TY - GEN
T1 - F-TBDA
T2 - 2023 IEEE International Conference on Big Data, BigData 2023
AU - Cuzzocrea, Alfredo
AU - De Bock, Geertruida H.
AU - Maas, Willemijn J.
AU - Soufargi, Selim
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we introduce and experimentally assess an innovative big data analytics technique for mining and analyzing Quality-of-Life Indicators (QoL) over time among patients with lung cancer and treated with immunotherapy. In more details, given datasets of QoL indicators collected over time, at regular intervals, the F-TBDA technique (Frequency-based Temporal Big Data Analytics) computes temporal relative frequency tables over fixed-time intervals where data of subsequent observations (i.e., intermediate therapy) are compared with the baseline observation (i.e., starting therapy). Then, on the basis of these relative frequency tables, both simple and complex frequency-based big data analytics tools are developed, in order to unveil hidden patterns over cancer patient therapies. Experimental results on top of a real-life dataset nicely complete the theoretical contributions we provide in our research.
AB - In this paper, we introduce and experimentally assess an innovative big data analytics technique for mining and analyzing Quality-of-Life Indicators (QoL) over time among patients with lung cancer and treated with immunotherapy. In more details, given datasets of QoL indicators collected over time, at regular intervals, the F-TBDA technique (Frequency-based Temporal Big Data Analytics) computes temporal relative frequency tables over fixed-time intervals where data of subsequent observations (i.e., intermediate therapy) are compared with the baseline observation (i.e., starting therapy). Then, on the basis of these relative frequency tables, both simple and complex frequency-based big data analytics tools are developed, in order to unveil hidden patterns over cancer patient therapies. Experimental results on top of a real-life dataset nicely complete the theoretical contributions we provide in our research.
KW - Big healthcare data analytics
KW - Cancer patient data analytics
KW - Clustering
KW - Frequency-based big data analytics tools
KW - Quality of life indicators
KW - Temporal relative frequency tables
UR - http://www.scopus.com/inward/record.url?scp=85184986374&partnerID=8YFLogxK
U2 - 10.1109/BigData59044.2023.10386767
DO - 10.1109/BigData59044.2023.10386767
M3 - Conference contribution
AN - SCOPUS:85184986374
T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
SP - 5197
EP - 5205
BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
A2 - He, Jingrui
A2 - Palpanas, Themis
A2 - Hu, Xiaohua
A2 - Cuzzocrea, Alfredo
A2 - Dou, Dejing
A2 - Slezak, Dominik
A2 - Wang, Wei
A2 - Gruca, Aleksandra
A2 - Lin, Jerry Chun-Wei
A2 - Agrawal, Rakesh
PB - IEEE
Y2 - 15 December 2023 through 18 December 2023
ER -