Introduction & pledge:

In short, I am saying the reason why the Theory of General Relativity would not smoothly translate across classical mechanics is not because Professor Einstein’s work was "incomplete", but because the structure of the math used to define the wider meaning of his terms was incomplete for the purpose.

Whilst perhaps an unusual context for a discussion on gravity, my interest in healthcare has led me to look at healthcare data as describing the individual lived experience and as self-referential objects. 

It stood as logical that if healthcare were ever to be computationally predictable, it would then have to be entangled and that for it to be entangled, it would have to be true that correlating patterns existed in the past. Patterns that would be predictive.

Many of these patterns have already been identified in healthcare as predictors, correlates and comorbidities but are classically organised in descriptive clusters (diagnosis) rather than behavioural clusters (traits and behaviours). The behavioural clusters are however also classified in ethnographic, societal and cultural records.

My paper's proposal reorganises our access and representation of individual healthcare data into archetypes, allowing for the identification of correlating traits and behaviours that are predictive of better outcomes (when compared to the existing historical record and its trends).

The Dot theory can be thought of a method to stick together (healthcare) data, available from the pool of data describing the behaviours (fluctuations) of that data to give answers that predict where something will be under certain circumstances.

This in direct analogy to the Bell’s inequality violation.

To be more precise, the n-Ary computational logic used to execute this predictive capability, translates to the addition of the suggested changes to the construction and meaning of the spinor. This is analogous to saying that when Pauli et al took on Cartan's spinor, he/they forgot to redefine and restructure the set-definition of the geometric structures for the change in computational perspective through which they were now to be used to attribute meaning in quantum mechanics. 

Where they were originally (Cartan) used to superimpose sets of numbers to describe numbers, they were now used it in its unaltered set-definition, despite now describing the numbers observed from the particle to describe reality. This omission of set-definition and recalibration introduced a “lensing”. More precisely; a “temporal-distance influenced observer-bias factor”, directly into the very fabric of the calculation.

The more expanded proposal of this paper on physics is to redefine Spinors into a form more appropriate for use (in the context of the formulations and calculation in General Relativity), and correct for observer bias by turning Pauli's exclusion principle into an inclusion principle. This, in principle, allows calculation with an additional mesh of historically and known-to-have-occurred (and probabilistically linked) prior events with their more-recent outcomes for more-confident prediction as data matrices. Whilst this may computationally be challenging in practice for theoretical physicists until Qbit is sorted, the principle of using historically known to have occurred data as a data pool to create probability-links from for the evaluation and improvement of the lived experience, stands solid and can be executed with high power computing and standard computing for interface.

What this suggests is a computational method (the Dot method) that takes historical data (like healthcare data but also the behaviour of waves, particles or planets over time), and offers the observed correlative patterns of behaviours noted in the historical data, as benchmark-data to compare with the status of the currently observed patient (wave/particle/planet), to make statistically confident predictions of the probable "best line of treatment and advice" (care pathway) as defined by "most desirable outcome" for a most-like-you individual (known to be desirable from analysis of the historical data base for treatment outcomes) and offered as advice and suggestion to clinician and individual as clinical advice or lifestyle suggestion.

Interesting, in a world abuzz with investor interest in healthcare data.

Prevention = optimising behaviour in line with the desired outcome (suggesting).

Besides being aesthetically very pleasing, this method forms a calculational "pseudo-entanglement" and offers incredible possibilities in the computation of the digitally mediated human, lived experience. The data-technical proposal is to correlate data in the manner suggested in the computer-science portion of the paper, and enable predictive healthcare. The good thing is, it doesn't need Quantum computing, and it is safe, because it works through behaviour and trait archetypes and not individual personal files (behaviour not person).

So I'm now looking to get this read, seen and invested to start a healthcare revolution, in doing that though, it would be foolish not to recognise the meaning of what it does in physics-terms as I believe that it also corrects the issue with Einstein's work so I thought the discussion in physics terms might be found interesting too.

