Investigators: Ms Shima Ghassem Pour (CI & PhD student), Prof Louisa Jorm, Prof Anthony Maeder.

Due to the chronic nature of prostate cancer it is difficult to understand the journey of patients who are at an advanced stage. The project aims were to find an information solution that can integrate multiple information elements and relate these to prostate cancer aetiology, risk and progression.

Outcomes resulting from this research:

Ghassempour S, Girosi F, Maeder A. Clustering multivariate time series using Hidden Markov Models. Int J Environ Res Public Health [Internet] 2014;11(3):2741-63. doi: 10.3390/ijerph110302741

Pour SG, Maeder A, Jorm L. “Constructing a Synthetic Longitudinal Health Dataset for Data Mining.” The Fourth International Conference on Advances in Databases, Knowledge and Data Applications; IARIA. 29 Feb–5 Mar 2010. Saint Gilles, Reunion Island.

Pour SG, Maeder A,  Jorm L. “Validating Synthetic Health Datasets for Longitudinal Clustering”. In Proc. Health Informatics and Knowledge Management 2013 (HIKM 2013) Adelaide, Australia. CRPIT, 142. Gray, K. and Koronios, A. Eds., ACS. 15-20

Ghassem Pour S, McLeod P, Verma B, Maeder A. “Comparing data mining with ensemble classification of breast cancer masses in digital mammograms”. In S Khanna, A Sattar & D Hansen (eds.) Proceedings of the Second Australian Workshop on Artificial Intelligence in Health (AIH 2012), CEUR-WS, Sun SITE Central Europe operated under the umbrella of RWTH Aachen University, Aachen, Germany.