## 2 Literature Review
### State of the Art
More recent proposals feature Expert System for Poisoning (ESP), which is a rule-based expert system for poisoning diagnosis and management that takes into account signs and symptoms of patients, coming up with a list of possible poisoning types [22]. In addition, novel techniques have been applied in medical diagnosis with promising results. Artificial immune systems (AISs) emerged in the 1990s as a new branch of artificial intelligence. This proposal is inspired by the human immune system behavior, comprising features such as recognition, memory acquisition, resource competition, diversity or self-tuning. Artificial immune systems have been successfully applied to heart, chest or thyroid disease diagnosis [23,24,25]. In addition, statistical methods, diverse training algorithms or techniques such evolutionary computation are used in conjunction with these systems in order to tune parameters and enhance results. Almost all of these above systems perform a binary classification or are among a reduced amount of classes, which differs from our approach. As already stated, most efforts in literature focus on specific diseases and a little attention has been paid recently to prior diagnosis support in primary health care. Furthermore, previously mentioned systems work with datasets including variables extracted from medical imaging and tomography, heart-valve sounds or symptoms and signs, but only a few take into account outcomes from laboratory tests. Despite that fact, there are some systems which focus on laboratory test's outcomes for performing a more general diagnosis between larger typology of diseases. [PERSON] and [PERSON] [26] proposed a framework for eliciting knowledge for a medical laboratory diagnostic expert system in order to construct a set of production rules which can interpret medical laboratory tests. They have implemented a fuzzy rule-based system prototype working with blood sugar and urine tests, although was not described how the system provides diagnosis advice neither how broad the coverage of diseases is, as they focused on knowledge's elicitation process. [PERSON] and [PERSON] [27] developed the Laboratory Advisory System (LAS). LAS is an expert system working with production rules and pattern-consequence relationships. This expert system provides recommendations and interpretations of test results, which assists clinicians with test selection and results interpretation of such laboratory tests and makes the adherence to established guidelines easier. A trial was accomplished and results showed that clinicians using the system performed a more accurate diagnosis, required fewer tests and needed less time to diagnose a patient and start a treatment. These all led to a cutting of costs and better quality care and outcomes from patients [11]. Promising results of previous systems and lack of large datasets containing high amounts of laboratory test parameters and their related diagnoses led us to the development of a system which incorporates experts' knowledge, instead of performing machine learning methods requiring a dataset. A rule-based system is selected owing to its ease and their closeness with human clinicians' reasoning regarding laboratory tests' results. Thus, we have selected a rule-based approach regarding the possibility of incorporating experts' knowledge, explanatory ability of these systems and suitability of this approach in relation with interpretation of laboratory tests' results. Besides, there are drawbacks when learning machine approaches are intended to be used, like the inexistence of accessible databases containing laboratory parameters values and their respective precise diagnosis or lack of initiatives in Spain, which provide access to clinical information repositories.