generative transformation

Q-UEL Toolkit for Medical Decision Making :- Science of Uncertainty and Probabilities

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Quantum Universal Exchange Language

Emergent | Interoperability | Knowledge Mining | Blockchain

Q-UEL

  1. It is a toolkit / framework
  2. Is an Algorithmic Language for constructing Complex System
  3. Results into a Inferential Statistical mechanism suitable for a highly complex system – “Hyperbolic Dirac Net”
  4. Involves an approach that is based on the premise that a Highly Complex System driven by the human social structures continuously strives to achieve a higher order in the entropic journey by continuos discerning the knowledge hidden in the system that is in continuum.
  5. A System in Continuum seeking Higher and Higher Order is a Generative System.
  6. A Generative System; Brings System itself as a Method to achieve Transformation. Similar is the case for National Learning Health System.
  7. A Generative System; as such is based on Distributed Autonomous Agents / Organization; achieving Syndication driven by Self Regulation or Swarming behavior.
  8. Essentially Q-UEL as a toolkit / framework algorithmically addresses interoperability, knowledge mining and blockchain; while driving the Healthcare Eco-system into Generative Transformation achieving higher nd higher orders in the National Learning Health System.
  9. It has capabilities to facilitate medical workflow, continuity of care, medical knowledge extraction and representation from vast large sets of structured and unstructured data, automating bio-statistical reasoning leading into large data driven evidence based medicine, that further leads into clinical decision support system including knowledge management and Artificial Intelligence; and public health and epidemiological analysis.

http://www.himss.org/achieving-national-learning-health-system

GENERATIVE SYSTEM :-

https://ingine.wordpress.com/2013/01/09/generative-transformation-system-is-the-method/

A Large Chaotic System driven by Human Social Structures has two contending ways.

a. Natural Selection – Adaptive – Darwinian – Natural Selection – Survival Of Fittest – Dominance

b. Self Regulation – Generative – Innovation – Diversity – Cambrian Explosion – Unique Peculiarities – Co Existence – Emergent

Accountable Care Organization (ACO) driven by Affordability Care Act transforms the present Healthcare System that is adaptive (competitive) into generative (collaborative / co-ordinated) to achieve inclusive success and partake in the savings achieved. This is a generative systemic response contrasting the functional and competitive response of an adaptive system.

Natural selection seems to have resulted in functional transformation, where adaptive is the mode; does not account for diversity.

Self Regulation – seems like is a systemic outcome due to integrative influence (ecosystem), responding to the system constraints. Accounts for rich diversity.

The observer learns generatively from the system constraints for the type of reflexive response required (Refer – Generative Grammar – Immune System – http://www.ncbi.nlm.nih.gov/pmc/articles/PMC554270/pdf/emboj00269-0006.pdf)

From the above observation, should the theory in self regulation seem more correct and that adheres to laws of nature, in which generative learning occurs. Then, the assertion is “method” is offered by the system itself. System’s ontology has an implicate knowledge of the processes required for transformation (David Bohm – Implicate Order)

For very large complex system,

System itself is the method – impetus is the “constraint”.

In the video below, the ability for the cells to creatively create the script is discussed which makes the case for self regulated and generative complex system in addition to complex adaptive system.

 

Further Notes on Q-UEL / HDN :-

  1. That brings Quantum Mechanics (QM) machinery to Medical Science.
  2. Is derived from Dirac Notation that helped in defining the framework for describing the QM. The resulting framework or language is Q-UEL and it delivers a mechanism for inferential statistics – “Hyperbolic Dirac Net”
  3. Created from System Dynamics and Systems Thinking Perspective.
  4. It is Systemic in approach; where System is itself the Method.
  5. Engages probabilistic ontology and semantics.
  6. Creates a mathematical framework to advance Inferential Statistics to study highly chaotic complex system.
  7. Is an algorithmic approach that creates Semantic Architecture of the problem or phenomena under study.
  8. The algorithmic approach is a blend of linguistics semantics, artificial intelligence and systems theory.
  9. The algorithm creates the Semantic Architecture defined by Probabilistic Ontology :- representing the Ecosystem Knowledge distribution based on Graph Theory

To make a decision in any domain, first of all the knowledge compendium of the domain or the system knowledge is imperative.

System Riddled with Complexity is generally a Multivariate System, as such creating much uncertainty

A highly complex system being non-deterministic, requires probabilistic approaches to discern, study and model the system.

