**1. Introduction**

For decades, efforts have been made to endow computers with knowledge, i.e. valuable information, to support them in solving problems or needs in different domains that directly or indirectly affect humans. *Domain knowledge* has proven to be an essential component for human expertise, so intelligent software tools are only as good as they are programmed to be. Certainly, there are significant advances that allow *machine learning* tools to model, define rules, instances, and knowledge from the information they receive. These tools improve accuracy, adapting to domain-specific characteristics and sometimes extending their capabilities set out in their original programming. The results obtained with these tools have been promising, and applications are emerging every day in a wide variety of domains with certain types of intelligence. However, the challenge for the development of such intelligent solutions goes beyond the understanding and application of these intelligent technologies. The main challenge lies in understanding the domain and the ability to gain domain knowledge, which can be used as *a priori knowledge* for the training of many tools of *machine learning*. Therefore, *domain knowledge* is key to establishing the requirements for the development of these nowadays apps. This is most relevant when the construction of an intelligent solution is linked to an *Informal Structured Domains* (ISD) [1].

An ISD has the following characteristics:

1.The presence of one or more domain specialists who influence the domain from their particular backgrounds, perspectives, interests, and expectations, bringing large amounts of wealth knowledge. Most of this knowledge is tacit, informal, and implicit; hence, only partial domain knowledge is formal, and therefore easily available. Moreover, this knowledge is often nonhomogeneous, with varying degrees of specificity, and is also incomplete. In essence, knowledge in ISD depends on the domain specialists' experience in the domain.


In the medical domain literature, it is common to find different ways of doing knowledge management, as in ref. [2], and these different ways are due to the fact that often the characteristics of a medical ISD require particular treatment. Due to these characteristics, extracting truly useful information or knowledge from an ISD is a complex task and remains a relevant challenge, and requires the use of unconventional methodologies for their knowledge management. In particular, the characteristics of the medical domain case study presented in this chapter fit an ISD, where there is not enough *real a priori knowledge* to train powerful machine learning tools, and also contains in a particular way, unlike other medical ISDs, very short and repeated serial measurements with blocking factor. Some machine learning tools trained with very little *real a priori knowledge* or even using synthetic information could be employed and although the results could become congruent they cannot be valid for the medical domain due to intrinsic liability, so they could only be illustrative. On the other hand, by employing statistical tools, it is possible to obtain valid results. However, the knowledge that is lost can completely change the outcome of using a given medical therapy.

Of most of the current state-of-the-art architectures, only a few explore the frequency domain to extract useful information, identify time series patterns, and improve results. Many methods are based on a classification using deep learning models from Fourier analysis [3], a predictive method such as the autoregression model [4], or hidden Markov models [5]. However, these approaches, which are mainly based on a mathematical expression of the models, often do not provide an intuitive explanation of the results and are often difficult for humans or even machines to interpret. Even as they are based on general time series models that make use of many observations, they cannot deal with cases where there are few observations per series. In fact, when the number of observations per series is minimal, it is not possible to obtain convenient estimates from these models.

At the same time, some artificial intelligence approaches need a large amount of *real a priori knowledge* about the structure of the series or about the time patterns to be discovered [6, 7]. Unfortunately, this knowledge is often not available in real cases. From this point of view, a challenge that will always be interesting is to achieve the best possible characterization of the domain in which one is working. It is important to keep in mind that not all ISD will have a machine learning solution that is currently in fashion. If the real objective is to perform optimal knowledge management, sometimes it will be necessary to design a particular way to do it, as they say, *a tailor-made suit*.

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

The Knowledge Discovery Serial Measurements (KDSM) [8] is a hybrid methodology that uses machine learning and statistical techniques for the knowledge management and the benefit of using KDSM, in this particular study case where efficient handling of repeated measurements is desired as well as reliable knowledge management, is because it is the only methodology that is adapted to these particular characteristics to extract useful information, and that can be represented as *crisp rules*. KDSM also makes it possible to describe some behaviors to improve a medical therapy or treatment. In addition, the fact of being open allows it to adapt to the specific characteristics of the domain to be treated by assimilating tools to do so. However, it does not mean that it is the only way because each ISD challenges the knowledge manager to offer the optimal solution, and it is here when it can be said that it is an art to be able to do it. As it can be observed, knowledge management of a medical ISD with these characteristics is a complex task and remains a major challenge. Therefore, it is pertinent to communicate achievements and results of hybrid methodologies, AI, and Statistics, that treat medical ISD such as the one performed with the KDSM methodology. The results presented in this chapter illustrate why it is important to be open to this type of methodologies, as they have changed the paradigm of how to perform knowledge management for certain medical ISD; in addition, they motivate to continue working in this line of research.

