Difference between revisions of "Encrypted code: Ephaptic transmission"

In the chapter '[[1° Clinical case: Hemimasticatory spasm - en|1st Clinical case: Hemimasticatory spasm]]' we immediately reached a conclusion bypassing all the cognitive, clinical and scientific process which underlies the diagnostic definition but it is not that simple otherwise our poor patient Mary Poppins would not have had to wait 10 years for the correct diagnosis.<blockquote>It should be emphasized that it is not a question of negligence on the part of clinicians rather of the complexity of 'biological complex systems' and above all of a mindset still anchored to a 'classical probability' which categorizes healthy and diseased phenotypes according to symptoms and signs sampled clinicians instead of probing the 'State' of the system in the temporal evolution. This concept, anticipated in the chapter '[[Logic of medical language: Introduction to quantum-like probability in the masticatory system]]' and in '[[Conclusions on the status quo in the logic of medical language regarding the masticatory system]]' has laid the foundations for a medical language more articulated and less deterministic, mainly focused on the 'State' of the 'Mesoscopic System' whose purpose is, essentially, to decrypt the message in machine language generated by the Central Nervous System as we will assist in the description of other clinical cases that will be reported in the next Masticationpedia chapters. </blockquote>This model, which we propose with the term 'Cognitive Neural Network' abbreviated as 'CNN' is a dynamic cognitive intellectual process of the clinician who interrogates the network for self-training. The 'CNN' is not a 'Machine Learning' because while the latter must be trained by the clinician, with statistical and prediction adjustments, the 'CNN' trains the clinician or rather directs the clinician to the diagnosis while always being questioned following a logical human, hence the term 'cognitive'.

In fact, some classic machine learning models, whose training in the laboratory gives positive results, fail when applied to the real context. This is typically attributed to a mismatch between the datasets the machine was trained with and the data it encounters in the real world. A practical example of this can be represented by the conflict of assertions encountered in the diagnostic process of our patient Mary Poppins between the dental and neurological context which only the support of the coherence demarcator <math>\tau</math>(cognitive process) managed to solve.

One of the limits of machine learning, therefore, is known as "data shift",<ref>Jérôme Dockès, Gaël Varoquaux, Jean-Baptiste Poline. Preventing dataset shift from breaking machine-learning biomarkers.GigaScience, Volume 10, Issue 9, September 2021, giab055,</ref> or "data movement" and another underlying cause of the failure of some models outside the laboratory, is the "subspecification"<ref>Alexander D’Amour et al. Underspecification Presents Challenges for Credibility in Modern Machine Learning. Journal of Machine Learning Research 23 (2022) 1-61,Submitted 11/20; Revised 12/21; Published 08/22</ref><ref>Damien Teney, Maxime Peyrard, Ehsan Abbasnejad. Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning.ECCV 2022: Computer Vision – ECCV 2022 pp 458–476Cite as</ref> so much so that the attempt to build an algorithm-enhanced electronic medical record (EMR) system designed specifically for use in a cancer center, was a notable failure at an estimated cost of $39,000,000 USD. This effort was a 2012 partnership between M.D. Anderson Partners and IBM Watson in Houston, Texas.<ref>Herper M. MD Anderson benches IBM Watson in setback for artificial intelligence in medicine. Forbes. 2017 February 19. [Ref list]</ref> Early promotional news describing the project stated that the plan was to combine genetic data, pathology reports with doctors' notes and relevant journal articles to help doctors come up with diagnoses and treatments. However, five years later, in February 2017, M.D. Anderson announced that he had closed the project because, after several years of trying, he hadn't produced a tool for use with patients that was ready to move beyond pilot testing.{{q2|Fascinating and provocative, explain to me in detail|... the model is essentially simple in its cognitive complexity}}

In essence, the encrypted machine language message sent out by the Central Nervous System in the 10 years of illness of our patient Mary Poppins was interpreted through verbal language as Orofacial Pain from Temporomandibular Disorders'. We have remarked several times, however, that human verbal language is distorted by vagueness and ambiguity therefore, not being a formal language, such as mathematical language, it can generate diagnostic errors. The message in machine language sent out by the Central Nervous System to be searched for is not pain (pain is a verbal language) but the anomaly of 'System State' in which the organism was in that time period. Hence the shift from the semiotics of the symptom and the clinical sign to the '[[System logic|System Logic]]' which, through 'Systems Theory' models, quantify the system's responses to incoming stimuli, even in healthy subjects.

All this is replicated in the proposed 'CNN' model by dividing the process into incoming triggers (Input) and outgoing data (Output) to then be reiterated in a loop managed cognitively by the clinician up to the generation of a single node useful for the definitive diagnosis. The model basically breaks down as follows:

* '''Input:''' By incoming trigger we mean the cognitive process that the clinician implements as a function of the considerations received from previous statements, as has been pointed out in the chapters concerning the 'Medical language logic'. In our case, through the 'Consistency Demarcator <math>\tau</math>, the neurological context was defined as suitable instead of the dental one pursuing a clinical diagnostic explanation of TMDs. This trigger is of essential importance because it allows the clinician to center the network analysis initiation command which will connect a large sample of data corresponding to the set trigger. To this essential initial command, as an algorithmic decryption key, is added the last closing command which is equally important as it depends on the intuition of the clinician who will consider the decryption process finished. In Figure 1, the structure of the 'CNN' is represented in which the difference between the more common neural network structures in which the first stage is structured with a high number of input variables can be noted. In our 'CNN' the first stage corresponds only to a node and precisely to the network analysis initialization command called 'Consistency Demarcator <math>\tau</math>', the subsequent loops of the network, which allow the clinician to terminate or to reiterate the network, (1st loop open, 2st loop open,...... nst loop open) are decisive for concluding the decryption process ( Decrypted Code ). This step will be explained in more detail later in the chapter.