Oral Presentation Australian Society for Medical Research Annual Scientific Meeting 2016

Precision Software for Complex Disease Management in the Age of Precision Medicine (#5)

Graham Radford-Smith 1
  1. Department of Gastroenterology and Hepatology, Royal Brisbane and Women’s Hospital, University of Queensland School of Medicine, QIMR Berghofer MRI, Brisbane, QLD, Australia

Personalized medicine is a term that is being increasingly utilized in the clinical and research environments. Its application is often linked to a number of “OMICS” platforms – including the use of GWAS and sequencing data – to define individual subjects’ pathology, disease risk, and response to treatment. This has applications in rare diseases but is now also being applied to some more common, complex diseases including malignancies and chronic diseases. With this increasing uptake of a Genomic Medicine approach, there is and will be increasing need for access to high level phenomic data. Extraction of structured information from electronic health records (EHRs) has a relatively long history but has been influenced by concerns around privacy. Several initiatives have been undertaken in the recent past to develop anonymized collections of EHRs, including ShARE/CLEF1, DeepPhe (http://cancerhealthnlp.org/) and PhenoMiner2. Although these approaches may provide access to “big data”, the populations are usually cross-sectional and retrospective.
In contrast, some investigators are driving the development of bespoke software for specific disorders which integrates with the EHR and provides prospective data on disease management and response to treatment using validated clinical instruments. One example of this is a clinical management software (CMS) initiative championed by the ANZ Inflammatory Bowel Disease Consortium in partnership with Crohn’s Colitis Cure (www.c-c-cure.org). My talk will use the development of this “CMS” as an example of “precision software” and how it will contribute to the further advancement of clinical phenomics in the age of Precision Medicine.

  1. Suominen H, et al. Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Berlin Heidelberg; Springer, 2013;212-31;
  2. Collier N, et al. Learning to recognize phenotype candidates in the autoimmune literature using SVM re-ranking. Plos One 2013;8:e72965.