The BioIngine.com™

Ingine; Inc™, The BioIngine.com™, DiracIngine™, MARPLE™ are all Ingine Inc © and Trademark Protected; also The BioIngine.com is Patent Pending IP belonging to Ingine; Inc™.

High Performance Cloud based Cognitive Computing Platform

The below figure depicts the healthcare analytics challenge as the order of complexity is scaled.

**1. Introduction Beta Release 1.0**

It is our pleasure to introduce startup venture Ingine; Inc that brings to market **The BioIngine.com™**; **Cognitive Computing Platform **for the Healthcare market, delivering Medical Automated Reasoning Programming Language Environment (MARPLE) capability based on the mathematics borrowed from several disciplines and notably from *late Prof Paul A M Dirac’s Quantum Mechanics*.

**The BioIngine.com™**; is a **High Performance Cloud Computing Platform**delivering HealthCare Large-Data Analytics capability derived from an ensemble of bio-statistical computations. The automated bio-statistical reasoning is a combination of “* deterministic*” and “

*” methods employed against both structured and unstructured large data sets leading into Cognitive Reasoning.*

**probabilistic****The BioIngine.com™**; delivers Medical Automated Reasoning based on a Medical Automated Programming Language Environment (MARPLE) capability, so better achieving __2nd order semantic interoperability1 in the Healthcare ecosystem__. (Appendix Notes)

**The BioIngine.com™** is a result of several years of efforts with Dr. Barry Robson; former Chief Scientific Officer, IBM Global Healthcare, Pharmaceutical and Life Science. His research has been in developing quantum math driven exchange and inference language achieving semantic interoperability, while also enabling Clinical Decision Support System, that is inherently Evidence Based Medicine (EBM). The solution, besides enabling EBM, also delivers knowledge graphs for Public Health surveys including those sought by epidemiologists. Based on Dr Robson’s experience in the biopharmaceutical industry and pioneering efforts in bioinformatics, this has the data mining driven potential to advance pathways planning from clinical to pharmacogenomics.

**The BioIngine.com™**; brings the machinery of Quantum Mechanics to Healthcare analytics; delivering a comprehensive data science experience that covers both Patient Health and Population Health (Epidemiology) analytics, driven by a range of bio-statistical methods from descriptive to inferential statistics, leading into evidence driven medical reasoning.

**The BioIngine.com™;** transforms the large clinical data sets generated by interoperability architectures, such as in Health Information Exchange (HIE) into “* semantic lake*” representing the Health ecosystem that is more amenable to bio-statistical reasoning and knowledge representation. This capability delivers evidence-based knowledge needed for Clinical Decision Support System, better achieving Clinical Efficacy by helping to reduce medical errors.

**The BioIngine.com™;** platform working against large clinical data sets or while residing within the large Patient Health Information Exchange (HIE) works in creating opportunity for Clinical Efficacy, while it also facilitates in the better achievement of “Efficiencies in the Healthcare Management” that Accountable Care Organization (ACO) seeks.

Our endeavors have resulted in the development of revolutionary Data Science to deliver Health Knowledge by Probabilistic Inference. The solution developed addresses critical areas in both scientific and technical, notably the healthcare interoperability challenges of delivering semantically relevant knowledge both at patient health (clinical) and public health level (Accountable Care Organization).

*2. Why***The BioIngine.com™**?

The basic premise in engineering **The BioIngine.com™ **is in acknowledging the fact that in solving knowledge extraction from the large data sets (both structured and unstructured), one is confronted by very large data sets riddled by high-dimensionality and uncertainty.

Generally in solving insights from the large data sets the order in complexity is scaled as follows:-

**A. Insights around :- “what” **

For large data sets, descriptive statistics are adequate to extract a “what” perspective. Descriptive statistics generally delivers statistical summary of the ecosystem and the probabilistic distribution.

