TY - GEN
T1 - Detection of drug-associated rhabdomyolysis through data mining techniques
AU - Kunakorntham, Patratorn
AU - Pattanaprateep, Oraluck
AU - Dejthevaporn, Charungthai
AU - Thammasudjarit, Ratchainant
AU - Thakkinstian, Ammarin
N1 - Publisher Copyright:
Copyright © JCSSE 2020 - 17th International Joint Conf. on Computer Science and Software Engineering.
PY - 2020/11/4
Y1 - 2020/11/4
N2 - Rhabdomyolysis (RM) is a life-threatening adverse drug reaction (ADR). Statins are the common drugs causing RM and weakness as well as a combination with other drugs, which increase the level of statins. The estimated cost per QALY of supportive treatment for RM was 69,742.50 USD per year, but the early detection of ADRs can reduce the cost of approximately 1,400.00 USD per patient. RM has a problem of under-reporting in spontaneous reporting systems (SRSs). Detecting RM at the early stage is the crucial task by finding the relationship between drugs and RM from electronic health records (EHRs). The aim of this study is to propose a predictive model for RM analysis by predicting the probability of RM or weakness in patients who used statin alone or combined with other drugs. The proposed model can predict the probability occurrence of RM or weakness with sensitivity equal to 0.66.
AB - Rhabdomyolysis (RM) is a life-threatening adverse drug reaction (ADR). Statins are the common drugs causing RM and weakness as well as a combination with other drugs, which increase the level of statins. The estimated cost per QALY of supportive treatment for RM was 69,742.50 USD per year, but the early detection of ADRs can reduce the cost of approximately 1,400.00 USD per patient. RM has a problem of under-reporting in spontaneous reporting systems (SRSs). Detecting RM at the early stage is the crucial task by finding the relationship between drugs and RM from electronic health records (EHRs). The aim of this study is to propose a predictive model for RM analysis by predicting the probability of RM or weakness in patients who used statin alone or combined with other drugs. The proposed model can predict the probability occurrence of RM or weakness with sensitivity equal to 0.66.
KW - Data mining
KW - Drug interaction statin
KW - Machine learning
KW - RM
KW - Rhabdomyolysis
KW - Statin
KW - Statin-induced RM
UR - https://www.scopus.com/pages/publications/85098516718
U2 - 10.1109/JCSSE49651.2020.9268229
DO - 10.1109/JCSSE49651.2020.9268229
M3 - Conference contribution
AN - SCOPUS:85098516718
T3 - JCSSE 2020 - 17th International Joint Conference on Computer Science and Software Engineering
SP - 86
EP - 91
BT - JCSSE 2020 - 17th International Joint Conference on Computer Science and Software Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Joint Conference on Computer Science and Software Engineering, JCSSE 2020
Y2 - 4 November 2020 through 6 November 2020
ER -