Month: April 2016

Platform for BigData Driven Medicine and Public Health Studies [ Deep Learning & Biostatistics ]

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Bioingine.com; Platform for comprehensive statistical and probability studies for BigData Driven Medicine and Public Health.

Importantly helps redefine Data driven Medicine as:-

Ontology (Semantics) Driven Medicine

Comprehensive Platform that covers Descriptive Statistics and Inferential Probabilities.

Beta Platform on the anvil. Signup for Demo by sending mail to

“demo@bioingine.com”

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. 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.

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.

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.

BigData Driven Medicine Program :-

http://med.stanford.edu/iddm.html

Objectives and Goals

Informatics & Data-Driven Medicine (IDDM) is a foundation area within the Scholarly Concentration program that explores the new transformative paradigm called BIG DATA that is revolutionizing medicine. The proliferation of huge databases of clinical, imaging, and molecular data are driving new biomedical discoveries and informing and enabling precision medical care. The IDDM Scholarly Concentration will provide students insights into this important emerging area of medicine, and introducing fundamental topics such as information management, computational methods of structuring and analyzing biomedical data, and large-scale data analysis along the biomedical research pipeline, from the analysis and interpretation of new biological datasets to the integration and management of this information in the context of clinical care.

Requirements

Students who pursue Informatics & Data-Driven Medicine in conjunction with an application area, such as Immunology, are required to complete 6 units including:

Biomedin 205: Precision Practice with Big Data

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Bioingine.com :- Quantum Mechanics Machinery for Healthcare Ecosystem Analytics

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Notational – Symbolic Programming Introduced for Healthcare Analytics

Quantum Mechanics Firepower for Healthcare Ecosystem Studies        

Interoperability Analytics

Public Health and Patient Health

Quantum Mechanics Driven A.I Experience

Deep Machine Learning

Descriptive and Inferential Statistics

Definite and Probabilistic Reasoning and Cognitive Experience

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:-