General Characteristics of Complex System Methods

  • Descriptive statistics are employed to study “WHAT” aspects of the System
  • Inferential Statistics are applied to study “HOW”, “WHEN”, “WHY” and “WHERE” probing both spatial and temporal aspects.
  • In a highly complex system; the causality becomes indeterminable; meaning the correlation or relationships between the independent and dependent variables are not obviously established. Also, they seem to interchange the position. This creates dilemma between :- subject vs object, causes vs outcomes.
  • Approaching a highly complex system, since the priori and posterior are not definable; inferential techniques where hypothesis are fixed before the beginning the study of the system become enviable technique.

Review of Inferential Techniques as the Complexity is Scaled

Step 1:- Simple System (turbulence level:-1)

Frequentist :- simplest classical or traditional statistics; employed treating data random with a steady state hypothesis – system is considered not uncertain (simple system). In Frequentist notions of statistics, probability is dealt as classical measures based only on the idea of counting and proportion. This technique is applied to probability to data, where the data sets are rather small.

Increase complexity: Larger data sets, multivariate, hypothesis model is not established, large variety of variables; each can combine (conditional and joint) in many different ways to produce the effect.

Step 2:- Complex System (turbulence level:-2)

Bayesian :- hypothesis is considered probabilistic, while data is held at steady state. In Bayesian notions of statistics, probability is of the hypothesis for a given sets of data that is fixed. That is, hypothesis is random and data is fixed. The knowledge extracted contains the more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems.

Additionally the hypothesis can be explored only in an acyclic fashion creating Directed Acyclic Graphs (DAG)

Increase the throttle on the complexity: Very large data sets, both structured and unstructured,  Hypothesis random, multiple Hypothesis possible, Anomalies can exist, There are hidden conditions, need arises to discover the “probabilistic ontology” as they represent the system and the behavior within.

Step 3: Highly Chaotic Complex System (turbulence level:-3)

Certainly DAG is now inadequate, since we need to check probabilities as correlations and also causations of the variables, and if they conform to a hypothesis producing pattern, meaning some ontology is discovered which describes the peculiar intrinsic behavior among a specific combinations of the variables to represent a hypothesis condition. And, there are many such possibilities within the system, hence  very chaotic and complex system.

Now the System itself seems probabilistic; regardless of the hypothesis and the data. This demands Multi-Lateral Cognitive approach

Telandic …. “Point – equilibrium – steady state – periodic (oscillatory) – quasiperiodic – Chaotic – and telandic (goal seeking behavior) are examples of behavior here placed in order of increasing complexity”

A Highly Complex System, demands a Dragon Slayer – Hyperbolic Dirac Net (HDN) driven Statistics (BI-directional Bayesian) for extracting the Knowledge from a Chaotic Uncertain System.

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Know Your Health Ecosystem (Semantic Lake) :- Deep Learning from Healthcare Interoperability BigData – Descriptive and Inferential Statistics

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Bioingine.com; Platform for Healthcare Interoperability (large data sets) Analytics

Deep Learning from Millions of EHR Records

1. Payer – Provider:- (Mostly Descriptive Statistics)

Mostly answers “What”

  • Healthcare Management Analysis (Systemic Efficiencies)
  • Opportunities for cost reduction
  • Chronic patient management
  • Pathway analysis for cost insights
  • Service based to Performance Based – Outcome Analysis (+Inferential)

2. Provider – Clinical Data – (Mostly Inferential Statistics)

Reasoning to understand “Why”, “How”, “Where” (Spatial) and “When” (Temporal)

  • Healthcare Delivery Analysis (Clinical Efficacies)
  • EBM – Clinical Decision Support – Hypothesis Analysis
  • Pathways and Outcome (+Descriptive)

Health Information Exchange :- Interoperability Large BigData

HDN_Cognitive_Computing

Sample Descriptive Statistics:-

Inferential Statistics:-

New Kind of Cognitive Science – Medical Ontology – A.I driven Reasoning

cover_imageBioingine_Platform

 

 

Multi Lateral Cognition – Key Learning and Discerning Capability to Study Complex System

Simple View

Multi-Lateral View

Music by Bach, Chopin, Beethoven, Mozart – they helped to reach our experience beyond known boundary conditions.

Beethoven was deaf, he could not hear his own compositions. But then how did he create “ode to joy”… he believed god whispered to him…else how could he write the program for the composition that lifts one to heavenly levels….I chanced upon the idea of generative techniques that he employed in the way music progresses and creates emergent musical patterns…this is true stretch in experience of the boundary conditions…where the human mind listening to music is taken to unexplored areas….and joy that awaits is exhilarating and ecstatic.