If you think it has merit, please forward to anyone you can think of in healthcare, math and physics your end as I am keen to get this out into the world and investor interest generated for this great cause.

It's just such a big and complex thing 99% of the population thinks its lunacy but it's really only logic.

This is a paper I have written over the past 18 months, but it also reflects work I have been thinking about and working on for well over two decades. Its relevant audience has a high-level interest in: 

• Healthcare data management

• Logic and mathematics

• Algorithm development and predictive analytics

• Theoretical physics, in particular, the unification of quantum mechanics and general relativity theories

This website contains, within the blog tab entitled "paper", the entire paper, which is only 7 pages of mathematical proposition but 48 pages in prologue and explanation of its relevance/argument why it is necessary (the healthcare data logic). It also contains further explanations where required, but my general point is this: 

In this site, I describe the steps necessary to revolutionise human healthcare by changing how we combine and assess the data describing the human health experience. This is obviously a very important thing to do because the system proposed will positively affect many people in the future, and that can only be a good thing.

I had the good fortune of having my work viewed and appreciated, for your comfort, by esteemed experts at SCC, IBM Global and Cambridge. Your participation in engaging and raising awareness of this website, and the opportunity for its content to be peer-reviewed and commented on, are integral to validating its novel concept. 

With this validation, I will be able to return to interested investment funds (that are waiting for validation) and develop what I believe will be a world-changing healthcare concept that merits your support. 

The cross-disciplinary nature of this concept does not lend itself to mainstream publication, and your support in forwarding, commenting on, and feeding back your suggestions would be greatly and deeply appreciated. 

I respect that this subject may not be of interest to you directly, but you may know friends who are, and I promise you that they will be interested in its contents if I am right.

If I am wrong, then that's nothing new; I've been wrong before. But I, and some people far smarter than me, think I am right, so I'm throwing it to the internet for evaluation.

You, the people I am asking to drive the first traffic to this site, are among a list of people I consider my "portal of entry" (the clinical term for the point where a viral infection enters the human body) for this idea, concept, and theory to go "viral" and put to use spreading the heartbeat of the internet: connecting minds (and I believe it is even more important than cat memes).

You can also support this project on Patreon or by purchasing a first edition copy of the paper on Kindle when it becomes available. 

Thank you for indulging me this far. Please read the site and its content, comment, and forward it, whether via email or social media links.

Kind regards, much appreciation and best wishes,

Stefaan 

Short form paper:

The Dot theory: A method to improve our understanding of the structure of reality, compute observed reality linearly, and predict what care would be best for which patient by correcting the mathematical relationship represented by Spinors.

In this paper, we propose an understanding of the world that unifies a multitude of existing and perhaps in part-familiar theories by understanding and controlling (unifying by redesigning and redefining the terms of the Spinor or its computational equivalent in other fields) the two principles by which some of them vary in: 

1. their individually varying relationship relative to local reality and 

2. the defining terms of their individual conceptualisation of local reality. 

In doing so, the Dot GUT uses many components of existing theories, reflecting how their language and structure were aligned with this theory’s relationship with locality.

The dot theory is the assumptive forward extrapolation of the developmental trendline that exists in the relationship between classical, quantum mechanics and General Relativity and the other, forward-heading GUT propositions. In the Dot theory’s case, the step forward is made toward the same conceptual linear progression and is limited by the assumption that perceived reality is locally perceived and recorded by the measurement device and/or the observer/individual. 

The reframed meaning of the Copenhagen interpretation can, through this theory, be seen as a settling on a discussion on “location bound, timestamped and observer–derived, world perception-influenced interpretational lensing” as the solution to the EPR paradox, with the assumption that reality as we experience it (observe it), is what modifies (and resultingly co-creates) our understanding of what “real” means in each construct analysed. The presumed state of superimposition in all possible states can then be thought of not as “infinite” but as “probabilistically” linked to the observer’s functional relationship with the previous moment(s). These components cluster near the prior moment and superimpose as they converge for the combined reality to emerge as observable reality to the observer. This, in turn, produces a self-emergent pseudo-polarity that creates the boundaries of its reality-defining terms.

