**6. Conclusion**

As mentioned at the beginning of this chapter, the main challenge to carry out a proper Knowledge Management of an Informally Structured Domain, described in section §1, is based on both an excellent understanding of the domain and the ability to obtain knowledge from it. This action is key, to perform a suitably optimal intelligent data analysis or establish the knowledge requirements for the correct development of intelligent applications, or even for the proper representation of the knowledge of such domain. Since the knowledge management of an ISD is a complex task, but very often yields very interesting results, it is considered very relevant to communicate the use of the KDSM methodology and the results obtained through it, in a domain characterized as ISD, where very short and repeated serial measurements are presented, and there is not enough *a priori knowledge* to train machine learning tools. Moreover, as serial measurements are commonly found in a large amount of data from medical domains, it is doubly important to communicate successful KDSM practices in this type of domain, as this chapter aimed to do.

Thus, in this sense, the chapter reported on knowledge management related to certain attributes of interest to the domain specialist; in particular, the reaction time behavior of patients undergoing electroconvulsive therapy (section §2), at very specific times and where the characteristics of each patient constitute building blocks for serial measures. It is worth mentioning that thanks to KDSM a successful knowledge management of repeated and very short serial measurements of patients were achieved, as KDSM can be considered a special type of data management in the presence of a blocking factor. Consequently, some conclusions can be drawn: when conducting global analyses of situations that are characterized as ISD domains, the effect of using a summary of all serial measurements masks relevant information affecting the observed individual; that is, the use of the KDSM methodology, in domains where very short and repeated serial measurements with block factor are presented, have shown that the behavioral curves formed by serial measurements of an attribute of interest *Y* in each individual are neither inherent to the *Toward Optimization of Medical Therapies with a Little Help from Knowledge Management DOI: http://dx.doi.org/10.5772/intechopen.101987*

individual nor the global observation of all measurements after each event *E*. On the contrary, all individuals may react differently after each event *E*. As exemplified and communicated through **Table 9**.

Finally, the frequent presence of this type of domain poses the risk that an inadequate characterization of the domain, the routine use of summary measures … directly affect the performance and efficiency of the solution to a problem or satisfaction of a need that is intended to be satisfied through Knowledge Management.
