TY - GEN
T1 - New Cancer Treatment Evaluation through Big Data Analytics
AU - Li, Gangmin
AU - Gu, Jianze
AU - Bai, Xuming
N1 - Funding Information:
ACKNOWLEDGMENT This research project was supported by the fund provided by the Research Institute of Big Data Analytics (RIBDA), Xi’an Jiaotong-Liverpool University and The 2nd Affiliated Hospital of Suzhou University.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/2
Y1 - 2019/1/2
N2 - Cancer plays a leading role in causing morbidity and mortality worldwide. Several treatments have been developed and practiced for fighting against cancer. Totally Implantable Venous Access Port Drug Supply (TIVAPDS) treatment is a new method utilizing Totally Implantable Venous Access Port (TIVAP) delivery method, which is one kind of Intrathecal Drug Delivery System (IDD) with lower side effects, to increase patient's quality of life. This paper reports our study aiming to evaluate the effectiveness of TIVAPDS treatment in order to make contributions to generalize this treatment in China. Our data samples come from The Second Affiliated Hospital of Suzhou University, a forerunner of TIVAPDS practices in China and with patients' agreement. The data statistics summary results and the relationships between each two identified attributes are analyzed. Based on the results, 2 predictive models utilizing C4.5 decision tree and logistic regression algorithms are adopted for prediction. The results are used as reference to assess individual treatment cases, so that the effectiveness of the treatment can be achieved and if possible, to improve the efficiency of TIVAPDS treatment.
AB - Cancer plays a leading role in causing morbidity and mortality worldwide. Several treatments have been developed and practiced for fighting against cancer. Totally Implantable Venous Access Port Drug Supply (TIVAPDS) treatment is a new method utilizing Totally Implantable Venous Access Port (TIVAP) delivery method, which is one kind of Intrathecal Drug Delivery System (IDD) with lower side effects, to increase patient's quality of life. This paper reports our study aiming to evaluate the effectiveness of TIVAPDS treatment in order to make contributions to generalize this treatment in China. Our data samples come from The Second Affiliated Hospital of Suzhou University, a forerunner of TIVAPDS practices in China and with patients' agreement. The data statistics summary results and the relationships between each two identified attributes are analyzed. Based on the results, 2 predictive models utilizing C4.5 decision tree and logistic regression algorithms are adopted for prediction. The results are used as reference to assess individual treatment cases, so that the effectiveness of the treatment can be achieved and if possible, to improve the efficiency of TIVAPDS treatment.
KW - Big Data analysis
KW - C4.5 decision tree
KW - Effectiveness
KW - Length of stay (LOS)
KW - Logistic regression.
KW - Prognostic prediction model
KW - TIVAPDS
UR - http://www.scopus.com/inward/record.url?scp=85061508015&partnerID=8YFLogxK
U2 - 10.1109/ICSAI.2018.8599466
DO - 10.1109/ICSAI.2018.8599466
M3 - Conference Proceeding
AN - SCOPUS:85061508015
T3 - 2018 5th International Conference on Systems and Informatics, ICSAI 2018
SP - 484
EP - 489
BT - 2018 5th International Conference on Systems and Informatics, ICSAI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Systems and Informatics, ICSAI 2018
Y2 - 10 November 2018 through 12 November 2018
ER -
