different types of preprocessing. The results are encouraging: a well-known rule induction method and maximum entropy models achieve 90% accuracy in a balanced cross-validation experiment.
dc.format	text/html
dc.language	en
dc.publisher	Universidad de Tarapacá.
dc.relation	10.4067/S0718-33052011000300006
dc.rights	info:eu-repo/semantics/openAccess
dc.source	Ingeniare. Revista chilena de ingeniería v.19 n.3 2011
dc.subject	Clinical coding
dc.subject	controlled vocabulary
dc.subject	international classification of diseases
dc.subject	machine learning
dc.subject	natural language processing
dc.title	Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses