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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
Action (Machine Reinforcement Learning)
The decision that could be made by the Clinician (
Agent
) at the
Inflection Point
.
Agent (Machine Reinforcement Learning)
In HCn3D represent the clinician (and patient) taking
Action
at the
Inflection Point
.
Discount factor (Machine Reinforcement Learning)
Provides a method for weighting or biasing the value of immediate vs. deferred
Rewards
. In HCn3D it is used to influence
Outcomes
for acute vs chronic care. For instance an acute condition would necessarily require a bias towards more immediate
Actions
.
Environment (Machine Reinforcement Learning)
The world through which the
Agent
moves. In HCn3D it is the Healthcare Network and all data associated with the
Patient Record
, both intrinsic and extrinsic. Computationally it is where the Agent’s
Actions
are computed to formulate the feedback, the
Reward
and next iteration.
Policy (Machine Reinforcement Learning)
Is the strategy employed by the
Agent
as it moves through the
Environment
to determine its next
Action
.
Q-value (Action value) (Machine Reinforcement Learning)
Is the prediction of the
Q Function
which formulates the highest probable
Reward
from the sequences of
State
/
Action
pairs, essentially a consideration of all
Action Spectrums
from the current
cube
through the last cube achieving the desired
Trajectory
. It is HCn3D best “guess” for the desired
future outcomes
at the current
Inflection Point
. Together with
Policy
the application of
Counterfactual
analysis.
Reinforcement Learning
In HCn3D is an iterative form of “
Semi-Supervised Learning
” whereby feedback is
counterfactually
analyzed for the next iteration. The process evaluates its
actions
based on outcomes compared with known
patient journeys
. Its aim is to learn sequences of actions that will achieve the goal of predicting the patient’s possible futures (
Future Value Outcomes
)
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