TY - GEN
T1 - An Innovative Big Temporal Data Analytics Technique over Real-Life Healthcare Datasets
T2 - 32nd Italian Symposium on Advanced Database Systems, SEBD 2024
AU - Cuzzocrea, Alfredo
AU - de Bock, Geertruida H.
AU - Maas, Willemijn J.
AU - Soufargi, Selim
AU - Hafsaoui, Abderraouf
N1 - Publisher Copyright:
© 2024 Copyright for this paper by its authors.
PY - 2024
Y1 - 2024
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 - https://www.scopus.com/pages/publications/85202073455
M3 - Conference contribution
AN - SCOPUS:85202073455
T3 - CEUR Workshop Proceedings
SP - 112
EP - 121
BT - Proceedings of the 32nd Symposium on Advanced Database Systems
A2 - Atzori, Maurizio
A2 - Ciaccia, Paolo
A2 - Ceci, Michelangelo
A2 - Mandreoli, Federica
A2 - Malerba, Donato
A2 - Sanguinetti, Manuela
A2 - Pellicani, Antonio
A2 - Motta, Federico
Y2 - 23 June 2024 through 26 June 2024
ER -