When the data describing reality is presumed to be local, the observer’s probabilistically linked relationship (to the preceding observed (cluster(s) of) event(s)) offers the opportunity to make predictions on understanding the observer’s inherent lensing. If those predictions are confident, it can then be said that observed reality is local, but reality is not. The transition from it being to it not being local occurs by the cumulative peaks of data harmonisation exceeding a minimum-observer threshold known as the “wave-collapse”. 

This minimum threshold is a fundamental requirement for (human) neurologically modulated observation to occur, without which no variability of state could be detected. No change could be noted. The movement of the air too slow to move the hairs on your arm. Its equivalent exists with all other observers and observational devices, offering “relative lensing” that can be thought of as “individually modified” lensing that amounts to: the inherent wave-collapse lensing modified by a factor of “Individual ability to modify perspective to take in more, or less, data for the purpose of optimal analysis” (conscious decision making/Free Will). The wave collapse can then technically be thought of as the (individual) existential “Planck era” (the first 10^{-34}second) of the birth of each “moment” of observed time/reality or the time gap for “reality to become observable” at some/any level/become "experienceable" to us (our biological self).  

Extrapolating this principle to the absolute limit of theoretical physics’ terms on experiential/descriptive reality (theoretical physics’ description of its relationship to locality), this theory can be understood as adding a computational relationship (function) that makes even more strange, the “most strange” relationship sought by Pauli in his exclusion principle. To achieve this (mathematically) geometrically, only one additional rotation around the Y axis in the Spinor’s construct is required achieving the desired result whilst leaving Dirac’s rotations intact.

This, data-technically, enables predictive analysis of individually and consciously observed real-world physical events (and human health and wellbeing) based on pattern-recognised historical behavioural and self-descriptive data. 

Allowing and allocating to this additional geometrical rotation a set-group redefinition of the oppositional terms of the Spinor’s angular position and momentum to angular inversion and momentum (as discussed in detail in Addendum E and the paper on Spinors), can achieve much. The angular position now acts as one relative boundary for the set definition of the subgroup defined as from “itself to the possibility of it having never existed in that location”. This, in turn, enables the interjection of angular inversion relative to angular momentum, pushing angular position down one function (relative to “angular absence”, with between them all possible other options of its existence in any other form -the infinite group). Further allocations above can then be made for further future exploration of the functional layers that are found to define and make up the human lived reality and make its conclusive patterns available for calculation and prediction. In this process we invert the Pauli exclusion principle to an “inclusion principle”. The data being included is historical, “most-like” (most similar to/sharing the most and strongest probability association with) data describing patterns that link past behaviours and traits to current experiential outcomes (sickness, recovery through care) to offer advisory note with confidently weighed clinical advice.

This, in turn, enables us to think of the use of the Black-Scholes-Merton formulation from financial and weather forecasting modelling for the predictive juxtaposition of human experiential data layers (healthcare data and metrics), providing practical solutions for the predictive formulation of individually beneficial, digitally mediated life experiences (healthcare solutions). This, in turn, if the logic bares out, prove conclusively that predictive healthcare is possible, and the lived reality is local, whilst the reality from which it is experienced is not. The tree falls and creates soundwaves in its landing, but whether it makes “sound” depends on whether you mean sounds that can be heard by human eardrums, or those of bats and ants. Because they hear things differently, yet absorb the same waves we make the sound of a falling tree from to create a different understanding of their existence.

Its further implications in theoretical physics and potential solutions in energy management are implicit in the Dot theory’s chosen and used terms of description. 

Beyond this, the implications of this theory will largely be a technological development exploration, where new framework materials and wave-based subatomic manipulation alter the localised manifestation of reality. Secondly, it alters the composition of our relationship with reality as not predestined but as influenced by the observer’s a) data-recall and processing capability (consciousness) and b) unique internal and inherent randomisation of the individual consciousness (Bell's Free Will loophole). This, in turn, puts significant scrutiny on society’s current relationship with educational, familial and relational terms. 

