Artificial intelligence and machine learning techniques like logistic regression, collaborative filtering, and deep learning can be applied in healthcare for tasks like predicting patient outcomes, automating care coordination, and assisting disease diagnosis. However, challenges remain around limited data availability in healthcare, the complexity of medical data, and the lack of data scientists currently working in the field. With more healthcare data and interdisciplinary collaboration between data scientists and medical experts, artificial intelligence has strong potential to positively impact areas like early detection of sepsis and reducing insurance claim rejections.

1. Artificial Intelligence
and
Healthcare

2. What if...

3. ...instead of

4. What if...

5. ...instead of

6. What if...

7. ...instead of

8. Patient Journey

9. Use this data

10. Spreadsheets

11. EHR/EMR

12. Log Files

13. Unstructured text

14. Imaging data

16. What is data science?

23. Data Visualization

24. Data Visualization

25. Data Visualization

26. Extract and
Interpret Data
Extracting value from data
Extraction
Secure file transfers protocol
Anonymizing P.H.I.
Gigabytes, Terabytes, &
Petabytes
The bigger the better!
Interpretation
Categorical variables:
‘male’,‘yellow’,‘jaundice’,‘IVIg’
Numerical variables:
82, 1.325, 1.024 E^31
Sparsity:
Null, NaN, 0
How many variables?
Data has Dimensions!

27. 80%
The proportion of our time
that’s spent cleaning the
data.
Before:
(Nonsense)
After:
(Insight!)
Cleaning and
Munging
the Data
Extract and
Interpret Data
Extracting value from data

28. Statistical inference:
Deducing properties of an underlying
probability distribution by analysis of data.
Before:
After:
Cleaning and
Munging
the Data
Extract and
Interpret Data
Multivariate Inferential
Statistics
𝛼
Parameters
Extracting value from data

29. Cleaning and
Munging
the Data
Extract and
Interpret Data
Multivariate Inferential
Statistics
Machine Learning
Algorithm
s
Machine learning:
The latest field of computer science.
Giving computers the ability to learn
without being explicitly programmed
How does it work?
Algorithms iteratively and implicitly
learn the data.
The Result:
A model is developed.
Insights made go deeper, beyond
basic descriptive analysis
Extracting value from data

30. Pattern Recognition
Systems
Hidden Insights
Discovery
Analytics and
Visualizations
Data scientists provide...
Recommendation
Engines
Artificial Intelligence
Networks

31. Data science is not...

38. Machine Learning

39. How do we predict whether
something will or will not happen?

40. Logistic regression: Simple example
Sex Weight Diabetes
M 160 N
M 160 N
M 160 Y
M 160 ?

41. Logistic regression: Simple example
Sex Weight Diabetes
M 160 N
M 160 N
M 160 Y
M 160 N

42. Logistic regression
Logistic Regression is a way of
measuring relationships
in past data
to predict characteristics
about an individual.

43. Logistic regression: Unsimplified
Sex Weight Ethnic City BP Smoker Diabetes
F 142 H M 130/80 Y Y
M 178 A F 170/90 N N
F 203 C M 130/90 N Y
M 187 A P 170/90 Y Y
F 162 A P 170/90 Y N
F 120 H M 80/50 N Y
M 263 A F 80/50 Y Y
M 207 C P 130/80 N

44. Care coordination
Automatically assign care tasks
based upon a patient’s diagnosis
or living conditions

45. Disease as predictor
Predict other potential health
issues if someone has a
particular disease along with
other personal characteristics

46. Treatment efficacy
Predict the failure of a
particular treatment for high
risk patients or patients from
certain demographics

47. Patient scheduling
Predict patient no-shows given a
historical log of visits

48. Binary
Limit of logistic regression

49. How do we combine the
characteristics of similar people
to make accurate predictions
about individuals?

50. Collaborative filtering: simplified
Sex Weight Diabetes
M 159 Type1
M 160 Type2
M 158 Type3
M 160 ?

51. Collaborative filtering: simplified
Sex Weight Diabetes
M 159 Type1
M 160 Type2
M 158 Type3
M 160 Type2

52. Collaborative filtering
Collaborative filtering is a way
of collecting and weighing
similarities about the population to
make predictions about an
individual

53. Collaborative filtering: Unsimplified
Sex Weight Ethnic City BP Smoker COPD Diabetes Heart Disease GI
F 142 H M 130/80 Y 2 4 3
M 178 A F 170/90 N 5 1
F 203 C M 130/90 N 1 3 2
M 187 A P 170/90 Y 3 2
F 162 A P 170/90 Y 3 4 1
F 120 H M 80/50 N 4 2
M 263 A F 80/50 Y 2 5 2 4
M 207 C P 130/80 N 5 4 3

54. Collaborative filtering: Large data

55. Claim rejection
Determine the probability of any
particular claim being rejected
an insurance company

56. Medication adherence
Identify cohorts of patients
who may not adhere to their
medication plan

57. Identify deductibles, copays, and
patient’s share of medical costs,
even when there are a wide
variety of insurance coverage
options
Insurance coverage

58. Gray sheep
Limit of collaborative filtering

59. How do we teach a computer to
learn from examples?

60. Deep learning: Simple example

61. Deep learning: Simple example

62. Deep learning
Deep learning is inspired by how our brain
works and is based on learned, rather than
programmed, parameters which can be in the
order of few thousands to 100+ million

63. Deep learning
High Level

64. Deep learning
High Level Low Level

65. Deep learning

66. Deep learning

67. Treatment ranking
Automate the search for evidence-
based, patient-centric treatment
options

68. Clinical support
Assist doctors in identifying
disease in images and
reduce diagnosis error rate

69. Disease identification
Using EHR data, identify
and predict patients with
high risk of heart failure

70. Blackbox
Limit of deep learning

71. Challenges of doing data science

72. Data scientists are in demand
By 2018, the gap in the supply of data scientists
versus the demand will be 50%

73. Data scientists are scarce

74. Data scientists are expensive

75. Just 2% of all data
scientists work in
healthcare
Data scientists and healthcare

76. Early warning of sepsis
Impact today

77. Predict insurance claim rejections
Impact today

78. Radiomics
Impact today

79. Importance of data + you

80. Listening to the data is important… but so is
experience and intuition. After all, what is intuition
at its best but large amounts of data of all kinds
filtered through a human brain rather than a math
model? – Steve Lohr

81. Thank you!

89. If you are reading this, there is a
99% chance you are not looking at
your phone right now.
some guy