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Introduction
Videos
Our Team
Blogs
Glossary
HCn3D System
Provider Logic
Patient Centered Network
Patient Journey
Value | Population
Patient Centered Medical Home
CMS AI Health Outcomes Challenge
TennCare
The Pyramid Metaphor
1st Dimension
2nd Dimension
3rd Dimension
Scenarios
Reinforcement
Contact
Glossary
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Introduction
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Glossary
Healthcare in 3D Terms and Phrases
Classification (Machine Learning)
In HCn3D classification occurs when
patient journeys
of others are
counterfactually
analyzed to categorize the attributes or features of a subject patient journey (e.g. whether the patient is likely to have heart disease). A type of
reinforcement learning
built on
supervised
and
unsupervised learning
techniques.
Clustering (Machine Learning)
Is the result of
unsupervised learning
methods that attempt to find similarities in
unlabeled data
sets. HCn3D uses clustering methods to discover previously unknown correlations or classifications for use in
supervised learning
methods.
Decision Tree (machine learning)
Is a flowchart-like structure in which each “decision” represents the evaluation (or test) of a
feature
(e.g. whether a patient is male or female), each branch of the tree represents an outcome of the test, and each “leaf” (last outcome on the branch) represents a classification (e.g. Males over 40 years old who have heart disease).
Feature (machine learning)
Is a property or characteristic that can be measured or categorized in the data being analyzed (e.g. person's age).
Feature Engineering (machine learning)
Is a process by which knowledge of the data (or business domain) is used to create new (non-natural)
features
in an effort to improve the function (predictive power and/or efficiency) of
machine learning
algorithms?
Feature Hierarchy (Machine Learning)
The resulting feature output of the traversal of a
deep-learning
neural network
. The output of each layer aggregates and recombines forming recognizable patterns from
high-dimensional
data sets. For example, pixels in a picture combine to form facial features (eyes, mouth, nose, etc…) which in turn combines to form a recognizable face. In HCn3D this method can be applied to the free-from text fields in the patient
EHR
to obtain classifiable information that in turn is used in
Supervised Learning
techniques.
Feedforward Neural Network (Machine Learning)
The simplest form of a
neural network
in which the information moves in only one direction through the
node
. A feedforward neural net is considered “
deep
” when it constitutes 3 or more layers.
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