*4.4.1 BLA phase*

Baseline serial measurement records were analyzed for the simple visual (e5) and auditory (e6), visual and categorization (e7), visual and auditory, and categorization (e8) tests. To classify these patient reaction times, the hierarchical clustering technique was used, as these represent the baseline condition of the patients (using the hierarchical reciprocal neighbors method with Ward's aggregation criterion and Euclidean distance). Subsequently, those patient characteristics that are relevant to the partition chosen by the domain specialist are determined in order to proceed to derive rules. The classification technique suggested partitions into 2, 3, 4, and 5 classes that group different patients according to their baseline reaction times. According to the domain specialist's experience, he/she chose a cut-off into three classes (C1, C2, and C3, see **Table 6**).

The characteristics of each class were then analyzed with the information obtained from the classification. **Figure 8** shows the general trend of the baseline RT records for each class, i.e. it shows the initial conditions of the patients with respect to RT. It is possible to observe a consistent response of reaction times in


### **Table 6.**

*The table shows a partial view of the arrangement of patient records in the three-cluster partition corresponding to the hierarchical cluster. The clustering technique indicates the varying baseline conditions of the patients.*

*Toward Optimization of Medical Therapies with a Little Help from Knowledge Management DOI: http://dx.doi.org/10.5772/intechopen.101987*

**Figure 8.** *BLA phase, VAC plot showing the variability between classes regarding the basal TRs.*

classes C1 (blue), C2 (orange), and C3 (gray) having essentially the same level for all tests. In all tests, both simple e5, e6, complex e7, and e8, the reaction times of classes C2 and C3 show a noticeable increase compared to class C1.

As previously noted, patient-specific information is available in the *X* matrix (102 attributes), and this information is used to find those attributes that identify patterns that clearly characterize the selected partition. Quantitative and qualitative attributes that are statistically significant for the partition are distinguished from patient characteristics using the non-parametric H-test and the *χ*2-test, both (*ρ*<0*:*05). In addition, multiple boxes and bar charts were used to visualize the distribution of the data and how these attributes of the *X* matrix interpret the chosen 3-class partition.

The information obtained from the tests and graphs was reviewed by the domain specialist to determine which attributes he/she was interested in observing, including some that were not statistically significant. Subsequently, after attribute selection and personal interpretation by the domain specialist, rules (expert knowledge) are derived from the graphs. These are crisp rules, representing all the attributes selected by the specialist, e.g. height, weight, number of cigarettes per day … They also constitute the initial and partial knowledge base (*KB*) of this domain.

**Figure 9** shows the distribution of the age attribute in the 3-class partition. It can be seen that the BLA C1 class includes all baseline records of the youngest patients (up to 29 years) and the BLA C2 and BLA C3 classes include baseline records of patients older than 29 years. The attribute age was the first of the 40 statistically significant attributes to be selected based on the specialist's extensive experience and the fact that this attribute is one of the attributes that exerts a strong influence on psychophysiological testing [20] consistent with clinical evidence. Therefore, three classes of patients are described: younger patients (BLA C1) with smaller and regular baseline RT for all tests (e5-e8) and younger (BLA C2) and mature (BLA C3) patients with longer baseline RT, in particular with much longer times in the

### **Figure 9.**

*The distribution of the attribute age in each class and partition stands out for its clear delimitation and symmetry. This attribute is relevant because it clearly differentiates the reaction times of younger patients from those who are older. Here it can be seen that the younger patients are in BLA class 1, with a smaller baseline RT than both BLA class 2 and the mature patients in BLA class 3.*

complex tests (e7 and e8) than in the simple ones (e5 and e6). The next step is to integrate the 103 rules from the *KB* knowledge base, obtained from the selected significant attributes (**Table 7**). The specialist determined it appropriate to include this knowledge for the EEA phase, with separate processes for *younger*, *young,* and *mature patients*.

### *4.4.2 EEA phase*

To study the effect of each *ES* on reaction time and due to both the fact that the data in the matrix *Y* are ill-conditioned for classical statistical hypothesis testing, and the existing blocking factor in this matrix, this factor was treated. The treatment to remove the blocking factor consisted of transforming the *Y* matrix into a new matrix, but without the blocking factor. The transformation was carried out by performing the differences between *post-ES* and *pre-ES*. Then, on the transformed matrix, the rule-based hierarchical clustering technique (RBHC) [8] is applied.


*b Extracted rules relating to the attribute.*

### **Table 7.**

*This table shows an abstract of crisp rules contained in the KB. Specifically, the rules for the age attribute.*

## *Toward Optimization of Medical Therapies with a Little Help from Knowledge Management DOI: http://dx.doi.org/10.5772/intechopen.101987*

Thus, the RBHC based on the rules contained in the *KB* suggests six classes of effect curves for each *ES* applied to the reaction times. The differences, in three of these classes, from *pre-ES* to *post-ES* are notable to slightly positive. In addition, they show a slightly more uniform pattern, where one class corresponds to younger patients, another to younger patients, and the last to mature patients. This means that reaction times are slower after *ES* application and would therefore be considered poor reactions. The remaining three classes present a more variable pattern corresponding to patients with faster reaction times after *ES* application. The differences between before and after an *ES* are negative, i.e. they are good reactions (see **Figures 10**–**12**) and there is a uniform trend in the EEA classes C1, C3, and C5 for an increase in RTs. Therefore, there is a tendency for patients in these classes to deteriorate. In contrast, in EEA classes C2, C4, and C6, a variable pattern is visualized in which the effect tends to decrease the RTs, i.e. a tendency toward the improvement of patients in these classes.
