Fisheye Visualization and Multi-Path Trees for Presenting Clinical Practice Guidelines: Methods and Application to Covid-19

Decision trees are commonly used for representing medical reasoning, both in structured representations for automatic reasoning and in visual presentations for clinicians. However, in the latter purpose, decision trees often suffer from two problems: (a) some reasoning processes are not well suited for trees, e.g. when considering several independent followup elements, (b) the size of the tree is limited by the screen. Here, we propose the dynamic and interactive visualization of decision trees. To solve problem (
$a$
), we used multi-path decision trees, allowing the selection of multiple choices at some nodes, which greatly reduces the size of the tree in some situations. We provide algorithms for one-click navigation in a multi-path tree, i.e. the user may click on any node of the tree at any time. For problem (b), we used the fisheye technique for reducing the visual space devoted to the inaccessible part of the tree. In addition, we represented the tree internally in a formal ontology, making it machine-interpretable and permitting the automatic execution of parts of the tree. We applied this approach in a clinical decision support system related to the management of Covid-19, and we showed the resulting user interface to 6 clinicians.