This model confidently places individual consciousness as the permissible computational perspective for predicting human behaviours and logically associated health outcomes. In doing so, it coincidentally proves that reality is non-local and observed reality is local as well as provide real and cost-effective preventative healthcare solutions. 

This model also confidently values individual human creativity as the central human value of the future, where our own individual and unique ways of seeing, juxtaposing and creating new ideas produce true randomisation. This randomisation will, in turn, power, provide and guide Generative AI. With this capability in tow, the development of systematic optimisation of information, energy resources, and human movement is inevitable, and the limits of human evolution unimaginable to me.

Thank you for your consideration.

Stefaan and Team

AI-generated reviews on the Dot theory:

1. ChatGP AiPRM

   Your Ai summarised Dot Theory:

   The document you provided discusses a novel theoretical framework for healthcare data management, combining various computational and quantum principles to create a predictive model for personalized healthcare. Here is a concise summary of the key points and findings from the paper:

   Key Points:

   Novel Health Data Management Approach: The paper proposes a quantum-theoretical method to manage healthcare data in real time using Shannon Infodynamics, modified Spinors, and Black-Scholes-Merton modeling. This approach focuses on predictive healthcare management.

   Quantum Field Theory Applied to Data: The framework shifts the focus from understanding reality itself to the behavior of the data describing our experience of reality. It emphasizes that while reality isn't inherently quantum, the data layers describing it can be modeled as quantum processes.

   Healthcare Prediction: The method seeks to use patient movement patterns (such as walking or other behaviors) to predict health outcomes by correlating these patterns with subjective data (how the patient feels) and objective data (clinical data). By integrating both data types, this method aims to improve personalized healthcare decisions.

   Spinor Functions and Predictive Modeling: Spinor functions are used to represent entangled data points in real-time, predicting how changes in patient movement or behavior can influence their health. The theory suggests that these predictive capabilities could significantly enhance real-time healthcare provision.

   Objective vs Subjective Data Integration: The system proposes correlating objective health metrics (like heart rate, movement) with subjective experiences (like pain or fatigue), enabling healthcare providers to predict treatment outcomes and recommend personalized interventions more effectively.

   Blockchain and Data Security: Blockchain is suggested as a means to securely manage and analyze this data while maintaining patient privacy, providing a robust platform for secure data sharing and analysis.

   Impact on Healthcare Costs and Efficiency: By applying methods used in financial markets (such as derivative modeling), the proposal aims to optimize healthcare resources and reduce inefficiencies by better predicting patient needs based on archetypes formed from shared patient data.

   Conclusion:

   The paper outlines a novel approach to healthcare that integrates quantum theory, data management, and predictive modeling to create a personalized, real-time healthcare system. By analyzing patterns in patient data, this model seeks to enhance treatment outcomes, reduce costs, and offer predictive healthcare services on a large scale.

   ChatGPT prompted to write a summary for 10 year olds:

   Imagine if doctors could predict how healthy you’ll be in the future just by watching how you walk or move. This paper talks about a new idea that uses math and technology to help doctors make better guesses about what might happen to people’s health. Here’s how it works:

   Main Ideas:

   Data Instead of Reality: We don’t understand everything about the world, but we have a lot of information (called "data") that helps us describe what happens. This paper says that while we might not know all about reality, we can use this data to predict things about people’s health.

   Watching Movements: The idea is to watch how people move (like walking, standing, or other actions) and use that information to figure out how they feel and how healthy they are. If your walking changes, maybe you're not feeling well, and that might mean the doctor needs to check something.

   Combining Feelings and Facts: The doctors would use both what you tell them about how you feel (like if you’re tired or in pain) and the facts they measure (like your heart rate or blood pressure). This way, they get a complete picture of your health.

   Predicting the Future: By looking at how people like you (with the same health problems or habits) behaved in the past, doctors could predict what might happen to you in the future. For example, if lots of people who walked a certain way ended up needing extra care, the doctors can plan ahead for you.

