1. Informatics in
Disease
Management:
What will the
Future bring?
Michael Hogarth, MD, FACMI, FACP
Clinical Research Information Officer, and
Director of Biomedical Informatics, Altman
Clinical Translational Research Institute
University of California San Diego Health

2. Informatics and Disease Management -
here comes machine learning (ML) and
artificial intelligence (AI)!

3. FYI: We have been using “machine learning”
for over 200 years
http://www.andreykurenkov.com/writing/ai/a-brief-history-of-neural-nets-and-deep-learning/

4. So what’s new?
• most AI/ML algorithms are not new
• why is “machine learning” and “deep learning” a new revolution
in the use of AI?
https://mc.ai/awesome-ai-the-guide-to-master-artificial-intelligence/

5. Key Informatics Trends in 2019...
1. HITECH and EHR adoption now at
over 90%
– has made massive amounts of
healthcare data (“dirta”)
available
– fuel for “machine learning”
based predictive systems
2. High performance computing is
highly commoditized
– Commercial clouds - “data center
at your fingertips”
– example: 11 machine cluster for
6 hours = $3.96 (Jan 2019)
– cheap computing to generate
machine learning models
3. Machine learning engines and
algorithms are commoditized
(ML as a service)

6. The commercial attraction to ML/AI in Healthcare
https://www.healthcarebusinessinsights.com/blog/information-technology/growing-value-ai-machine-learning-healthcare/

7. Why is healthcare such an attractive target for
optimization?

8. Big tech sees significant opportunities
https://healthitanalytics.com

9. Research into ML/AI “models” for various tasks
-- a very hot area!

10. Yes, there is “magical thinking” in this space...
• MD Anderson partnered
with IBM in 2012 to help
oncologists with diagnoses
and choosing therapeutic
courses
• IBM Watson Manager -
“training alongside doctors
to do what they can’t”
• Feb 2017 - project canceled
after MD Anderson had
already paid IBM over $39M
(originally priced at
$2.4M)!!!

11. But don’t throw out the baby with the bath water...
Using ML on 36 cases evenly
split between AML and
normal, an algorithm
correctly distinguished
disease progression in 90% of
cases of relapsing AML, and
was 100% correct in
distinguishing between AML
and normal bone marrow

12. How does machine learning work?
https://www.edureka.co/blog/what-is-machine-learning/

13. Machine Learning Approaches
https://www.kdnuggets.com/2017/11/3-different-types-machine-learning.html

14. ML Basics: Supervised
• training data set
• data used to “train the
model”
• Once ‘trained’, the
model can make
predictions or suggest
decisions with new
data inputs
https://bigdata-madesimple.com/machine-learning-explained-understanding-supervised-unsupervised-and-reinforcement-learning/

15. ML Basics: Unsupervised
• The ML model
‘learns’ through self-
finding structures or
associations in the
data
• Will automatically
assemble “clusters”
based on variables
available in the data
set
https://bigdata-madesimple.com/machine-learning-explained-understanding-supervised-unsupervised-and-reinforcement-learning/

16. ML Basics: Reinforcement Learning
• Paradigm is a “software
agent” that attempts to
achieve as close to the
“goal state” as possible.
• Goal state = best outcome
• Follows a concept of “hit
and trial” where the agent
is rewarded or penalized
(using points) for a
particular action or
answer.
• It attempts to ‘learn’ using
hit/trial and can then
predict with new data
https://bigdata-madesimple.com/machine-learning-explained-understanding-supervised-unsupervised-and-reinforcement-learning/

17. Artificial Neural Networks (ANNs)
• AI system loosely based on biological
neural networks in brains
• not an algorithm - its a framework for
machine learning algorithms that work
by using multi-weighted inputs across
multiple collaborating nodes to process
data inputs
• can be supervised or unsupervised ML
• first neural networks were implemented
in circuits
– neurophysiologist Warren McCulloch and
mathematician Walter Pitts wrote paper
theorizing how neurons might work in a
network with weighted inputs
– first tested in circuitry - Mark I Perceptron
– Software based NN’s became known as
“artificial neural networks”
Mark I Perceptron with patchboard
to connect nodes, potentiometers as
weights

18. “Deep” Learning
What is “deep learning”?
● uses a multi-layered “artificial neural
network”
● first layer (captures raw data -- input
layer)
● “hidden layers” -- integrate the values of
the raw input nodes to derive
“transformed features”
● predicts outcomes or classifies based on
transformed features

19. “Convolutional” Neural Networks (CNN)
• a type of multi-layered (deep) ANN designed for recognition of visual
features
• originally devised in 1988 by Yann LeCun at AT&T Labs to recognize
handwritten digits
• inspired by the connectivity pattern seen between neurons in the
visual cortex
• Convolution layers apply a convolution operation to the input, passing
result to next layer.
• Convolution operation emulates response of an individual neuron to
visual stimuli. Pooling coalesces outputs from one layer into a single
neuron in the next layer
• significantly resurging interest in CNNs due to:
– availability of labeled digital image sets (large training sets)
– high performance computing with graphical processing units
(GPU) necessary for the significant computation required

