Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data

Diana Purwitasari; Safitri Juanita; I. Ketut Eddy Purnama; Muhammad Raihan; Mauridhi Hery Purnomo

doi:10.1109/ICITISEE57756.2022.10057601

Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data

Diana Purwitasari
, Safitri Juanita
, I. Ketut Eddy Purnama
, Muhammad Raihan
, Mauridhi Hery Purnomo

Institut Teknologi Sepuluh Nopember
Budi Luhur University
University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Searching for health-related information online is becoming more difficult due to the proliferation of multiple-meaning phrases such as semantic words. This study examines the semantic process in medical terms using a collection of doctor's answer texts which requires finding an appropriate model for recognizing text in pairs of comparable Indonesian medical phrases or terminology synonyms. This study contributes to finding an automatic semantic text detection using a word embedding approach to identify text in pairs of similar Indonesian medical terms. We selected 108 pairs of annotated medical terms (Biomedical Named Entity Recognition (Bio-NER)) in Indonesian based on a collection of doctor texts with 60 pairs of similar words and 48 pairs of dissimilar words. Our dataset was processed with the word embedding approach of FastText and BioWordVec. There are two approaches of BioWordVec: with (BioWordVec-2) and without (BioWordVec) translation process. We compared the performance of FastText, BioWordVec, and BioWordVec-2 using measures like accuracy, specificity, and sensitivity. The results show that the BioWordVec-2 model performs better than other models in identifying similar pairs.

Original language	English
Title of host publication	Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
Subtitle of host publication	Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	565-569
Number of pages	5
ISBN (Electronic)	9798350399615
DOIs	https://doi.org/10.1109/ICITISEE57756.2022.10057601
Publication status	Published - 2022
Event	6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 - Virtual, Online, Indonesia Duration: 13 Dec 2022 → 14 Dec 2022

Publication series

Name	Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

Conference

Conference	6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Country/Territory	Indonesia
City	Virtual, Online
Period	13/12/22 → 14/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Indonesian Medical Terms
Question-Answer Data
Semantic Text
Word Embedding

Access to Document

10.1109/ICITISEE57756.2022.10057601

Cite this

Purwitasari, D., Juanita, S., Eddy Purnama, I. K., Raihan, M., & Purnomo, M. H. (2022). Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data. In Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022 (pp. 565-569). (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITISEE57756.2022.10057601

Purwitasari, Diana ; Juanita, Safitri ; Eddy Purnama, I. Ketut et al. / Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data. Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 565-569 (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022).

@inproceedings{9a39e6d044744de1b61e7ea035210632,

title = "Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data",

abstract = "Searching for health-related information online is becoming more difficult due to the proliferation of multiple-meaning phrases such as semantic words. This study examines the semantic process in medical terms using a collection of doctor's answer texts which requires finding an appropriate model for recognizing text in pairs of comparable Indonesian medical phrases or terminology synonyms. This study contributes to finding an automatic semantic text detection using a word embedding approach to identify text in pairs of similar Indonesian medical terms. We selected 108 pairs of annotated medical terms (Biomedical Named Entity Recognition (Bio-NER)) in Indonesian based on a collection of doctor texts with 60 pairs of similar words and 48 pairs of dissimilar words. Our dataset was processed with the word embedding approach of FastText and BioWordVec. There are two approaches of BioWordVec: with (BioWordVec-2) and without (BioWordVec) translation process. We compared the performance of FastText, BioWordVec, and BioWordVec-2 using measures like accuracy, specificity, and sensitivity. The results show that the BioWordVec-2 model performs better than other models in identifying similar pairs.",

keywords = "Indonesian Medical Terms, Question-Answer Data, Semantic Text, Word Embedding",

author = "Diana Purwitasari and Safitri Juanita and \{Eddy Purnama\}, \{I. Ketut\} and Muhammad Raihan and Purnomo, \{Mauridhi Hery\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 ; Conference date: 13-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/ICITISEE57756.2022.10057601",

language = "English",

series = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "565--569",

booktitle = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering",

address = "United States",

}

Purwitasari, D, Juanita, S, Eddy Purnama, IK, Raihan, M & Purnomo, MH 2022, Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data. in Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022, Institute of Electrical and Electronics Engineers Inc., pp. 565-569, 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022, Virtual, Online, Indonesia, 13/12/22. https://doi.org/10.1109/ICITISEE57756.2022.10057601

Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data. / Purwitasari, Diana; Juanita, Safitri; Eddy Purnama, I. Ketut et al.
Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 565-569 (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data

AU - Purwitasari, Diana

AU - Juanita, Safitri

AU - Eddy Purnama, I. Ketut

AU - Raihan, Muhammad

AU - Purnomo, Mauridhi Hery

PY - 2022

Y1 - 2022

N2 - Searching for health-related information online is becoming more difficult due to the proliferation of multiple-meaning phrases such as semantic words. This study examines the semantic process in medical terms using a collection of doctor's answer texts which requires finding an appropriate model for recognizing text in pairs of comparable Indonesian medical phrases or terminology synonyms. This study contributes to finding an automatic semantic text detection using a word embedding approach to identify text in pairs of similar Indonesian medical terms. We selected 108 pairs of annotated medical terms (Biomedical Named Entity Recognition (Bio-NER)) in Indonesian based on a collection of doctor texts with 60 pairs of similar words and 48 pairs of dissimilar words. Our dataset was processed with the word embedding approach of FastText and BioWordVec. There are two approaches of BioWordVec: with (BioWordVec-2) and without (BioWordVec) translation process. We compared the performance of FastText, BioWordVec, and BioWordVec-2 using measures like accuracy, specificity, and sensitivity. The results show that the BioWordVec-2 model performs better than other models in identifying similar pairs.

AB - Searching for health-related information online is becoming more difficult due to the proliferation of multiple-meaning phrases such as semantic words. This study examines the semantic process in medical terms using a collection of doctor's answer texts which requires finding an appropriate model for recognizing text in pairs of comparable Indonesian medical phrases or terminology synonyms. This study contributes to finding an automatic semantic text detection using a word embedding approach to identify text in pairs of similar Indonesian medical terms. We selected 108 pairs of annotated medical terms (Biomedical Named Entity Recognition (Bio-NER)) in Indonesian based on a collection of doctor texts with 60 pairs of similar words and 48 pairs of dissimilar words. Our dataset was processed with the word embedding approach of FastText and BioWordVec. There are two approaches of BioWordVec: with (BioWordVec-2) and without (BioWordVec) translation process. We compared the performance of FastText, BioWordVec, and BioWordVec-2 using measures like accuracy, specificity, and sensitivity. The results show that the BioWordVec-2 model performs better than other models in identifying similar pairs.

KW - Indonesian Medical Terms

KW - Question-Answer Data

KW - Semantic Text

KW - Word Embedding

UR - https://www.scopus.com/pages/publications/85150469224

U2 - 10.1109/ICITISEE57756.2022.10057601

DO - 10.1109/ICITISEE57756.2022.10057601

M3 - Conference contribution

AN - SCOPUS:85150469224

T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

SP - 565

EP - 569

BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

Y2 - 13 December 2022 through 14 December 2022

ER -

Purwitasari D, Juanita S, Eddy Purnama IK, Raihan M, Purnomo MH. Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data. In Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 565-569. (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022). doi: 10.1109/ICITISEE57756.2022.10057601

Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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