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Profiling
Intra-patient
Type 1 Diabetes
Behaviours
Iván Contreras
OUTLINE
•Introduction
•Methodology: Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work
2
Type 1 Diabetes
3
• Incurable disease
– Autoimmune attack on β-cells
– Hyperglycemia
– 5%-10%
• Intensive insulin treatmen...
Type 1 Diabetes
4
• Artificial Pancreas
– Limited capacity to extract information
– High Variability
• Seasons, age, habit...
• Devoted to provide an innovative tool
– Cope to overload information
– Better Management
• Profile daily patterns
– Impr...
•Introduction
•Methodology :Hidden Patterns
•Diabetes: Normalized Compression Distance
•In Silico Validation
•In Vivo Expe...
Recommendation
ranges for the
standardization
of glucose
7
8
GREEN: GGGHGBBBGBHHGBBBGBAA
BLUE: GBEDBGEEBEGBHEBBCCAB
RED: GBEDBGEEBEGBGEECEBBB
9
Modified Normalized Compression
Distance
10
OUTLINE
•Introduction
•Methodology :Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work
In Silico Experiments:
A Proof of Concept
•Mathematical models of diabetic patients
•Not guarantee in vivo performance
•Li...
In Silico Experiments: Scenarios
12
A
B
C
•10 Dalla Man patients
•Insulin Pump
•Individualized variations
• Value per minu...
In Silico Experiments:
Scenarios Example
13
In Silico Experiments:
Good & Bad Control patients
14
Number of hypoglycemia
Basic Exercise Exercise + Snack
P1 2 9 3
P2 1...
Patient 7 Results
15
In Silico Experiments:
Patient 4 Results
16
In Silico Experiments:
17
In Silico Experiments:
Patient 1 Results
18
In Silico Experiments:
Patient 9 Results
19
OUTLINE
•Introduction
•Methodology :Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work
20
• Complex task : Collection, noise, consistent
database, etc.
• 10 patient of the hospital Clínic i Universitari of
Bar...
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...
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...
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
St...
24
OUTLINE
•Introduction
•Methodology :Hidden Patterns
•In Silico Validation
•In Vivo Experiments
•Current and Future Work
Current and Future Work
• Profiling time series
• Real tagged information: premenstrual, pregnancy, etc.
• Automatic class...
26
THE END
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Diabetes: Patrones ocultos en series temporales

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Conferencia Posgrado del Dr. Iván Contreras Fernández - Dávila, Universitat de Girona: "Diabetes: Patrones ocultos en series temporales" impartida el 09 de Abril de 2015

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Diabetes: Patrones ocultos en series temporales

  1. 1. Profiling Intra-patient Type 1 Diabetes Behaviours Iván Contreras
  2. 2. OUTLINE •Introduction •Methodology: Hidden Patterns •In Silico Validation •In Vivo Experiments •Current and Future Work 2
  3. 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. 4. Type 1 Diabetes 4 • Artificial Pancreas – Limited capacity to extract information – High Variability • Seasons, age, habits, menstural period, etc.
  5. 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. 6. •Introduction •Methodology :Hidden Patterns •Diabetes: Normalized Compression Distance •In Silico Validation •In Vivo Experiments •Current and Future Work 6 OUTLINE
  7. 7. Recommendation ranges for the standardization of glucose 7
  8. 8. 8 GREEN: GGGHGBBBGBHHGBBBGBAA BLUE: GBEDBGEEBEGBHEBBCCAB RED: GBEDBGEEBEGBGEECEBBB
  9. 9. 9 Modified Normalized Compression Distance
  10. 10. 10 OUTLINE •Introduction •Methodology :Hidden Patterns •In Silico Validation •In Vivo Experiments •Current and Future Work
  11. 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 11
  12. 12. In Silico Experiments: Scenarios 12 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. 13. In Silico Experiments: Scenarios Example 13
  14. 14. In Silico Experiments: Good & Bad Control patients 14 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. 15. Patient 7 Results 15 In Silico Experiments:
  16. 16. Patient 4 Results 16 In Silico Experiments:
  17. 17. 17 In Silico Experiments: Patient 1 Results
  18. 18. 18 In Silico Experiments: Patient 9 Results
  19. 19. 19 OUTLINE •Introduction •Methodology :Hidden Patterns •In Silico Validation •In Vivo Experiments •Current and Future Work
  20. 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. 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. 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. 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. 24 OUTLINE •Introduction •Methodology :Hidden Patterns •In Silico Validation •In Vivo Experiments •Current and Future Work
  25. 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 25
  26. 26. 26 THE END

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