Healthcare Analytics and Medical Automated Reasoning

Quantum Mechanics Machinery for Big Data Driven Medicine

Hyperbolic Dirac Net

Bioingine.com employs algorithmic approach based on Hyperbolic Dirac Net that allows inference nets that are a general graph (GC), including cyclic paths, thus surpassing the limitation in the Bayes Net that is traditionally a Directed Acyclic Graph (DAG) by definition.

Probabilistic Knowledge

The Bioingine.com approach thus more fundamentally reflects the nature of probabilistic knowledge in the real world, which has the potential for taking account of the interaction between all things without limitation, and ironically this more explicitly makes use of Bayes rule far more than does a Bayes Net.

Deep Learning

It also allows more elaborate relationships than mere conditional dependencies, as a probabilistic semantics analogous to natural human language but with a more detailed sense of probability. To identify the things and their relationships that are important and provide the required probabilities, the Bioingine.com scouts the large complex data of both structured and also information of unstructured textual character.

Priori and Posterior Knowledge

It treats initial raw extracted knowledge rather in the manner of potentially erroneous or ambiguous prior knowledge, and validated and curated knowledge as posterior knowledge, and enables the refinement of knowledge extracted from authoritative scientific texts into an intuitive canonical “deep structure” mental-algebraic form that the Bioingine.com can more readily manipulate.

BioIngine.com

MARPLE

The Medical Automated Reasoning Programming Language Environment

Dirac-Ingine :- Non Predicated Machine Learning

Is a data science approach that embraces Dirac Notation, employing Quantum-Universal Exchange Language (Q-UEL) and leading into Hyperbolic Dirac Nets (HDN - General Graphs) which include Bayes Nets (Directed Acyclic Graphs) option as subset.

Dirac-Miner :- Unsupervised Structured Data Mining

Is a machine learning (supervised and unsupervised) tool that employs Dirac Notation to extract knowledge (semantic triple store) from large data sets riddled with uncertainty. The extracted medical knowledge in terms of semantic triples or multiples), represented as Hyperbolic Dirac Net (HDN) Graphs, are placed in a Knowledge Representation Store (KRS).

Dirac-Inference : - HDN based Inference Mechanism from KRS

Is an inference tool, that based on a complex query, which is an ensemble of variables and measurements, scans the HDN based semantic triple store - Knowledge Representation Store (KRS), and finds the missing statements or paths that connect them so that overall, questions, statements between, and each answer in turn represents an HDN.

Dirac-Xtractor :- Unsupervised Unstructured Data Mining

Is a unsupervised unstructured web searching tool that employs Dirac Notation having strong relationship with the Semantic Structure used in the Semantic Web. The tool can quickly generate many millions of statements of knowledge. The extracted knowledge as semantic triples and semantic multiples are placed in the Knowledge Representation store.

Health Ecosystem Risk Stratification

Hyperbolic Dirac Net Inference (Probabilistic and Deterministic Reasoning)

Dirac-Ingine - Artificial Intelligence based Unsupervised Clinical Knowledge Inference From Large Data Sets

Hyperbolic Dirac Net - Machine Learning - Resolving Uncertainty

The Q-UEL Language

Q-UEL is an XML-like universal exchange language based on mutually consistent principles from physics, mathematics, artificial intelligence and semantics.

Dirac Notation

Based on the notation and associated algebra developed by Professor Paul A M Dirac, called the Dirac notation, bracket notation, or braket notation.

Quantum Algebra

It was first developed to handle uncertain observations and probabilistic inference in quantum mechanics.

Web Semantics

Integrates with web as an intelligent thinking entity, capable of reasoning to help humans make decisions.

Healthcare Performance based Outcomes

Driven by Clinical Efficacy

Efficacy of an Evidence-based Clinical Decision Support in Primary Care Practices http://www.ncbi.nlm.nih.gov/pubmed/23896675

The inherent uncertainty of medical knowledge renders it important to associate its representation with some kind of measure of probability, or degree of certainty, confidence, or scope. The modern approaches to medicine such as Evidence Based Medicine, Comparative Effectiveness Research and outcomes analysis are based on ideas of risk management and medical metrics that involve fundamentally probabilistic measures.

20 %

Mortality Rate - - % of population dying of disease in a specified time during the epidemic

30 %

Fatality Rate - % of people with disease who die

40 %

Attack Rate - % of specified population acquiring disease in a specified time at the start of the epidemic

50 %

Incidence Rate - % of specified population acquiring disease in a specified time during the epidemic