Semantic Data Lake Delivering Tacit Knowledge – Evidence based Clinical Decision Support

Can the complexity be removed and tacit knowledge delivered from the plethora of the medical information available in the world.

” Let Doctors be Doctors”

Semantic Data Lake becomes the Book of Knowledge ascertained by correlation and causation resulting into Weighted Evidence

Characteristics of Cognitive Computing Platform

  • Architecture style moves from Event driven into Semantics driven
  • Paradigm shift in defining system behavior – it is no more predicated and deterministic – Non Predicated Design
  • Design is “systemic” contrasting the technique such as objected oriented based design, development and assembling components
  • As such a system is better probabilistically studied.
  • Design is context driven, where the boundary diminishes between context and concept
  • System capability is probabilistically programmed by machine learning based on A.I, NLP and algorithms driven by ensemble of Math
  • Design based on Semantic mining and engineering takes precedence to complex event processing (CEP). CEP and Event Driven Architecture (EDA) are the part of the predicated system design. Business rules engine may be an overkill.
  • Ontology is created driven by both information and numbers theory

–Algebra – relationship amongst variables

–Calculus – rate of change in variable and its impact on the other

–Vector Space – study of states of the variables algorithm design driven by Probabilistic Ontology

  • Probabilistic Ontology characterizes the ecosystem’s behavior
  • Complex System’s semantic representation evolves generatively
  • System better represented by semantic multiples. Overcomes the barrier of Triple Store (RDF)
  • Human’s interact with the system employing knowledge inference technique
  • Inductive knowledge precedes knowledge by deduction is a Probabilistic Computing Machine

  • System’s behavior better modeled by the employ of probability, statistics and vector calculus (Statistics based on HDN an advancement to Bayes Net, where acyclic in DAG is overcome)
  • Generally the system is characterized by high dimensionality in its data set (variability) in addition to volume and velocity.
  • Most computing is in-memory; is designed based on mathematics borrowed from several disciplines and notably from Paul A M Dirac’s quantum mechanics. The approach overcomes many of the inadequacies in the Bayes Net that is based on the directed acyclic graph (DAG). Like knowledge relationships in the real word, and as was required for quantum mechanics, our approaches are neither unidirectional nor do they avoid cycles. Features –

  • Bi-directional Bayesian Probability for knowledge Inference and Biostatistics (Hyperbolic complex).
  • Built upon medical ontology (in fact this is discovered by machine learning, AI techniques).
  • Can be both hypothesis and non-hypotheses driven.
  • Quantum probabilities transformed to classical integrating vector space, Bayesian knowledge inference, and Riemann zeta function to deal with sparse data and finally driven by overarching Hyperbolic Dirac Net.
  • Builds into web semantics employing NLP. (Integrates both System Dynamics and Systems Thinking).

Framework of Bioingine –Dirac-Ingine Algorithm Ensemble of Math 

Q-UEL & HDN (More Info click the link)


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