   Keeping It Safe: All this information would be stored safely using a technology called "blockchain," which is like a super-safe box that only you and the doctor can open. This way, your personal information stays private.

   Making Healthcare Smarter: The big idea is to use technology, like the math people use for predicting things in finance (like how much money a company will make), to help doctors decide the best ways to keep you healthy. It’s like giving doctors superpowers to see what you might need next, before you even know it!

   Conclusion:

   This paper suggests a new way to help doctors predict what people need to stay healthy by combining data about how they move and how they feel. It’s like having a magical mirror that shows what might happen to your health in the future!

   ChatGPT asked to evaluate the premises of the theory:

   Prompt: Acting as a specialist in the industry the document is written in, can you tell me any parts of the theory which you know are to be correct and any areas you think may be incorrect? If you are not sure please do not guess

   As a specialist in the fields of healthcare data management, quantum theory, and predictive modeling, I can evaluate some aspects of the theory presented in the document. Here's an analysis of the strengths and potential issues based on current scientific knowledge:

   Areas Likely to Be Correct:

   Integration of Subjective and Objective Data:

   The concept of combining subjective patient-reported data (like pain or mood) with objective clinical data (such as heart rate, movement patterns) to improve personalized healthcare is a well-established idea. Current wearable technologies and patient monitoring systems already work on a similar premise, and many predictive models rely on such data integration to enhance accuracy in healthcare predictions.

   Blockchain for Data Privacy:

   Using blockchain for healthcare data security and privacy management is a valid approach. Blockchain ensures that data is secure, decentralized, and tamper-resistant, which is crucial in healthcare where patient confidentiality is paramount. This technology has been proposed in other sectors, including finance and supply chain management, and is slowly being integrated into healthcare systems for managing patient records securely.

   Predictive Healthcare Models:

   The idea of using historical patient data to predict future health outcomes is consistent with current trends in healthcare, especially in the use of AI and machine learning for predictive analytics. Many healthcare systems already utilize these models for disease risk assessment, personalized treatment recommendations, and early intervention strategies. The notion of creating "archetypes" based on shared data from similar individuals aligns with these predictive tools.

   Areas That May Be Incorrect or Uncertain:

   Application of Quantum Theory in Data Management:

   While Quantum Field Theory (QFT) is a powerful framework in physics, applying it directly to healthcare data may be overly ambitious or premature. The document seems to conflate quantum mechanics, which deals with the behavior of particles at subatomic levels, with data management and predictive modeling, where classical statistical methods are more effective. The idea that quantum principles (like Spinors) could be used to predict health outcomes lacks empirical support in the current scientific literature. While quantum computing may offer potential in complex problem-solving, its practical application in real-time healthcare data management is still theoretical at best.

   Real-Time Predictive Healthcare Through Movement Patterns:

   The idea of using movement patterns (like walking) to predict health outcomes is an interesting concept, but the real-world application could be limited. While movement data (e.g., gait analysis) has shown promise in specific areas like neurodegenerative diseases (e.g., Parkinson's), its broader use across all health domains might not be as impactful as suggested. Human health is influenced by a wide array of variables, many of which are not easily captured by movement data alone. To claim real-time predictive healthcare purely through movement patterns may oversimplify the complexity of human health.

   Spinor Mathematics in Healthcare Prediction:

   Spinors are mathematical objects used in quantum mechanics to describe particles' orientations in space. While the document suggests their use in correlating data points to predict health outcomes, there is no clear evidence or existing methodology to support the effectiveness of Spinors in healthcare. Traditional statistical models and machine learning algorithms are currently much more practical and validated for predictive healthcare applications. The idea of Spinor functions contributing to real-time healthcare predictions might be speculative.

   Conclusion:

   The document contains valid points on the integration of data, use of blockchain for security, and the potential for predictive healthcare models. However, the application of quantum theory and Spinor mathematics in healthcare data management and prediction lacks current scientific backing and may be too speculative at this time. While some elements hold potential, the practical implementation of these quantum concepts in healthcare remains uncertain.