This document outlines research on using machine learning techniques to profile and cluster daily patterns in patients with type 1 diabetes. The goal is to better manage diabetes by improving treatment recommendations based on a patient's daily routines and behaviors. The methodology involves analyzing glucose time series data using a modified normalized compression distance approach to identify hidden patterns. In silico experiments using mathematical models and in vivo studies with patient data are presented and show clustering can capture variations related to factors like exercise and meals. Current and future work aims to advance profiling methods and develop more complex models incorporating real patient behaviors.

1. Profiling
Intra-patient
Type 1 Diabetes
Behaviours
Iván Contreras

2. OUTLINE
•Introduction
•Methodology: Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work
2

3. Type 1 Diabetes
3
• 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

4. Type 1 Diabetes
4
• Artificial Pancreas
– Limited capacity to extract information
– High Variability
• Seasons, age, habits, menstural period, etc.

5. • Devoted to provide an innovative tool
– Cope to overload information
– Better Management
• Profile daily patterns
– Improved treatments
– Better control
– Close loop algorithms symbiosis
5
Introduction: General Objective

6. •Introduction
•Methodology :Hidden Patterns
•Diabetes: Normalized Compression Distance
•In Silico Validation
•In Vivo Experiments
•Current and Future Work
6
OUTLINE

7. Recommendation
ranges for the
standardization
of glucose
7

8. 8
GREEN: GGGHGBBBGBHHGBBBGBAA
BLUE: GBEDBGEEBEGBHEBBCCAB
RED: GBEDBGEEBEGBGEECEBBB

9. 9
Modified Normalized Compression
Distance

10. 10
OUTLINE
•Introduction
•Methodology :Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work

11. 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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12. 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

13. In Silico Experiments:
Scenarios Example
13

14. 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

15. Patient 7 Results
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In Silico Experiments:

16. Patient 4 Results
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In Silico Experiments:

17. 17
In Silico Experiments:
Patient 1 Results

18. 18
In Silico Experiments:
Patient 9 Results

19. 19
OUTLINE
•Introduction
•Methodology :Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work

20. 20
• 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

21. 21
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

22. 22
In Vivo Experiments:
Patient 5 Results
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

23. 23
In Vivo Experiments:
Patient 1 Results
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

24. 24
OUTLINE
•Introduction
•Methodology :Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work

25. 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