A metaphor for generative transformation creating emergence from incorporation of Beethoven’s composition “emperor” integrated with a visualization of a dynamic state of complex system; in which there operates multiple influences on context. The influences are themseles in dynamic change, like a cube or tetrahedron or such having multilateral influences, all in dance, in motion and intersecting with one another. Each lattice, each bounce, each intersection producing a perception. Continous are such perceptions in creation, and each one is unique and every one perception produced is emergent, nothing from past produced…how to view such a dynamic system depicted above as A Metaphor for Generative emerging from the multi-lateral cognition.

Implicate Order as Ontology For Complex System – Creating Generative Transformation

For a messy complex system - undergoing generative transformation - Implicate Order provides the direction.

For a messy complex system – undergoing generative transformation – Implicate Order provides the direction.

Implicate Order as an Ontology v1.1

 

When Cartesian Breaks Down - Implicate Order Reins

When Cartesian Breaks Down – Implicate Order Reins

Generative Transformation :- System is the Method

Simple View – Cognition

Multi-Lateral View – Cognition

System is the Method:-

Application :- Accountable Care Organization (ACO) – Complex Adaptive / Generative System – CAS Modeling

ACO objective is to achieve Systemic Healthcare Effectiveness through High Quality Rendering at Least Possible Cost, by co-ordinated efforts, while the engaging to achieve a share in  the savings. This is completely a different Systemic behavior, such as seen in swarming of birds.

Cognition – result of social observation
Where each social unit employs relevant symbols to capture the knowledge and each of these knowledge could be a different level of abstraction. Enterprise or System Architecture as a architecture is sum of several architecture abstractions (various social observations), where architecture is considered as set of decisions. Architecture is established to navigate the system complexities. System architecture is described by sets of abstractions and system occupies different orders to mitigate complexities owing to entropy in a very complex system (messy).

The above challenge of observation (discernment) by different stakeholders can be brought into an assimilation, realized from the integrative influence, where different disciplines intermingle and create an ecosystem influence. Such findings are being studied to understand the rich diversity that emerged during cambrian explosion. Example the the geological influence on the sedimentation and its impact on the living organisms developing functional capabilities deriving from certain calcite properties as in bones, teeth etc

Combining discussion around Context and Integrative Social Inquiry

A. Observer (unconditioned and defeating imposition) merely observing system and asking only one question “why” – in context

(incorporating Bohm – Science, Order and Creativity – significance of social abilities in languaging, sensing, cognition, assimilation etc playing a role in formation of situational experience / knowledge).

B. Within system complexity becoming more complex – higher order of complexity. System “constraints” are experienced.

Change which is a constant by itself, has ‘conflict’ traveling along side constantly. This is a paradoxical phenomenon. Conflict is because of the inherent ‘constraint’ that is pushing the system to seek transformation. The constraint emerges due to the conflict in the resources, for which all living organisms are fighting for its own sustenance. Seeking to resolve the conflict, in order to overcome the constraint, the system transforms and it undergoes entropy occupying different increasing orders, as it mitigates levels of complexities in a complex system.

C. Observer (set of observers) record the “order” of the system as a result of integrative influences. Observers have to acquire ways to achieve equilibrium in the new order of complexity, by overcoming system constraints.

D. There are two contending ways the society can respond to achieve harmony within complex system.

References:
From – De’Arcy Wentworth Thomson (DWT) – Growth and Form
http://archive.org/stream/ongrowthform00thom#page/n7/mode/2up

and Form and Transformation – Gerry Webster and Brian Goodwin; this book makes a case for generative development in biology

a. Natural Selection – Adaptive – Darwinian – Natural Selection – Survival Of Fittest – Dominance

b. Self Regulation – Generative – Innovation – Diversity – Cambrian Explosion – Unique Peculiarities – Co Existence – Emergent

Accountable Care Organization (ACO) driven by Affordability Care Act transforms the present Healthcare System that is adaptive (competitive) into generative (collaborative / co-ordinated) to achieve inclusive success and partake in the savings achieved. This is a generative systemic response contrasting the functional and competitive response of an adaptive system.

Natural selection seems to have resulted in functional transformation, where adaptive is the mode; does not account for diversity.

Self Regulation – seems like is a systemic outcome due to integrative influence (ecosystem), responding to the system constraints. Accounts for rich diversity.

