Data visualisation in biomedical research
The rapid increase in volume and complexity of modern biomedical data has outpaced currently available analysis methods. As a result, many potential discoveries remain buried in data already collected; worse, many errors in clinical diagnosis remain unrecognised1, contributing to a major cause of death2,3.
Garvan's BioVis Centre is working to help address these challenges, by fostering changes in research and clinical practices. These changes include adopting new, automated analysis methods (e.g., machine learning). However, automated analysis alone is not sufficient4 - we also need to improve the methods used for visual analysis of biomedical data5,6.
Currently, data visualisation is often rate-limiting in biomedicine. For example, current rates of misdiagnosis1 can be reduced by improving how data are visualised7. Driven by this realisation, data visualisation is a major research focus in computer science, creating many resources that could accelerate discovery in science and medicine8-11.
Co-located within a world-leading medical research institute, the BioVis Centre focuses on applying these advanced data visualisation methods to address cutting-edge research challenges. As part of this mission, the Centre develops and maintains a range of widely-used tools & services, and also creates scientific animations designed to educate and inspire the public about key biomedical breakthroughs.
1Graber et al. (2005). Diagnostic error in internal medicine. Archives of Internal Medicine 165.
2Kohn et al. (1999). To err is human: Building a safer health system, National Academies Press.
3Naghavi, et al. (2015). A systematic analysis for the Global Burden of Disease Study, Lancet 385.
4Anscombe (1973), Graphs in statistical analysis, American Statistician 27.
5O'Donoghue et al.(2018), Visualising biomedical data, Annual Review of Biomedical Data Science 1.
6O'Donoghue & Procter (2017), Data visualisation isn’t just for communication, it’s also a research tool, The Conversation.
7Craft et al.(2015). An assessment of visualization tools for patient monitoring and medical decision making. Systems and Information Engineering Design Symposium.
8Card et al.(1999), Readings in information visualization: Using vision to think, Morgan Kaufmann.
9Tufte (2009), The visual display of quantitative information. Graphics Press.
10Evanko (2013), Data visualization: a view of every Points of View column. Springer Nature.
11Munzner (2014), Visualization analysis and design. CRC Press.