**B. Univariate Problem :- “what” **

Considering some simplicity in the variables relationships or is cumulative effects between the independent variables (causing) and the dependent variables (outcomes):-

a) Univariate regression (simple independent variables to dependent variables analysis)

b) Correlation Cluster – shows impact of set of variables or segment analysis.

https://en.wikipedia.org/wiki/Correlation_clustering

[From above link:- In machine learning, **correlation clustering** or **cluster editing** operates in a scenario where the relationships between the objects are known instead of the actual representations of the objects. For example, given a weighted graph G = (V,E), where the edge weight indicates whether two nodes are similar (positive edge weight) or different (negative edge weight), the task is to find a clustering that either maximizes agreements (sum of positive edge weights within a cluster plus the absolute value of the sum of negative edge weights between clusters) or minimizes disagreements (absolute value of the sum of negative edge weights within a cluster plus the sum of positive edge weights across clusters). Unlike other clustering algorithms this does not require choosing the number of clusters k in advance because the objective, to minimize the sum of weights of the cut edges, is independent of the number of clusters.]

**C. Multivariate Analysis (Complexity increases) :- “what”**

a) Multiple regression (considering multiple univariate to analyze the effect of the independent variables on the outcomes)

b) Multivariate regression – where multiple causes and multiple outcomes exists

All the above are still discussing the “what” aspect. When the complexity increases the notion of independent and dependent variables become non-deterministic, since it is difficult to establish given the interactions, potentially including cyclic paths of influence in a network of interactions, amongst the variables. A very simple example in just a simple case is that obesity causes diabetes, but the also converse is true, and we may also suspect that obesity causes type 2 diabetes cause obesity… In such situation what is best as “subject” and what is best as “object” becomes difficult to establish. Existing inference network methods typically assume that the world can be represented by a Directional Acyclic Graph, more like a tree, but the real world is more complex than that that: metabolism, neural pathways, road maps, subway maps, concept maps, are not unidirectional, and they are more interactive, with cyclic routes. Furthermore, discovering the “how” aspect becomes important in the diagnosis of the episodes and to establish correct pathways, while also extracting the severe cases (chronic cases which is a multivariate problem). *Indeterminism also creates an ontology that can be probabilistic, not crisp.*

Most ACO analytics addresses the above based on the PQRS clinical factors, which are all quantitative. **Barely useful for advancing the ACO into solving performance driven or value driven outcomes most of which are qualitative.**

**D. Neural Net :- “what”**

https://www.wolfram.com/language/11/neural-networks/?product=mathematica

The above discussed challenges of analyzing multivariate pushes us into techniques such as Neural Net; which is the next level to Multivariate Regression Statistical Approach…. where multiple regression models are feeding into the next level of clusters, again an array of multiple regression models.

The Neural Net method still remains inadequate in exposing “how” probably the human mind is organized in discerning the health ecosystem for diagnostic purposes, for which “how”, “why”, “when” etc becomes imperative to arrive at accurate diagnosis and target outcomes efficiently. Its learning is “smudged out”. A little more precisely put: it is hard to interrogate a Neural Net because it is far from easy to see what are the weights mixed up in different pooled contributions, or where they come from.

“So we enter Probabilistic Computations which is as such Combinatorial Explosion Problem”.

**E. Hyperbolic Dirac Net (Inverse or Dual Bayesian technique): – “how”, “why”, “when” in addition to “what”.**

Note:- **Beta Release 1.0 only addresses HDN transformation and inference query against the structured data sets and Features A, B and E. However, as a non-packaged solution C and D features can still be explored.**

**Release 2.0 will deliver full A.I driven reasoning capability MARPLE working against both structured and unstructured data sets. Furthermore, it will be designed to be customized for EBM driven “Point Of Care” and “Care Planning” productized user experience.**

**The BioIngine.com™**; *offers a comprehensive bio-statistical reasoning experience in the application of the data science as discussed above that blends descriptive and inferential statistical studies.*

**The BioIngine.com™**; is a **High Performance Cloud Computing Platform**delivering HealthCare Large-Data Analytics capability derived from an ensemble of bio-statistical computations. The automated bio-statistical reasoning is a combination of “* deterministic*” and “

*” methods employed against both structured and unstructured large data sets leading into Cognitive Reasoning.*

**probabilistic**Given the challenge of analyzing against the large data sets both structured (EHR data) and unstructured data; the emerging Healthcare analytics are around above discussed methods D and E; Ingine Inc is unique in the Hyperbolic Dirac Net proposition.