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

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 t...

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Autores principales:	Jara,José Luis, Chacón,Max, Zelaya,Gonzalo
Lenguaje:	English
Publicado:	Universidad de Tarapacá. 2011
Materias:	Clinical coding controlled vocabulary international classification of diseases machine learning natural language processing
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052011000300006
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spelling	oai:scielo:S0718-330520110003000062012-03-14Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnosesJara,José LuisChacón,MaxZelaya,Gonzalo Clinical coding controlled vocabulary international classification of diseases machine learning natural language processing 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.info:eu-repo/semantics/openAccessUniversidad de Tarapacá.Ingeniare. Revista chilena de ingeniería v.19 n.3 20112011-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052011000300006en10.4067/S0718-33052011000300006
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Clinical coding controlled vocabulary international classification of diseases machine learning natural language processing
spellingShingle	Clinical coding controlled vocabulary international classification of diseases machine learning natural language processing Jara,José Luis Chacón,Max Zelaya,Gonzalo Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses
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.
author	Jara,José Luis Chacón,Max Zelaya,Gonzalo
author_facet	Jara,José Luis Chacón,Max Zelaya,Gonzalo
author_sort	Jara,José Luis
title	Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses
title_short	Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses
title_full	Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses
title_fullStr	Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses
title_full_unstemmed	Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses
title_sort	empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses
publisher	Universidad de Tarapacá.
publishDate	2011
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052011000300006
work_keys_str_mv	AT jarajoseluis empiricalevaluationofthreemachinelearningmethodforautomaticclassificationofneoplasticdiagnoses AT chaconmax empiricalevaluationofthreemachinelearningmethodforautomaticclassificationofneoplasticdiagnoses AT zelayagonzalo empiricalevaluationofthreemachinelearningmethodforautomaticclassificationofneoplasticdiagnoses
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Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses

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