Transcript A Telemedicine System for Modeling and Managing Blood Glucose

Intelligent
Intelligent Diabetes
Diabetes Assistant
Assistant
A Telemedicine System for Modeling and
Managing Blood Glucose
David L. Duke
October 26, 2009
IDA
IDA Thesis
Thesis
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Individual models, taking into account
nutrition, medication, and exercise, with
appropriate mathematical modeling, can learn
accurate representations of specific patients
suitable for providing therapy advice.
Measurement
Measurement
Measurement
Diabetic
Diabetic
Diabetic
Therapy
Therapy
Therapy
Proposed
Proposed Outcomes
Outcomes
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Demonstrate the system that can collect,
share, and analyze.
Demonstrate improvement over previous
methods of modeling and blood glucose
prediction.
Demonstrate methods for generating therapy
advice from the models.
Background
Background and
and Motivation
Motivation
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Diabetes occurs when the control system for
maintaining normal blood glucose (70-100
mg/dl) fails.
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–
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Type 1 = pancreas failure.
Type 2 = insulin utilization failure.
The primary therapy inputs are meals,
medication, and exercise.
There are over 300,000,000 diabetics in the
world.
Modeling
Modeling Task
Task
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Two modeling tasks
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Predicting postprandial blood glucose
Continuous dynamic modeling
Key methods for model improvement
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Start with better data
Include the controllable inputs
Model nonlinearities
Uncertainty propagation
Start
Start with
with Better
Better Data
Data
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Three primary controllable components:
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Meals, Medication, Exercise -> BG
Collection system must be:
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In situ: data that represents real life.
Accurate: best available measurement
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Efficient: simple for patients to use
Complete: collects all primary inputs
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Networked: shares data with the care team
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IDA
IDA System
System
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Patient System
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Cellphone, Bodymedia
Health Care System
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Time-line, Meal analysis
Messaging
Clinical
Clinical Collection
Collection Protocol
Protocol
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16 patients collected data for two weeks.
Subjects were to measure:
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Pre-meal BG
Postprandial BG
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Meal images/carbs
Exercise
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All medications
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9 male, 7 female
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9 T2DM, 7 T1DM
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Diverse population
Descriptive
Descriptive Statistics
Statistics
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Parameter
Min Mean Max
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Age (yrs)
19
46
56
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BMI
21
29
43
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Weight (lbs)
121
170
232
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Mean BG (mg/dl)
108
147
229
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Std BG (mg/dl)
15
55
83
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Mean Carb (gm)
35
66
116
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Std Carb (gm)
17
35
68
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Mean Ex. (cal/m)
1.25 1.74 2.73
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Std Ex. (cal/m)
0.40 0.92 1.76
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Number BG
18
60
115
Postprandial
Postprandial Prediction
Prediction Problems
Problems
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Determining the variable order
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Modeling method comparison
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Linear vs. nonlinear
Joint vs. individual
Model performance as a function of training set
size
Predicting performance for patient
Evaluation
Evaluation Methods
Methods
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R2 coefficient
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Clarke Error grid
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A - 20%
B
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C
D
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E
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E
E
C
C
A
A
B
B
D
D
B
B
D
D
E
E
Model
Model Input
Input Parameters
Parameters
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Pre-meal BG
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Recent carbs
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Time of Postmeal BG
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Recent fat
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Exercise 2-1 hr before
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Recent protein
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Exercise 1-0 hr before
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Recent calories
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Exercise 0-1 hr after
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Rapid insulin
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Exercise 1-2 hr after
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Regular Insulin
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Time of day
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Recent Mixed Insulin
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Carbs
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Earlier Mixed Insulin
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Fat
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Sulfonylureas
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Protein
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Meglitinides
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Calories
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Biguanides...
Modeling
Modeling Methods
Methods
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Gaussian Process Regression
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Linear kernel or Gaussian kernel
Individual, Weighted, or Joint data
Reduced Rank Gaussian Process Regression
with Generic Basis
Gaussian
Gaussian Process
Process Regression
Regression
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GP Regression can be expressed as least
squares minimization with a regularization
parameter in the error function.
This give the solution below for new test
points.
GP
GP Kernel
Kernel Functions
Functions
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Linear kernel
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Gaussian kernel
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Patient similarity kernel (weighted mixture)
RRGP
RRGP description
description
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Convert to a generic reduced rank basis and
then apply patient specific coefficients.
Solution found using ALS
Variable
Variable Order
Order Experiment
Experiment
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10 times with randomly selected training and
test sets
GP regression with Gaussian and linear kernel
Greedy algorithm to select the next variable
based on either the R2 or Clarke metric
Results were combined using a voting
algorithm
Variable
Variable Order
Order Results
Results
Model
Model Performance
Performance Experiment
Experiment
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10 experiments with random training and test
sets.
Evaluated each model at each variable in
previously selected order.
Combined the results by calculating the mean
value of the evaluation metric.
