TY - GEN
T1 - Disease Similarity Measurement Implementation for Atlas of Human Infectious Diseases Using BIOSSES
AU - Fortuna, Dian Nizzah
AU - Vinarti, Retno Aulia
AU - Mahananto, Faizal
AU - Putri, Atina Irani Wira
AU - Fadli, Sonny
AU - Handayani, Vitria Wuri
AU - Aryanto, Anang Fajar
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Over the past 30 years, more than 30 emerging infectious diseases (EIDs) have surfaced globally, necessitating coordinated efforts at the international level to better prevent and treat these diseases. This includes utilizing knowledge resources such as the Atlas of Human Infectious Diseases (AHID). The information contained in AHID has been structured and summarized in previous research in the form of a web-based dictionary, but there is currently no visualization representing the similarities between diseases. Disease similarity analysis is important in understanding the pathogenesis of complex diseases, early prevention, diagnosis of major diseases, and even the development of new drugs. Based on the AHID data, which contains biomedical text, this study applied the Biomedical Text Semantic Sentence Similarity Estimation System (BIOSSES) method to measure disease similarity using attributes such as epidemiology, clinical findings, agent, transmission, incubation period, and diagnostic tests. This method achieved a Pearson Correlation Coefficient (PCC) value of 0.836 in previous research. However, the model produced a low text similarity score with a PCC value of 0.3630 and a Median Absolute Deviation (MAD) of 0.1158, indicating underfitting. The results of the similarity score measurement is presented in the form of a human disease network and are available on the web application alongside the AHID Dictionary.
AB - Over the past 30 years, more than 30 emerging infectious diseases (EIDs) have surfaced globally, necessitating coordinated efforts at the international level to better prevent and treat these diseases. This includes utilizing knowledge resources such as the Atlas of Human Infectious Diseases (AHID). The information contained in AHID has been structured and summarized in previous research in the form of a web-based dictionary, but there is currently no visualization representing the similarities between diseases. Disease similarity analysis is important in understanding the pathogenesis of complex diseases, early prevention, diagnosis of major diseases, and even the development of new drugs. Based on the AHID data, which contains biomedical text, this study applied the Biomedical Text Semantic Sentence Similarity Estimation System (BIOSSES) method to measure disease similarity using attributes such as epidemiology, clinical findings, agent, transmission, incubation period, and diagnostic tests. This method achieved a Pearson Correlation Coefficient (PCC) value of 0.836 in previous research. However, the model produced a low text similarity score with a PCC value of 0.3630 and a Median Absolute Deviation (MAD) of 0.1158, indicating underfitting. The results of the similarity score measurement is presented in the form of a human disease network and are available on the web application alongside the AHID Dictionary.
KW - AHID
KW - BIOSSES
KW - Biomedical text
KW - Semantic sim ilarity
UR - http://www.scopus.com/inward/record.url?scp=85210525040&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE63424.2024.10729987
DO - 10.1109/ICITISEE63424.2024.10729987
M3 - Conference contribution
AN - SCOPUS:85210525040
T3 - 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
SP - 262
EP - 267
BT - 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
Y2 - 29 August 2024 through 30 August 2024
ER -
