Sunday, January 13, 2013

Visual Analytics for Clinical Decision Making

In my last post, I talked about the era of Big Data in medicine, Evidence-Based Practice (EBP),  Practice-Based Evidence (PBE), and the need for a human-centered approach to building intelligent health IT (iHIT) systems. In this post, I discuss Visual Analytics, an emerging discipline in Data Science. In a report titled "Illuminating the Path: The R&D Agenda for Visual Analytics" published in 2004 by the National Visualization and Analytics Center (NVAC), Visual Analytics is defined as "the science of analytical reasoning facilitated by visual interactive interfaces."

The goal of Visual Analytics is to obtain deep insight for effective understanding, reasoning, and decision making through the visual exploration of massive, complex, and often ambiguous data. As a multidisciplinary field, Visual Analytics combines several disciplines such as human perception and cognition, interactive graphic design, statistical computing, data mining, spatio-temporal data analysis, and even art.

In his book titled "Beautiful Evidence", Edward Tufte illustrates the fundamental principles of analytical design by using Charles Minard's famous map known as "Carte figurative des pertes successives en hommes de l'Armée Française dans la campagne de Russie 1812-1813" (Figurative Map of the successive losses in men of the French Army in the Russian Campaign 1812-1813). The map is a dramatic account of the heavy losses of the french army during Napoleon's Russian campaign of 1812. Edward Tuffe calls the map the "best statistical graphics ever". Click on the image below to enlarge it.

Visual Analytics is also an emerging discipline in healthcare informatics. For example, similar to Minard's map of the Russian Campaign of 1812-1813, Visual Analytics can help in comparing different interventions and care pathways and their respective clinical outcomes over a certain period of time through the vivid showing of causes, variables, comparisons, and explanations. This approach contrasts with the traditional display of clinical data in table rows that is so common in electronic health record (EHR) systems interfaces.

Another Visual Analytics technique called Visual Cluster Analysis can be particularly helpful in Comparative Effectiveness in clinical care settings where the goal is to compare the benefits and harms of different interventions for different subgroups (groups of patients sharing similar clinical characteristics such as age, gender, race, genetic profile, and comorbidities). Given a specific patient, Visual Cluster Analysis can help the clinician visually explore what works and what doesn't work for "similar patients".

You can find interesting examples of research projects and implementations in the proceedings of the Visual Analytics in Healthcare Workshop which has been held in conjunction with the IEEE VisWeek for the past three years. The 2013 Visual Analytics in Healthcare Summit (VHAC 2013) will be held in conjunction with the AMIA 2013 conference in Washington DC. There are a number of open source toolkits that can be used to implement Visual Analytics. Some of them are based on open web standards such as HTML5, CSS3, SVG, and Javascript. My favorite is D3.js. DC.js and Crossfilter are built on top of D3.js and facilitate the creation of interactive visualization of multivariate datasets in the browser.

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