Model
Model Performance
Performance Results
Results
Comparison
Comparison to
to Other
Other Results
Results
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IDA: 57% in region A
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Human predictions
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Other computational prediction systems
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28.5 %
41.5 %
34 %
51 %
Simulated theoretical bound
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43.6 %
Model
Model Performance
Performance Results
Results
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The Gaussian kernel is better than the linear
kernel.
There is no significant difference between the
individual and weighted mixture of patients
methods.
For the interdependent variables like
carbohydrates and insulin doses, the model
only improves with both measurements are
included.
Performance
Performance for
for aa New
New Patient
Patient
Patients
Patients with
with more
more than
than 30
30
valid
valid meal
meal events
events in
in their
their
dataset.
dataset.
Converges
Converges after
after 33 days.
days.
Predicting
Predicting modeling
modeling performance
performance
Continuous
Continuous Models
Models
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Four model were evaluated
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ARX – autoregressive with exogenous variables
AR – autoregressive
PM – published physiological model
PM+EX – physiological model with exercise
Three use cases
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Prediction: Model is updated with recent CGM
Real-time: Model is updated with recent BG
Retrospective: Model is update with both past
and future BG measurements
Physiological
Physiological Model
Model Components
Components
EKF
EKF Physiological
Physiological Model
Model
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The physiological model is implemented as an
Extended Kalman Filter.
The glucose regulatory system is a nonlinear
system.
The uncertainty in a blood glucose prediction
or estimate is as important as the actual
estimated value when generating therapy
advice.
The EKF propagates the uncertainty through
the system.
Model
Model Output
Output
Optimizing
Optimizing Insulin
Insulin Sensitivity
Sensitivity
Results
Results (15
(15 minutes)
minutes)
Comparison
Comparison of
of Models
Models
ARX
ARX
Physiological
Physiological Models
Models
Old Model
Model New
New Model
Model with
with Exercise
Exercise
Old
Exercise
Exercise vs
vs Old:
Old: Prediction
Prediction Time
Time
Old Model
Model New
New Model
Model with
with Exercise
Exercise
Old
Exercise
Exercise vs
vs Old:
Old: Real-time
Real-time
A
A
B
B
C
C
D
D
E
E
Old Model
Model New
New Model
Model with
with Exercise
Exercise
Old
Exercise
Exercise vs
vs Old:
Old: Retrospective
Retrospective
A
A
B
B
C
C
D
D
E
E
Therapy
Therapy Advice
Advice
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There are two general types of therapy advice
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Real-time
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Insulin dose adjustments
Hypoglycemia warnings
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Closed-loop control
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Retrospective
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Behavior advice
Parameter adjustment
Insulin
Insulin Dose
Dose Adjustment
Adjustment
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Combine insulin dose calculated using a
common equation with the optimal value
estimated using GP regression to shift the
suggested dose.
Providing
Providing Justification
Justification
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Justification can be given for a therapy
suggestion by referencing similar data from the
training set. Similar data can easily be
selected using the Gaussian kernel.
Hypoglycemia
Hypoglycemia Prediction:
Prediction: ARX
ARX
Closed-loop
Closed-loop Control
Control
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The most common form of closed-loop control
being investigated for an artificial pancreas is
model predictive control.
Any improvement in modeling performance
can help improve closed-loop control.
The key limiting factor is the variability in the
system, so accounting for exercise and the
uncertainty propagation is a positive
contribution.
Behavior
Behavior Space
Space Optimization
Optimization
Meal
Meal Portion
Portion Estimation
Estimation
Similar Meal
Meal Images
Images
Similar
Finding
Finding Similar
Similar Meals
Meals
Conclusions
Conclusions
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Demonstrate System:
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Demonstrate Improved Modeling:
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Data collection.
Postprandial: performs better than other
systems and humans.
Continuous: the method for including exercise
improved the published physiological model.
Demonstrate Advice:
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The system can be used to generate many types
of real-time and retrospective advice.
Acknowledgments
Acknowledgments
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Carnegie Mellon Qatar
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Hamad Medical Corporation
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Qatar Diabetes Association
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Mahmoud Zirie and Mazahir Mahmoud
Katie Nahas and Nedaa El-Khatib
Qatar National Science Fund
Questions?
Questions?
Gaussian
Gaussian Process
Process Regression
Regression
Include
Include similar
similar patients
patients
to
to help
help extrapolate
extrapolate (red).
(red).
DDaata
taffrroom
mtteest
st ssuubj
be
jecctt ((bl
bu
luee))..
CGM
CGM Quality
Quality
Raw
Raw Patient
Patient Data
Data
Sample
Sample Results
Results
Mixture of
of Patients
Patients
Mixture
Extrapolation
Extrapolation and
and Interpolation
Interpolation
Individual Data
Data
Individual
ggee
a
r
a
vveer
A
A
ivningg
v
MMoo
New
NewBehavior
Behavior
Repeated
Repeated Behavior
Behavior
RRGP
RRGP Error
Error Convergence
Convergence