The observer learns generatively from the system constraints for the type of reflexive response required (Refer – Generative Grammar – Immune System – http://www.ncbi.nlm.nih.gov/pmc/articles/PMC554270/pdf/emboj00269-0006.pdf)

From the above observation, should the theory in self regulation seem more correct and that adheres to laws of nature, in which generative learning occurs. Then, the assertion is “method” is offered by the system itself. System’s ontology has an implicate knowledge of the processes required for transformation (David Bohm – Implicate Order)

For very large complex system,

System itself is the method – impetus is the “constraint”.

In the video below, the ability for the cells to creatively create the script is discussed which makes the case for self regulated and generative complex system in addition to complex adaptive system.

Invention (Innovation) Not Strategy Creates Renaissance – Moving from Darwinian Adaptive to Generative Transformation

Invention (Innovation), Not Strategy Creates Renaissance. Most Darwinian concepts does not engender to developing creativity, and so to innovation. Instead it is about strategy for developing dominant position, this is not a sustainable model as history has shown. Instead, Enterprise Architects should begin reinforcing energy into lost opportunities in innovation and explore to create newer territories.

In the recent times, we saw the fall of Michael Porter’s ideas around corporate Darwinism. His company during the past two decades influenced the CEO’s with trickle down ideas and C level were enamoured by it, as it helped them device system giving them enormous clout. Suddenly the landscape has changed, the market response has been very different, from what the CEO’s sought. This is because Darwinian theory does not sustain. Inorganic decisions are not working. From recent HP’s fiasco (Autonomy acquisition), it is much evident how corporates are massively faltering. Decade back, Carly Fiorina then HP’s CEO sought EA framework based on Darwinian adaptive principles as a way to achieve business enablement. It has not worked. It is now exactly a decade since she introduced. Theories developed on Darwinian dominance has been flawed and it is now much evident. Porter’s company recently declared bankruptcy. Those ideas are history.

What killed Michael Porter’s Monitor Group
http://www.forbes.com/sites/stevedenning/2012/11/20/what-killed-michael-porters-monitor-group-the-one-force-that-really-matters/

Check out interview with Carly’s Darwin EA framework to create adaptive capabilities.

http://www.hp.com/hpinfo/execteam/speeches/fiorina/forbes04.html

Based on flawed ideas, corporates employed resources targeting to achieve market dominating capabilities. Against this landscape, IT unfortunately has been delivering diminishing return. To overcome the value struggle that IT could offer, various schemes in the industry has been probed. Especially, TOGAF itself has been maturing to develop dialogue for IT from being across LOB service provider, cost to profit center, to more ambitious as business enabler. The argument of Nicholas Carr’s IT Does Not Matter when seemed almost true, ideas around creating EA driven strategic operating model emerged. Jeanne Ross book on EA as Strategy – Achieve Competitive Advantage

These ideas are getting outmoded. The essence of creating sustainable business model is to keep throttle on innovation. Challenges still remain to solve or probably discover newer opportunities by innovation that creates generative system, which intrinsically allows for emergence.

Check out Jeanne Ross discussion on EA – IT in context of business transformation

MIT’s Ross on How Enterprise Architecture and IT More Than Ever Lead to Business Transformation

In my mind, even Jeanne dwells on conventional wisdom. She is not discovering newer landscape. She discuses to improve the leverage to achieve strategy for transformation. The question is why/ which / what / where/ when strategy and how transformation and finally what outcome??

Dealing with thoughts like these, EA is not a domain of IT alone. EA is an integrative subject that brings together several disciplines to solve both macro systemic and micro functional concerns. EA can be used to reimagine and repurpose architectures including those realized by IT.

Another concern that EA must tackle in its value proposition is the value it can help achieve at system level. The GDP related to digitization has been in the increase. However, what is not evident is the “productive” impact of the digitized portion of the GDP. Meaning what activities in the digitized world are essential to mankind’s survival, are productive GDP. Innovations are required in increasing the potential of the productive GDP driven by IT. This argument is crucial.

In my view, EA can offer a great leverage to reimagine future, besides achieving leverage in the existing operating model. In pursuit of such mission, EA does not belong to “IT” alone. What we need is generative and not mere adaptive transformation efforts. It is in generative system, where integrative disciplines will work to allow for tacit knowledge creation. It is this tacit knowledge that will trigger emergence of newer opportunities, creating emergent architecture.