CAPÍTULO 1: APROXIMACIONES TEÓRICO CONCEPTUALES
1.2. A NTECEDENTES TEÓRICOS
1.2.1. Ocupación, apropiación e informalidad urbana
In an analogy to human fingerprints and DNA fingerprints, DSF visualizations form unique disease fingerprint patterns, enabling quick visual inspection of the disease state and raw measurement data at several levels of abstraction. In the DSF, patterns emerge from a tree of nodes rendered according to the DSI organization, using shapes and colours to quickly identify the patient’s disease state. The DSF is a visual counterpart of the DSI method intended to make reading of the original measurement data and analysis results quick and easy. It allows domain experts to see at a glance how the DSI values were computed and to determine which data are more important than others for the question at hand. The DSF also makes it possible to build a generic data analysis platform for visualizing disease state progression interactively in a CDSS. This section shows – based on Publica- tions I–III – how DSF visualizations are derived from the DSI results.
4.4.1 Colours
The DSF uses a gradient of colours from blue to white to red, indicating increasing DSI values, as shown in Figure 12. The choice of colours produces a heat map, in which cold (blue) colours indicate similarity to healthy controls and hot (red) col- ours similarity to disease state. Although colours resembling traffic lights were considered, ranging from good (green) to neutral (yellow) to poor (red), their use was avoided due to the difficulties they would create for colour-blind people.
Figure 12. Different DSI values are indicated using colours.
4.4.2 Node sizes
The DSF uses size to indicate relevance. The larger the node, the more relevant it is. To compare relevancies accurately, there needs to be a reference to compare node sizes to, or, more simply, the numeric relevance value can be shown to users when necessary. By default, nodes with a relevance of zero are not shown. A custom threshold could also be selected, hiding nodes that are less relevant
than the selected threshold. When observing many features simultaneously, sibling nodes in the tree hierarchy are organized in order of relevance, as shown in Figure 13.
Figure 13. Node sizes indicate differences in relevance. Siblings are sorted ac- cording to decreasing relevance. The tree shows features from an MRI processing method grouped by the regions of the brain from which they are derived.
4.4.3 Combining nodes in a hierarchy as the DSF
The combination of DSI and relevance values within a tree hierarchy captures the essence of patient data in relation to the studied disease. DSI values rendered as shades of red indicate which patient data are similar to the positive population in the training data, and the size specifies how relevant that information is based on previously diagnosed cases. A large DSI value and high relevance for a neuropsy- chological test, for example, indicate that the patient performed similarly to a known AD population and that the test discriminates between healthy and AD patients with good accuracy. This is visualized in the DSF as a large red node that is easy to notice. On the other hand, a test with a large DSI value but little or no relevance may often be ignored, since the test is unable to differentiate between the control and positive populations. Accordingly, these kinds of features are very small or even hidden in the DSF visualization. Figure 14 illustrates how individual points of data are combined in the DSF visualization to form a comprehensive picture for evaluating the disease state.
Figure 14. At the top is a DSF visualization of patient data with a large share of measurement values indicating early AD. The computation of the DSI for an MRI variable is depicted at the bottom. Adapted from Publication I with permission from IOS Press © 2011 IOS Press.
In Figure 14, the names of the tests and their DSI values (or raw measurement values in the case of leaf nodes) are shown next to each node. DSI values are indicated by both colours and numbers, providing an overview of the disease state for the patient from any branch of the tree in relation to the training set. Red col- oured nodes with DSI values approaching one indicate similarity to early AD cas- es. Blue colour and DSI values close to zero indicate similarity to stable MCIs. The relevance of a test is indicated by the size of the node next to the test’s name. Not all nodes are fully expanded; collapsed nodes show the overall DSI value from that test section. Here, neuropsychological tests and MRI contribute most to the total disease index, indicated by the largest node sizes and red colour. Nodes in the tree hierarchy can be presented within a software tool such that they are inter- actively expanded and collapsed. This allows users of the DSF to see an overview of all the data and, when necessary, drill into each individual patient measure. Leaves of the tree show original raw patient data (actual test results), such as ‘Delayed Word Recall’, which is a task in a neuropsychological test, and ‘Total Volume of Hippocampi’, derived from structural brain MRI.
4.4.4 Longitudinal DSF visualizations
Data from multiple time points can be rapidly analysed with the DSI method. Feed- ing the longitudinal results to the DSF produces visualizations with a temporal component. The results of longitudinal DSF visualizations are shown in Figure 15. The left side of the figure shows DSFs in which the DSI values of the individual tests at different time points are shown. The total DSI values (the topmost rows of the DSFs) combine results from all the tests. The size of a box indicates how well a feature discriminates between control and positive cases. Again, colours indicate into which group the data fit best. The right side shows linear regression of the total DSI values (red dashed line with white circles). Black squares present the total DSI values of a patient. A vertical line indicates the age (on the x-axis) of the patient being studied.
Figure 15. Longitudinal DSF visualizations for two MCI patients. The rows of boxes show the disease state evaluated at approximately 6-month intervals. Reprinted from Publication III with permission from IOS Press © 2014 IOS Press.
4.4.5 Summary of the DSF visualizations
The DSF provides a quickly interpretable visual overview of the patient state, obtained from data-driven and evidence-based analysis of patient data. Using colours and shapes, it draws attention to the data that are most relevant, reducing the need to go over hundreds of data points individually. DSF clearly discloses the factors contributing to the results, highlights the relevant measures and, thus, supports application of clinical judgment. The DSF respects the requirements specified earlier in Section 4.2 and emphasizes interpretability. It supports scala- bility by allowing a huge number of raw data points to be visualized interactively with only a subset of data visible at any time. It can also provide detailed infor- mation of any individual measurement if needed. Longitudinal visualizations allow clinicians to objectively monitor a changing disease state, and they can also be used to visualize the effects of drug treatments on the progression of AD.