## 2 Materials and Methods
### Applications
#### 2.5.1 Decision Support System for Pulmonary Embolism Diagnosis and Follow-Up  
The modeling of pulmonary embolism was done during the PhD thesis of [PERSON]. [PERSON] [52]. A FVSOOMM software platform implementation for this application was developed and tested. [PERSON] is a cardiologist and served as the domain expert. The initial work on the design was done in [53]. Figure 13: CBR: Distance is the difference of areas of \(f_{A}\) and \(f_{B}\). Clinical Context  
Pulmonary embolism (PE) is the most severe manifestation of venous thromboembolism disease. It is defined as the obstruction of the lung arteries, or their branches, by embolisms. This is mainly caused from deep vein thrombosis (DVT) when blood clots, formed in the lower limbs, travel to the heart. The main objective of this application was to construct a clinical prediction indicator of PE with FVSOOMM. The objective of developing this indicator, which is a temporal vector space, was to improve the clinical diagnostic capabilities of PE by considering uncertainties, inaccuracies, and clinical disease progression, in order to improve the recognition and management of PE before confirmation by computed tomographic pulmonary angiography (CTPA). The advantage of using FVSOOMM is that it relies on expert knowledge and the European Society of Cardiology (ESC) [54] recommendations on the management of PE that have been validated by learned societies and experts in the field. Graphics Summary of the PE Application  
A graphical summary, depicting the different steps of the method used to develop the PE application, is illustrated in Figure 14.  
Figure 14: The graphical summary of pulmonary embolism modeling with FVSOOMM. Variable Selection  
The analysis of the PE management recommendations and scores allowed the selection and classification of variables. Three types of variables were distinguished in the diagnosis of PE: static variables and medical history that are quasi-permanent or permanent; evolutionary variables with low variability; and dynamic variables with a high and quick variation. They are reproduced, with kind permission from [PERSON]'s thesis [52], in Table 2.  
The UML schema of disease progression is shown in Figures 15 and 16, which define the clinical course of a typical pulmonary embolism. There is information on the following:  
- Patient: age is the parameter used in the modelling of a risk probability score;  
- History: thromboembolism risk factors, cancer and hospitalization history;  
- Clinical symptoms and parameters: hemoptysis, unilateral leg pain, increased calf volume, heart rate and systolic blood pressure;  
- Syndromes: phlebitis syndrome consisting of unilateral lower limb pain and increase in lower limb volume, and hypotension syndrome consisting of systolic blood pressure \(<\)90 mmHg, and/or blood pressure variation \(>\)40 mmHg over 15 min. The presence of one of the two syndromes is sufficient to consider the presence of a pulmonary embolism until confirmation or invalidation by CTPA. In the case of pulmonary embolism, there are no pathognomonic or obligatory clinical signs, but there are suggestive and accessory signs that make diagnosis difficult. Moreover, it is a pathology that is very progressive in the short term, hence the interest of taking time into account in the development of a risk probability score. \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable Type** & **Variable Names** \\ \hline \multirow{4}{*}{Static variables} & Age \\  & Thromboembolic \\  & Predisposed factors \\  & Cancer \\  & Surgery or long-time bedrest \\ \hline \multirow{4}{*}{Evolutionary variables} & Hemoptysis \\  & Unilateral lower limb pain \\  & Unilateral increase in calf volume \\ \hline \multirow{4}{*}{Dynamic variables} & Heart rate \\  & Systolic blood pressure \\  & Systolic blood pressure variability \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of variables used in the FVSOOM model for PE. Figure 16: Clinical picture evolution object-oriented model. Figure 15: The UML disease diagram.