TY - GEN
T1 - Medical entity recognition using conditional random field (CRF)
AU - Herwando, Raditya
AU - Jiwanggi, Meganingrum Arista
AU - Adriani, Mirna
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
KW - conditional random field
KW - medical entity recognition
UR - http://www.scopus.com/inward/record.url?scp=85050734408&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2017.8275103
DO - 10.1109/IWBIS.2017.8275103
M3 - Conference contribution
AN - SCOPUS:85050734408
T3 - Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
SP - 57
EP - 62
BT - Proceedings - WBIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Workshop on Big Data and Information Security, WBIS 2017
Y2 - 23 September 2017 through 24 September 2017
ER -