2017-11-11 台灣資料科學年會演講 (http://datasci.tw/)

1. 用深度學習解決醫療
尚未滿足的需求
Pei-shen Wu, MD
2017-11-11 @ 資料科學年會

2. 吳沛燊 peishen.wu
台大醫院復健科總醫師
均一教育平台 Data scientist
深義分析公司 Data scientist

3. Disclaimer
I don’t own or make claim of the images used in this
slide or it’s copyright.
This work is solely intended as a medium to express
concepts and promote further work in this area.
All copyrights belongs to their respective authors

4. Data
Business Analytics &
Modelling

5. Finding gaps within
the healthcare system
Deep learning as a general
purpose technology

6. Finding gaps within
the healthcare system
Deep learning as a general
purpose technology

7. Gaps =
Unmet Needs 與 Market
之間的交集

8. 舉例 1

10. J Clin Oncol. 2006 Aug 10;24(23):3726-34.
N Engl J Med 2004; 351:2817-2826
What is Oncotype Dx ?
• A diagnostic test for breast cancer
• Analyze 21 genes from breast tumor to
determine
– Predicts recurrence
– Predicts the benefit of chemotherapy

11. Predicts recurrence
 diagnostics
Predicts benefit of
chemotherapy
 較高的議價空間
USD 25-50/test
Margin 5-10%
100% penetration
市場大小 5 million/yr
Biodesign: The Process of Innovating Medical Technologies 1st ed.
化療 15000 USD/人
若減少非必要的治療達50%
約莫 7000 USD/test
 市場大小 700 million/yr

12. Market
segmention
Market size
Market
dynamics
Product
development
Biodesign: The Process of Innovating Medical Technologies 1st ed.
Needs
assessment

13. Patients
Providers Payers
症狀、風險、場域
是否已經有診斷？
專科、場域、態度風氣
對新科技的評估
政策?
如何區隔市場

14. 舉例 2

16. 肺部復健
有助於改善慢性阻塞性
肺疾病患者的存活率
https://doi.org/10.2147/COPD.S86455

17. 但不同量表面向不盡相同
如何定義成效？
Eur Respir J 2015; 46: 1625–1635
Am J Respir Crit Care Med. 2013 Oct 15;188(8):e13-64.
喘的程度？
累的程度？

18. Patients
患者
Payers
給付方
Providers
醫療
提供方

19. Profiling differential response to
pulmonary rehabilitation in COPD
by self-organizing maps
Eur Respir J 2015; 46: 1625–1635

20. 跟預後/風險/安全性受重視程度
>> 方便/使用者體驗

22. Radiology. 2017 Aug;284(2):574-582. doi:
10.1148/radiol.2017162326. Epub 2017 Apr 24.

23. Finding gaps within
the healthcare system
Deep learning as a general
purpose technology

30. 把輸入的訊號放這裡
把答案放這邊

31. 權重電腦自己會去 ”學”

32. ICH?

33. ICH?

34. ICH?

36. 過去這一步最傷腦筋

37. 現在讓電腦自己想辦法

38. Black-box methods?
Not knowing how the prediction came from...

39. How to trust the model is
making reasonable predictions
in general?

40. https://arxiv.org/pdf/1602.04938.pdf

42. Interpretable
Trust
- Not just how often it is right
- But also which examples it is right ?
Causality
Transferability
- capacity to generalize to
unfamiliar situations
Being fair
& Ethical

43. Properties of interpretable models
1. Transparency
How does the model work?
2. Post-hoc interpretability
Besides the prediction, what else can
the model tell me?
 Learning the model locally around the prediction

44. Transparency
- Simulatability (entire model level)
Is it a “simple” model ?
Easy to put together a prediction by hand?

