Medical entity recognition using conditional random field (CRF)

Raditya Herwando, Meganingrum Arista Jiwanggi, Mirna Adriani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The main objective of this research is to extract the health information, such as diseases, symptoms, treatments and drugs from the health online forum discussion. The task is referred as the medical entity recognition (MER) in which is defined as the Named Entity Recognition (NER) task to extract the information from the unstructured text and transform it into the structured forms in the health field. The approach for the task used in this research is a supervised learning using Conditional Random Field(CRF). We experimented several combinations of features in order to produce the results with the best accuracy. As the final result, this research obtained the best accuracy of precision 70.97%, recall 57.83%, and f-measures 63.69%. The best combination of features resulting the best overall result consists of the word itself, phrase, dictionary, the first preceding word and the word length.

Original languageEnglish
Title of host publicationProceedings - WBIS 2017
Subtitle of host publication2017 International Workshop on Big Data and Information Security
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-62
Number of pages6
ISBN (Electronic)9781538620380
DOIs
Publication statusPublished - 29 Jan 2018
Event2017 International Workshop on Big Data and Information Security, WBIS 2017 - Jakarta, Indonesia
Duration: 23 Sep 201724 Sep 2017

Publication series

NameProceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
Volume2018-January

Conference

Conference2017 International Workshop on Big Data and Information Security, WBIS 2017
Country/TerritoryIndonesia
CityJakarta
Period23/09/1724/09/17

Keywords

  • conditional random field
  • medical entity recognition

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