Big Data and Artificial Intelligence in Critical Care Anesthesia and Intensive Care San Raffaele Hospital, Milan, Italy Vita-Salute San Raffaele University, Milan, Italy Follow us on Twitter, Facebook and Instagram @SRAnesthesiaICU

1. Big Data and
Artificial Intelligence
in Critical Care
@SRAnesthesiaICUfollowuson
Tommaso Scquizzato
@tscquizzato

2. Healthcare
Big Data
@ t s c q u i z z a t o

3. 2314 exabytes
@ t s c q u i z z a t o

4. 2314 exabytes
@ t s c q u i z z a t o
1 exabytes = 1 billion gigabytes

5. Open-Access Critical
Care Databases
@ t s c q u i z z a t o

6. ARTIFICIAL INTELLIGENCE
Machine
Learning
Computer
vision Robotics Language
processing

7. ARTIFICIAL INTELLIGENCE
Machine
Learning
Computer
vision Robotics Language
processing

8. ARTIFICIAL INTELLIGENCE
Machine Learning
Supervised
learning
Unsupervised
learning
Reinforcement
learning
Machine learning is a subfield of
artificial intelligence.

9. “The science of making
machines do things that
would require intelligence
if done by men”
– Marvin Minsky, Computer Scientist

10. Potential applications
of AI in medicine
@ t s c q u i z z a t o

11. automated radiology reporting

14. predictive analytics
?

15. Rapidly become a
subject of great interest
and intense debate
@ t s c q u i z z a t o

16. The role of AI
@ t s c q u i z z a t o

17. helping clinicians
make better and more
personalised decisions

18. boosting workflow
and reducing healthcare
expenditures

19. Technology
readiness level
• method for estimating the maturity of
technologies during the acquisition phase of a
program
• developed at NASA during the 1970s

21. Implementation
Bottleneck
• TRL of supervised learning models for
intensive care medicine is 4 to 7
• The majority of published models have never
been tested or deployed in clinical practice

22. The role of AI
in Intensive Care
@ t s c q u i z z a t o

23. 1970 2018
Increase in number
of publications

24. applications of AI
are mainly limited to
machine learning

26. ICU Patients Clinical (big) data Intensivist
Treatments

27. ICU Patients Clinical (big) data Intensivist
Model
Treatments
AI

28. What is learning
for a machine?
A machine is said to be learning from past
Experiences (data in) if it’s Performance in a
given Task improves with the Experience.

29. What is
machine learning?

30. a collection of data analysis and
modelling techniques aiming at
generating knowledge from data

31. Machine Learning
Algorithms
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning

32. Machine Learning
Algorithms
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning
Input
data with labels
Output
mapping
Input
data without labels
Output
classes
Input
states and actions
Output
state/action

33. Supervised
Learning

34. Supervised
Learning
• relies on labelled data for model training
• learn a mapping function for a dataset with an
existing classification
• focused on predictive tasks

35. Supervised
Learning
Input
data with labels
Output
mapping

36. What do we want to
predict in the ICU?
risk of mortality
readmission
length of stay
early deterioration
diagnosis sepsis

38. SPAM
INBOX
INBOX
SPAM
INBOX
Spam
Detection

39. Machine Learning
Model
SPAM
INBOX
SPAM
INBOX
INBOX
SPAM
INBOX
?
new email
arrives

40. Example
Sepsis

41. 3rd cause of mortality

42. best treatment
strategy remains
uncertain

45. Sepsis
Predictive
model
Not sepsis
Model
ML

46. Sepsis
Model
ML

47. Pacemaker-ID
App

48. 1013 chest X-rays

49. TYPE A
TYPE C
TYPE A
TYPE B
TYPE B
TYPE B
TYPE C
TYPE A
classification

50. Training Validation
TYPE A
TYPE C
TYPE A
TYPE B
TYPE D
TYPE B
TYPE C
TYPE A

51. 94%
accuracy

54. Prediction of
outcomes
Mortality
(Lee, Maslove, & Dubin, 2015;
Pirracchio et al., 2015)
Mortality, length of stay,
readmission, diagnoses
(Rajkomar et al., 2018)
Prediction
of events
Deterioration
(Koyner, Carey, Edelson, & Churpek,
2018)
Cardiac arrest
(Kwon, Lee, Lee, Lee, & Park, 2018)
Sepsis
(Mao et al., 2018; Nemati et al., 2018;
Wyk, Khojandi, & Kamaleswaran, 2019)
Fluid responsiveness
(Zhang, Ho, & Hong, 2019)
Acute Kidney Injury
(Koyner et al., 2018)
Readiness for discharge
(McWilliams et al., 2019)
Others
Reducing false alarms
(Hever et al., 2019)
Classifying
dysrhythmias
(Rajpurkar, Hannun, Haghpanahi, Bourn,
& Ng, 2017)
Virtual scribe
(Liu, 2018)
Reducing unnecessary
lab tests
(Cismondi et al., 2013)
Estimating duration of
ICU trials in certain
populations
(Shrime et al., 2016)
Source: Komorowski, Intensive Care Medicine 2019

55. Unsupervised
Learning

56. Unsupervised
Learning
• discovery of subclasses in a dataset
• categorize an unlabeled dataset based on
some hidden features in the data
• define patient subgroups and phenotypes

57. group unsorted information
according to similarities,patterns
and differences without any prior
training of data

58. Unsupervised
Learning
Input
data without labels
Output
classes

59. ICU Patients
Phenotype 1
Phenotype 2

60. Phenotype 2
Model
ML

61. Phenotyping sepsis
(Antcliffe et al., 2018; Davenport et al., 2016; Gårdlund et al., 2018; Knox,
Lanspa, Kuttler, Brewer, & Brown, 2015)
Phenotyping ARDS
(Shankar-Hari & McAuley, 2017)
Phenotyping hypotensive episodes
(Dai, Lokhandwala, Long, Mark, & Lehman, 2017)
Source: Komorowski, Intensive Care Medicine 2019

62. Reinforcement
Learning

63. Reinforcement
Learning
•
•
the most immature (TRL = 3-4)
learn policies for decision-making

64. Reinforcement
Learning
Input
states and actions
Output
state/action

65. a virtual agent is trained to learn
an optimal set of rules in order to
maximise some reward

66. Medical decision is a
reinforcement learning problem

67. Physician
Patient
Action
medical
intervention
State
patient
condition
Reward
mortality
benefit

68. Decision A
Model
ML
Decision B
Decision C

69. Decision B
is the most
optimal
Model
ML

70. Optimising fluid and vasopressors in sepsis
(Komorowski, Celi, Badawi, Gordon, & Faisal, 2018)
Optimising sedation and mechanical
ventilation
(Prasad, Cheng, Chivers, Draugelis, & Engelhardt, 2017; Yu, Liu, & Zhao,
2019)
Optimising heparin dosing
(Nemati, Ghassemi, & Clifford, 2016)
Optimising insulin
(Bothe et al., 2013)
Source: Komorowski, Intensive Care Medicine 2019

71. Software for decision
support is a medical
device

72. The future is
physicians + engineers + machines

73. Doctors won’t be replaced by
artificial intelligence