20. Machine Learning (ML) image
interpretation - supervised learning

21. Convolutional (deep) Neural Network and Dermatology
• trained a CNN with 1.29M
images of 2,032 different
conditions from 18
clinician-curated
repositories
• tested CNN based classifier
on 376 cases, head-to-head
with 21 board-certified
dermatologists on two
scenarios with real biopsy-
proven lesions
– differentiating benign
seborrheic keratoses from
keratinocyte carcinomas
(n=135)
– differentiating malignant
melanoma from benign nevi
(n=130;n=111)
• Results: CNN performance
was as good as the
dermatologists
(all AUCs over 0.91)!

22. Convolutional (deep) Neural Network and Echocardiography
“view classification”
• trained a CNN with 267
transthoracic echo studies
(200,000 images) to
differentiate 15 different
standard echo views (12
video, 3 still image views -
parasternal long axis, right
ventricular inflow, short
axis at mitral level, etc..)
• tested with 27 echo studies
(20,000 images)
– video views -- 97.8%
accurate
– still images - 91.7%
accurate
• 4 board certified
echocardiographers on the
27 echo studies --- 79.4%
average accuracy

23. Deep Learning and Interpretation of Retinal Images for
diabetic retinopathy
• trained a ML systems
with 494,661 retinal
images
• validated (tested) using
dataset of 71,896
images from 14,880
patients
• detection of vision-
threatening diabetic
retinopathy: AUC 0.958
(95% CI)
• detection of referable
diabetic retinopathy:
AUC=0.936(95% CI)

24. Reinforcement Learning in Sepsis - the “AI Clinician”
• developed an AI Clinician computational
model using reinforcement learning to
suggest optimal treatment of sepsis
regarding fluid resuscitation and pressor
administration
• AI clinician’s goal - maximize the
probability of a good outcome
• Developed using two large ICU databases
– MIMIC III and eICU Research
Institute Database (eRI)
– 17,083 admissions from MIMIC
– 79,073 admissions from eRI
• Performance:
– “the value of the AI Clinician’s
selected treatment is, on average,
reliably higher than human clinicians
– Validation done on a separate
dataset of cases.
– Found that cases where the
clinician action most closely
matched what AI Clinician’s would
have done had the lowest
mortality!

25. Artificial Intelligence Sepsis Expert (AISE)
• Nemati and colleagues at
Emory trained and validated on
31,000 Emory ICU admissions
– used 60 features: ECG, BP, HR,
MAP, SpO2, temp, GCS, FiO2,
Chem7 analytes, ABG, lactate,
etc..
• AISE predicted sepsis an
average of 4-8hrs in advance
(AUC of 0.85) of non-assisted
recognition of impending
sepsis
(using Sepsis 3 definition)

26. Regulating AI-based Clinical Decision Support -
is the FDA ready?

27. 2017 FDA guidance on decision support
software systems
• Clinical Decision Support (CDS) vs.
Patient Decision Support (PDS)
• With guidance from 21st Century Cures
Act, FDA introduced new guidance to
make it easier for software developers to
obtain clearance (to spark innovation)
– Low risk =(weight management,
mindfulness tools, etc.. not under
FDA oversight
– Products intended for the
diagnosis, cure, mitigation,
prevention, or treatment of a
condition will fall under FDA
oversight
– Software intended to process or
analyze medical images, signals
from diagnostic devices (ECG, EEG)
will continue under FDA oversight

28. Recent FDA Decisions for AI Based Systems
- De Novo: regulatory pathway for low-to-moderate risk
devices of a new type for which there is no legally
marketed predicate device with substantial equivalence
- 510(k): a pre-market submission to FDA to demonstrate
the device is at least a safe and effective (equivalent) to
a legally marketed device that is not subject to
premarket approval
- Class II: moderate-to-high
risk device
- Special controls:
- performance
standards
- post-market
surveillance
- special labeling

29. This has now also captured the attention of the
legal profession...
https://www.americanbar.org/groups/health_law/publications/aba_health_esource/2016-2017/october2017/machinelearning/

30. The future of disease management
could be bright...
https://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai100report10032016fnl_
singles.pdf

31. or it could go horribly wrong...
http://blog.petrieflom.law.harvard.edu/2017/10/26/

32. One thing is certain, AI has become an
integral part of society -- it is here to stay
Domino’s Pizza delivered by autonomous driving vehicle

33. A common question:
“When will we be “replaced” by smart machines?”
Surgeon
Nephrologist

34. Questions?
Tioga Pass, Yosemite