45. Transparency
- Decomposability (parameters level)
Is it easy to understand what
these coefficients mean?

46. Transparency
- Algorithmic transparency
(algorithm level)
The model learns by minimizing error

47. Post-hoc
interpretability
- Visualizing
uncertainty
We Need Bayesian Deep Learning for Safe
AI
https://goo.gl/oQAepZ
https://goo.gl/XEKbM9

48. Post-hoc interpretability
- Visualizing relevance
http://www.explain-ai.org/
https://arxiv.org/abs/1708.08296

49. Post-hoc interpretability
- Local Interpretable Model-agnostic
Explanations (LIME)
https://arxiv.org/pdf/1602.04938.pdf

50. Post-hoc interpretability
- Local Interpretable Model-agnostic
Explanations (LIME)
https://arxiv.org/pdf/1602.04938.pdf

51. Explanatory power =/= Predictive power

52. Construct X
Construct Y
X causes Y by
mechanism F
where F(X) = Y
Measurement x
Measurement y
model f maps x to y
via f(x) = y

53. Construct X
Construct Y
X causes Y by
mechanism F
where F(X) = Y
Measurement x
Measurement y
model f maps x to y
via f(x) = y
Explanation
match f to F

54. Construct X
Construct Y
X causes Y by
mechanism F
where F(X) = Y
Measurement x
Measurement y
model f maps x to y
via f(x) = y
Prediction
use f to generate y

55. + residual
Good fit of known data{X,y} to model
不表示 model 會在 {Xnew, ynew} 表現好

56. Predictive modeling
- Cross validation

58. Measurements are not accurate
representations of their underlying
constructs
Results in difference between prediction vs. explanation

59. Explanatory
models
Predictory models

60. Predictory models
Explanatory
models
“You know”
“You can tell”
You know more than you can tell
= Polanyi's Paradox

61. Model captures correlation relationship
Sufficient for explanation

62. But great variance in y
 Poor practical utility

63. Reduce overall error
→ Prediction goal
Reduce bias
→ Explanation goal
https://arxiv.org/pdf/1101.0891.pdf

64. “A good model”
Bias = 0, while exhibiting minimal overall error
= 0minimal

65. It is possible to reduce variance by increasing bias
 And still resulting in reduced overall error
Loss of model explanatory power → Increased predictive power
https://arxiv.org/pdf/1101.0891.pdf

66. Predictive power
(How does model perform in out-samples?)
Explanatory power
(Given theory, how does the sample data fit?)
Two dimensions

67. Good experimental design
(Randomized control trials
 Remove confounding)
In-sample model fitting
 Test hypothesis

68. 找到致病因素
 藥到病除
eg. 感染症
多重致病因素
(Multifactorial etiologies)
 複雜的因子間交互作用
eg. Cancer, Allergy, Pain, Metabolic
syndrome …
不適合實驗介入的研究
eg. Economic analysis
以Observational data為主

69. Observational data
(Complex interaction e.g. confounding)
In-sample model fitting
 Misuse?? Need assumptions??

70. Observational data
(Complex interaction e.g. confounding)
Out-sample, predictive modeling
Allows modeling non-linear relationships
 May have more utility?

71. https://arxiv.org/abs/1606.00931
Hazard function
(Covariates x at t time, treatment i)

72. The danger of adversarial examples
https://goo.gl/zA4W83

74. Adversarial examples are robust
https://goo.gl/zA4W83

75. https://blogs.fda.gov/fdavoice/index.php/2017/06/fostering-
medical-innovation-a-plan-for-digital-health-devices/

76. Software as a medical
device (SaMD)
軟體醫材

77. 軟體醫材 SaMD
• 使用於一種或多種醫療用途而非做為硬體
醫療器材的一部分之軟體
• 軟體已不再單純為了驅動或控制硬體醫材
存在，被侷限於硬體醫材上，而是成為一
種可以獨立運作的個體
https://technews.tw/2016/11/30/fda-samd-specification/

78. https://goo.gl/6ZqabL

80. Take home message
• Unmet needs + Market
– 3P: Patient, Payer, Provider
– 預後/風險/安全性 > 方便/使用者體驗
• Model Interpretability
– Transparency vs. Post-hoc interpretability
• Explanation =/= Prediction
• Predictive modeling for observational data
• Software as a Medical Device(SaMD)

81. Thank you
peishen.wu@gmail.com