IEML in Global Medical Communication

What is IEML ?

IEML – Information Economy Meta Language – is a mathematical language with the same expressive power as a natural language. It has a dictionary of 3000 concepts plus a regular grammar allowing to build recursively an infinity of sentences representing concepts and relations.

1. IEML has the same expressive power as a natural language. In particular, it allows the construction of complex recursive sentences that may be used for reasoning and complex causal modelling. It can explain itself (IEML is its own metalanguage), and it can translate into other natural and specialized languages. 
2. IEML is mathematical, which means that there are functions (algorithms) for the creation and recognition of concepts networks. You don’t create the nodes and the links of IEML semantic networks one by one, but you create them using algebraic functions. In addition, IEML semantics are computable: IEML sequences of phonemes (or chains of characters) self-decode into networks of concepts and these concepts and their relations can be read in natural languages.

Properties 1 and 2 make IEML the most advanced language for symbolic AI. IEML has been designed to be used as a universal metadata system, providing semantic interoperability at the conceptual level.

An Application in Healthcare

The main idea of the GMC (global medical communication) application is to use the growing availability of medical data, the power of artificial intelligence, and the principles of evidence based medicine in order to improve healthcare collective intelligence at a global level.

The whole system will respect the data exchange standard FHIR, and the personal data will be protected by following the SOLID specifications. The artificial intelligence will be hybrid, combining a carefully crafted semantic metadata system (or ontology, or knowledge graph) in IEML and the best techniques of machine learning.

The IEML semantic metadata system will make a distinct conceptual organization for risk factors, symptoms, biomarkers and disorders/treatments in order to make the machine learning findings as relevant as possible. The conceptual breakdown and structure of the overall ontology will be designed in such a way as to accommodate the different classification systems in use.

Examples of conceptual organization of symptoms (related to psychic functions) in the field of mental health

All concepts, presented here in English, have been constructed from the words of the IEML dictionary and following IEML regular grammatical rules. In GMC, the concepts will be readable in all major languages. They will be hypertextually related to their definitions, all the way down to the concepts of the dictionary.

Table 1 below organizes the major categories of psychic functions (and related symptoms) in mental health.

I used the fundamental symmetries of IEML to order the universe of discourse of mental health symptoms. The top row corresponds to the emergent qualities of the psyche in its most virtual dimension (autonomy) while the bottom row corresponds to the most actual dimension, which concretely supports the other psychic functions (memory). Between these two dimensions, the ascending and descending information is coded and decoded on a subjective (sign), emotional (being) and cognitive (thing) mode, as we see in the three intermediate rows. The three columns of the table correspond roughly to a sign / being / thing partition and the binary subdivision within each of the fifteen rectangles corresponds to a division between the most virtual aspects (for the top cell) and the most actual (for the bottom cell).

Note, in the row « subjectivation » that the subject relates to himself in the first box, to the alter-ego in the second, and to reality in the third. The « thought » represents the way the subject relates to reality, hence its affinity with the thing. On the other hand, in the third box of the « emotion » row, mood is the emotion felt and affect is the emotion expressed.

The following tables show some examples of further conceptual subdivisions. All this ontology building is based on extensive reading and conversations with experts of the field.