RCIS 2016 conference paper: Variable Interactions in Risk Factors for Dementia

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Presentation for my paper in #rcis2016. An information systems conference in Grenoble in France. In it I use neural networks to discover novel abstract features from dementia data.

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RCIS 2016 conference paper: Variable Interactions in Risk Factors for Dementia

  1. 1. Variable Interactions in Risk Factors for Dementia Jim O’ Donoghue, Mark Roantree and Andrew McCarren Research funded by: European Union Seventh Framework Programme, grant agreement number 304979 and Science Foundation Ireland, grant agreement number SFI/12/RC/2289.
  2. 2. 25% Introduction 50% Approach 90% Experiments 100% Conclusions 2
  3. 3. 3
  4. 4. Dementia 4
  5. 5. Dementia 1 Alzheimer Europe, The prevalence of Dementia in Europe, Online: http://www.alzheimer- europe.org/Policy-in-Practice2/Country-comparisons/The-prevalence-of-dementia-in- Europe; Last Accessed 30-05-16 . 5
  6. 6. Dementia 2 Alzheimer Society, Dementia 2014 Report: Opportunity for Change; Online: https://www.alzheimers.org.uk/dementia2014; Last Accessed 30-05-16. 6
  7. 7. Dementia 2 Alzheimer Society, Dementia 2014 Report: Opportunity for Change; Online: https://www.alzheimers.org.uk/dementia2014; Last Accessed 30-05-16. 7
  8. 8. In-Mindd 8
  9. 9. In-Mindd 9 FP7 Project
  10. 10. In-Mindd 10 In novative Mi d-life In tervention for D ementia D eterrence FP7 Project
  11. 11. In-Mindd 11
  12. 12. In-Mindd 12
  13. 13. In-Mindd 13
  14. 14. Lower dementia risk in middle-age (40-60) In-Mindd 14
  15. 15. Lower dementia risk in middle age (40-60) Modifiable Dementia Risk+Protective Factors In-Mindd 15
  16. 16. Evaluate dementia factors 16
  17. 17. Evaluate dementia factors Improve dementia survival predictions 17
  18. 18. Evaluate dementia factors Improve survival predictions Determine factor interactions 18
  19. 19. 19 :7 factor combinations tested
  20. 20. 20 :7 factor combinations tested :Neural network surivival analysis
  21. 21. 21 :7 factor combinations tested :Neural network surivival analysis :Candidate interactions found
  22. 22. 22 Test dementia factors :7 combinations tested Improve survival predictions :Neural networks Determine factor interactions :Hidden layer analysis
  23. 23. 23
  24. 24. Process 1. Hyper-Parameter Configuration 24
  25. 25. Process 1. Hyper-Parameter Configuration 2. Parameter optimisation 25
  26. 26. Process 2. Parameter optimisation 1. 2. 3. 4. 5. 26
  27. 27. Process 2. Parameter optimisation 1. initialise architecture 2. 3. 4. 5. 27
  28. 28. Process 2. Parameter optimisation 1. initialise architecture 2. construct hypothesis 3. 4. 5. 28
  29. 29. Process 2. Parameter optimisation 1. initialise architecture 2. construct hypothesis 3. build cost 4. 5. 29
  30. 30. Process 2. Parameter optimisation 1. initialise architecture 2. construct hypothesis 3. build cost 4. construct model 5. 30
  31. 31. Process 2. Parameter optimisation 1. initialise architecture 2. construct hypothesis 3. build cost 4. construct model 5. train 31
  32. 32. 32 Input Visible Layer h(1)Hidden Layer 𝑥 Output Visible Layer 𝑜
  33. 33. 33 Input Visible Layer h(1) ……𝑥2 Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 Output Visible Layer 𝑜 W(1)
  34. 34. 34 Input Visible Layer h(1) ……𝑥2 Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 Output Visible Layer 𝑜 W(1)
  35. 35. 35 Input Visible Layer h(1) ……𝑥2 Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 Output Visible Layer 𝑜 W(1)
  36. 36. 36 Input Visible Layer h(1) ……𝑥2 Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 Output Visible Layer 𝑜 W(1)
  37. 37. 37 a(1) 0 Input Visible Layer h(1) ……𝑥2 a(1) o…a(1) 2a(1) 1 Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 Output Visible Layer 𝑜 W(1)
  38. 38. 38 a(1) 0 Input Visible Layer W(1) h(1) ……𝑥2 a(1) o…a(1) 2a(1) 1 W(2) Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 D1 Output Visible Layer CD2S 𝑜
  39. 39. Classifications S -> Surivival D1 ->Dementia D2 -> Death (without dementia) C -> Censorship (study drop-out) 39
  40. 40. a(1) 0 Input Visible Layer W(1) h(1) ……𝑥2 a(1) o…a(1) 2a(1) 1 W(2) Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 D1 Output Visible Layer CD2S 𝑜 40
  41. 41. a(1) 0 Input Visible Layer W(1) h(1) ……𝑥2 a(1) o…a(1) 2a(1) 1 W(2) Hidden 𝑥1 𝑥 𝑛𝑥0 Layer 𝑥 D1 Output Visible Layer CD2S 𝑜 41
