Diabetes: Patrones ocultos en series temporales

Profiling Intra-patient Type 1 Diabetes Behaviours

Iván Contreras

OUTLINE

• Introduction

•Methodology: Hidden Patterns

• In Silico Validation

• In Vivo Experiments

•Current and Future Work

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Type 1 Diabetes

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• Incurable disease

– Autoimmune attack on β-cells

– Hyperglycemia

– 5%-10%

• Intensive insulin treatments

– Multiple Daily injections

– Continuous Subcutaneous Insulin Infusion

– Hypoglucemia

Cardiovascular complications

Diabetic coma

Epileptic fit

Diabetic coma

Type 1 Diabetes

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• Artificial Pancreas

– Limited capacity to extract information

– High Variability

• Seasons, age, habits, menstural period, etc.

• Devoted to provide an innovative tool

– Cope to overload information

– Better Management

• Profile daily patterns

– Improved treatments

– Better control

– Close loop algorithms symbiosis

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Introduction: General Objective

• Introduction

•Methodology :Hidden Patterns •Diabetes: Normalized Compression Distance




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OUTLINE

Recommendation ranges for the standardization of glucose

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GREEN: GGGHGBBBGBHHGBBBGBAA

BLUE: GBEDBGEEBEGBHEBBCCAB

RED: GBEDBGEEBEGBGEECEBBB

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Modified Normalized Compression Distance

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OUTLINE

• Introduction

•Methodology :Hidden Patterns




In Silico Experiments: A Proof of Concept

•Mathematical models of diabetic patients

•Not guarantee in vivo performance

•Limitations and efficiency

•Girona APSim and LabVIEW software

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In Silico Experiments: Scenarios

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A

B

C

•10 Dalla Man patients

• Insulin Pump

• Individualized variations

• Value per minute

• Mixed meals libraries

• Scenario A features

• Exercise each two days (45 min.)

• Varying intensities

• Scenario B features

• Snack before exercise

In Silico Experiments: Scenarios Example

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In Silico Experiments: Good & Bad Control patients

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Number of hypoglycemia

Basic Exercise Exercise + Snack

P1 2 9 3

P2 1 8 1

P3 0 13 4

P4 0 14 12

P5 1 13 3

P6 2 13 15

P7 2 12 17

P8 1 6 1

P9 3 10 3

P10 0 8 1

Poorly controlled

Well controlled

Patient 7 Results

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In Silico Experiments:

Patient 4 Results

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In Silico Experiments:

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In Silico Experiments: Patient 1 Results

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In Silico Experiments: Patient 9 Results

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OUTLINE

• Introduction





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• Complex task : Collection, noise, consistent

database, etc.

• 10 patient of the hospital Clínic i Universitari of

Barcelona.

• Continuous subcutaneous insulin infusion therapy

• Tagged with temporal information : weekends and

bank days with differentiated profiles.

In Vivo Experiments: Patients

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In Vivo Experiments: Patient 3 Results

Clusters

A B C D E

Days 5 19 23 13 13

AvgBG 130 155 142 135 137

AvgVBG 0,3 0,3 0,2 0,2 0,3

StdVBG 0,06 0,07 0,05 0,02 0,08

AUC(180) 2,8 11,7 3,2 1,0 6,1

AUC(70) 0,46 0,05 0,07 0,05 0,25

Carbs 13,3 13,8 13,9 13,4 15,0

In/Carbs 1,82 1,95 1,69 1,67 1,78

T.Ins. 63,7 65,8 64,0 63,1 64,2

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Clusters

A B C D E

Days 9 9 19 9 14

AvgBG 126 154 136 119 122

AvgVBG 0,3 0,3 0,4 0,4 0,3

StdVBG 0,04 0,05 0,07 0,08 0,06

AUC(180) 2,7 10,9 11,4 6,7 4,0

AUC(70) 0,83 0,24 2,12 2,88 1,43

Carbs 30,3 30,3 29,3 28,7 31,9

In/Carbs 1,30 1,24 1,44 1,30 1,32

T.Ins. 46,0 46,1 48,9 43,6 47,6

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Clusters

A B C D

Days 29 9 17 34

AvgBG 180 151 167 178

AvgVBG 0,3 0,2 0,3 0,4

StdVBG 0,07 0,04 0,05 0,07

AUC(180) 24,7 5,8 14,9 26,5

AUC(70) 0,35 0,03 0,18 0,60

Carbs 17,8 15,6 15,5 14,8

In/Carbs 0,83 0,69 0,80 1,89

T.Ins. 37,9 34,6 35,2 35,5

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OUTLINE

• Introduction





Current and Future Work

• Profiling time series

• Real tagged information: premenstrual, pregnancy, etc.

• Automatic classification

• Glucose prediction

• Complex models : Real behaviors

• Multi-objective algorithms

• Intra-patient models prediction

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THE END

Diabetes: Patrones ocultos en series temporales

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