The structure of the chapter is as follows: first, Section 2 summarizes the scope of the informally structured domains in which the KDSM methodology has been successfully used; its effects are also mentioned. Section 3 presents a formal description of the circumstances under which KDSM, using a combination of statistics and machine learning, can act as an optimal approach to obtain good knowledge results from patterns over repeated measurements in a very short time series with a blocking factor. In Section 4, a typical situation from an ISD in the medical field is shown, this section also briefly describes the knowledge management process, through KDSM, of the electroconvulsive therapy (ECT) domain: data characteristics used in the analysis, medical domain data is discussed through the KDSM phases, and results and conclusions derived from them. Section 5 reports the results of using the KDSM methodology in the situation described in the previous section. Finally, in Section 6 conclusions on the results and some interesting points for future work are mentioned.

### **2. Convenient knowledge management using KDSM**

Today, every advanced medical institution is embedded in the digital era where the exponential growth of information and its management by data science methods is a complex process ranging from descriptive statistics to pattern recognition through machine learning and visualization techniques to support the interpretation of data to extract useful information from it. For acceptable knowledge management, it must be corroborated that the domain is really understood, and consequently, be able to establish how to work with the data, including data cleansing. This is essential as there is certainly useful hidden information that can be distinguished as patterns or converted to rules or to the most convenient representation for the manager to analyze the results obtained. In medicine, knowledge management must bridge the gap between data analysis and medical decision support to make medical work more efficient and effective. In addition, there are several medical domains whose characteristics make them complex because there is no structure with data and knowledge organized in a way that supports adequate knowledge management. For these reasons, making decisions about what is and what is not good knowledge in these domains is always a very difficult problem.

Such domains are included in what is referred to in [1] as Informal Structure Domains (ISD). The situation to be presented in Section 4 fits the definition of an ISD. Moreover, it presents some additional particularities that will determine its further analysis, such as the inclusion of short and serially repeated measurements over time, heterogeneous data describing objects that can be quantitative or qualitative where the second ones usually have a significant number of modalities, and declarative knowledge is available concerning relationships between attributes, classification targets. Therefore, finding a consistent way to manage these very short measures of observations, to extract useful information from them, and to be able to find profiles of this type of data linked to the rest of the patients' characteristics and those of their treatment is complex for both statistics and machine learning, especially if no *a priori knowledge* is available.

Very short, repeated serial measurements are very few records made of the same attribute at a given time [9, 10]. Such records have the following characteristics:


Very short, repeated, serial measures are very often found in the domain of medicine (e.g. successive periods of illness and recovery under different treatment regimens). In many instances, medical research protocols, in humans, aim to measure the evolution of a disease or the performance of the treatment of a condition. This is done by collecting a set of serial measures of an attribute (e.g. symptoms), or some of them, over a period of treatment. The result of this process is a database containing one or more serial measures (one for each attribute of interest) for each patient included in the study. Thus, as mentioned above, several serial measures are available for a given attribute, and it is not correct to consider them all together. One of the most common ways to analyze serial measures of a continuous variable in a set of patients is to follow the proposal of Schober and Vetter [11]: (a) simplify sets of serial measures of records of the attribute in the observed entity to a single serial measure using some mathematical function (mean, area under the curve … ) and then (b) analyze the simplification using standard univariate methods. In addition, sometimes the structure of the records that make up the serial measures includes a blocking factor or the number of these measures is too small to use classical time series analysis.

Although the study reviewed in this chapter is from the psychiatric domain, the use of the KDSM approach is very feasible for any ISD in which a set of individuals is presented, and there are a variable number of occurrences of an event for each individual, all at different instances of time, and the measurement of an attribute to study its evolution—relative to the current individual—in a later time frame just after the occurrence of the event is of utmost importance. The attribute is measured a small fixed number of times, the same for all cases, and thus results in repeated and very short serial measurements (see **Figure 1**) for which each individual is a blocking factor. Basically, there are many medical circumstances for which it is necessary to record serial measurements of body functions, after repeated stimuli, of patients participating in a specific study to follow the effects of their therapy and their reaction to it. For example, changes in lung function are measured after repeated applications of a bronchodilator, serial tissue perfusion, or gut function measurements. Moreover, it should be noted that such situations are not exclusive

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

**Figure 1.** *Y measurements for all* i *after each occurrence of E.*

to medical environments; in fact, repeated serial measurements of very short duration are often encountered, for example, in economic situations where many economic indicators have to be measured periodically especially after an unexpected event. Or even volcanology, when in an attempt to better understand volcanoes and improve disaster prevention programs, volcanoes are subjected to a special indicator monitoring system during critical periods when events such as seismic movements or emissions of gases and other substances or both are recorded.