  42. 42. 42
  43. 43. Variables Maastricht Ageing Study (MAAS) 840x25 subset -> 15 binary -> 9 continuous/discrete -> 1 derived 43
  44. 44. Combinations 14 factors -> 3 non-modifiable -> 11 modifiable 44
  45. 45. Combinations 14 factors -> 3 non-modifiable - Age - Gender - Education before 21 45
  46. 46. Combinations 14 factors -> 11 modifiable 3 protective - Cognitive ativity - Physical activity - Moderate alcohol use 46
  47. 47. Combinations 14 factors -> 11 modifiable 8 risk - Smoking - Mid-life obesity - Mid-life hypertension - Diabetes 47 - Cholesterol - Cardiovascular disease - Kidney disease - Depression
  48. 48. Combinations 14 risk factors -> 11 modifiable -> 3 non-modifiable 7 combinations tested -> 3 baseline no relative risk weight -> 4 with relative risk 48
  49. 49. Combinations without relative risk weights B1 = BinaryBaseline1 B2 = BinaryBaseline2 CB = ContinuousBaseline 49
  50. 50. Combinations without relative risk weights B1 + B2: 11 binary modifiable; adjusting for age, sex + education 50
  51. 51. Combinations without relative risk weights B1 + B2: 11 binary modifiable; adjusting for age, sex + education dementia yes/no vs. multi-class 51
  52. 52. Combinations without relative risk weights B1 + B2: binary modifiable; adjusting; dementia yes/no vs. multi-class CB: 6 binary; ; 52
  53. 53. Combinations without relative risk weights B1 + B2: binary modifiable; adjusting; dementia yes/no vs. multi-class CB: 6 binary; ; modifiable; adjusting; multi-class 53
  54. 54. Combinations with relative risk weights BW1 BW2 BW3 CW 54
  55. 55. Combinations with relative risk weights BW1: 11 binary modifiable; adjusting BW2 BW3 CW 55
  56. 56. Combinations BW1: 11 binary modifiable; adjusting BW2: 11 bin. mod. adj. BW3 CW 56
  57. 57. Combinations BW1: 11 binary mod. adj. BW2: 11 bin. mod. adj. BW3: 11 bin. mod. CW 57
  58. 58. Combinations BW1: 11 binary mod. adjusted BW2: 11 mod. adj. BW3: 11 mod. CW: 6 binary; 5 continuous 58
  59. 59. batch size (1, 50) n hidden nodes (2, 20) learning rate (0.0001, 0.3) regularisation (0.0001, 0.1) max epochs (100, 2000) max iterations (5000, 100000) 59
  60. 60. batch size (1, 50) n hidden nodes (2, 20) learning rate (0.0001, 0.3) regularisation (0.0001, 0.1) max epochs (100, 2000) max iterations (5000, 100000) 60
  61. 61. 61
  62. 62. Accuracy 0.56 0.59 0.62 0.65 0.68 0.71 B2-sd CB-sd BW1 BW2-sd BW3 CW-sd62
  63. 63. 0.0187 0.0002 0.0221 0.003 0.0001 0.0002 B2-sd CB-sd BW1 BW2-sd BW3 CW-sd Predictive Significance 63
  64. 64. 64 Risk Interactions -0.39178 -0.004 -0.003 -0.002 -0.001 0 0.001 0.002 0.003 0.004 h1 h2 h3 h4 h5 h6 age_risk educ_risk cog_act phys_inact obese diabetes di_alcohol smokes depressed hyperT cholesterol cvd kidney
  65. 65. 65 Risk Interactions -0.39178 -0.004 -0.003 -0.002 -0.001 0 0.001 0.002 0.003 0.004 h1 h2 h3 h4 h5 h6 age_risk educ_risk cog_act phys_inact obese diabetes di_alcohol smokes depressed hyperT cholesterol cvd kidney
  66. 66. 66 Node Weights 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 h1 h2 h3 h4 h5 h6
  67. 67. 67
  68. 68. Neural network framework 68
  69. 69. Neural network framework HP optimisation Hidden layer + Non linear survival analysis 69
  70. 70. Neural network framework Confirm and improve predictions Candidate risk interactions 70
  71. 71. Neural network framework Confirm and improve predictions Candidate risk interactions Continuous Data + Relative Risk Weight Importance 71
  72. 72. jim.odonoghue@insight-centre.org 72
  73. 73. jim.odonoghue@insight-centre.org Research funded by: European Union Seventh Framework Programme, grant agreement number 304979 and Science Foundation Ireland, grant agreement number SFI/12/RC/2289.
  74. 74. Middle-aged individuals (40 – 60) In-Mindd 74
  75. 75. Middle-aged individuals (40 – 60) Risk Profiler + Support Environment In-Mindd 75
  76. 76. Middle-aged individuals (40 – 60) Risk Profiler + Support Environment Dementia Risk Factors In-Mindd 76
  77. 77. Sensitivity/Recall 0.35 0.45 0.55 0.65 0.75 0.85 0.95 B2-sd CB-sd BW1 BW2-sd BW3 CW-sd77
  78. 78. Specificity 0.35 0.45 0.55 0.65 0.75 0.85 0.95 B2-sd CB-sd BW1 BW2-sd BW3 CW-sd78
  79. 79. Precision 0.45 0.48 0.51 0.54 0.57 0.6 0.63 0.66 B2-sd CB-sd BW1 BW2-sd BW3 CW-sd79
  80. 80. Area Under the Curve 0.68 0.7 0.72 0.74 0.76 0.78 B2-sd CB-sd BW1 BW2-sd BW3 CW-sd80

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