Repurposing digitised clinical narratives to discover prognostic factors and predict survival in patients with advanced cancer

Electronic medical records (EMR) represent a rich informatics resource that remains largely unexploited for improving healthcare outcomes. Here we report a systematic text mining analysis of EMR correspondence for 4791 cancer patients treated between 2001 and 2017. Meaningful groups of text descriptors correlating with poor survival outcomes were systematically identified, and applying machine learning analysis to clinical text accurately predicted cancer patient survival at selected timepoints up to 12 months. In a validation cohort of 726 patients, inclusion of EMR descriptors to machine learning models outperformed the predictivity of conventional clinical symptom scores by 4.9% (p = 0.001). These results prove that labour-intensive EMR data collection can be repurposed to add clinical value. Extension of this approach to a broader spectrum of digital health data should transform the real-time utility of such latent informatics resources, enabling healthcare systems to be more adaptive and responsive to patient circumstances.