Implementation of the DSI and DSF in the PredictAD tool

library, called the PredictAD tool, were developed in parallel with the DSI and DSF methods. The goal of the PredictAD tool was to provide clinical decision support in the early diagnosis of AD using the DSI and DSF methods.

The development of all the software was done in the C# language using Mi- crosoft .NET Framework 3.5 or later [Hejlsberg et al. 2006]. User interface compo- nents were implemented with Windows Presentation Foundation (WPF) 3.5 or later. The PredictAD tool and the interactive implementations of the DSI and DSF methods were evaluated with clinical partners several times in an iterative devel- opment process. The following sections describe the PredictAD decision support tool, based on Publications II, V and VI.

4.5.1 Software library implementing the DSI and DSF methods

A proprietary software library implementing the DSI and DSF methods provides an application programming interface (API) for managing data, computing DSI values and visualizing the results interactively with the DSF (see Figure 16). It is a stand- alone library applicable to several domains in addition to early diagnosis of AD.

The library provides an abstraction for data repositories as persistence stores (see (a) in Figure 16) that allow receiving data from multiple data sources. The underlying data source can be virtually anything, a database, a web service or simply a set of data files on a disk. A data definition layer(b) is used for describing entries (the types of tests done on a patient) and features (the types of raw meas- urement values within entries). Definitions are application-specific meta-data and are configured in source code or by XML (Extensible Markup Language) when initializing the library for use. The organization of the DSI tree hierarchy is also described within this layer. The actual data are read from the persistence stores into another layer(c), where all the entities (e.g. patients), entries and feature val- ues are represented by object instances.

To perform DSI computations, the library needs to know how to select control and positive cases from the training data. For this, a rule-based grouping system(d) was developed. A CDSS using the API is responsible for defining the grouping rules, e.g., “if diagnosis equals AD, assign patient to positive group AD”. After applying grouping rules, control and positive cases in the training data are known to the library(e). Next, using a configurable sampling system(f), the library selects particular data from training cases as the training data for the DSI method. Soft- ware tools were created for interactive creation and modification of the grouping and sampling rules systems.

Finally, having sampled the training data(g), the library uses them together with the patient measurements(c) and the feature hierarchy(b) to evaluate the DSI(h). All data are first organized according to the tree hierarchy, and a disease model is

trained. Fitnesses, relevancies and DSI values are then computed recursively to obtain a total DSI value from all the available patient data.

To visualize the data and results from applying the DSI method to the user, the library provides graphical user interface components for interactively displaying and manipulating DSI trees(i), data distributions(j), entry timelines(k) and entry de- tails(l). If a user wishes to examine the DSI or relevance values of any tree node in more detail, clicking on the node provides more information in the form of data distributions. Any test or measurement node can also be omitted from the DSI tree interactively. This can be a useful feature if the user considers certain results unreliable or wants to test different hypotheses.

Figure 16. Overview of the architecture of a library implementing the DSI and DSF methods showing the main directions of data flow. Reproduced from Publication II with permission from Institute of Electrical and Electronics Engineers © 2011 IEEE.

4.5.2 PredictAD tool – a CDSS for early diagnosis of AD

The PredictAD tool integrates heterogeneous data such as imaging biomarkers, CSF biomarkers and results from neuropsychological tests for compact visualiza- tion within an interactive user interface. The reason for building the PredictAD tool was to investigate whether it – by using the DSI and DSF methods – can assist physicians in the early diagnosis of AD. The hypothesis was that physicians inter- acting with the software could predict conversion from MCI to AD better than with- out using the tool. This would allow some patients to be diagnosed earlier than they are currently, making possible earlier delivery of treatments and better selec- tion of subjects in pharmacological trials.

The tool was developed iteratively with clinicians. In the first prototype, basic design and architecture was put in place. In the second prototype, the user inter- face was improved and new features implemented. The third prototype was used for validation with clinicians. There also exists a more recent prototype version of the PredictAD tool, which will be used in future studies at several memory clinics. See Figure 17 for an overview of the tool evolution over these prototypes.

Figure 17. Evolution of the PredictAD tool research prototypes. Starting from top left in clockwise order: prototypes 1, 2 and 3, and the current prototype.

The PredictAD tool provides an overview screen from which all patient data can be easily accessed. The overview screen contains basic demographic information and a timeline of the tests and measurements performed on the patient. In the latest versions, an interactive implementation of the DSF is also visible on the overview screen, showing data analysis results from the DSI. These are provided by the software library implementation described in Section 4.5.1.

4.5.3 Summary of the PredictAD tool

The PredictAD tool was developed as a means to validate the DSI and DSF meth- ods clinically. The implementation of the application was also a central deliverable for the EU-funded PredictAD project. The design and development work to build the PredictAD tool was a software engineering project whose detailed description is outside the scope of this thesis. Nevertheless, the work resulted in a CDSS that could be installed in end-user environments for validation by clinicians, forming a crucial part of this thesis work.