Over the past two decades there has been an explosion in the use of digital footprints to monitor and predict human behaviours. The source of data used for this purpose is our online use of the internet, the emails we send and transactions we make.
Analysis of these footprints through machine-learning techniques (MLT) has been exploited in the public domain by government and business to predict behaviours and inform investment decisions. In research, MLT have also been used to analyse gene expression data and for medical imaging analysis. However to date, there has been little exploration of these methodologies in the clinical setting. We hypothesised that MLT may offer a paradigm shift in clinical medicine that can address core issues with large and complex data sets. These techniques offer the potential to derive adaptive systems from diverse data sets, discover latent connections between data items and to predict outcomes.
In this presentation, we will discuss the use of MLT for cancer outcome predictions using electronic administrative records, purpose built cancer registries and genomic data and its potential for use in the clinical setting and for quality monitoring.