A talk by Dr. Diana Coman Schmid, Personalized Health Data Services Manager. Scientific IT Services, ETHZ. Held on the occasion of Geek Girls Carrots' meetup on the 10th of March 2018.

1. ||ID | SIS 8 March 2018 1Diana Coman Schmid
Artificial Intelligence in Personalized Health
Dr. Diana Coman Schmid
Personalized Health Data Services Manager
Scientific IT Services, ETHZ
diana.coman@id.ethz.ch

2. ||ID | SIS 8 March 2018Diana Coman Schmid 2
The speaker > in brief

3. ||ID | SIS
www.cancer.govSchork, 2015 Nature
8 March 2018Diana Coman Schmid 3
Personalized Health > the Need
Imprecision Medicine Precision Medicine

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www.cancer.gov
8 March 2018Diana Coman Schmid 4
Personalized Health > the Need
Precision Medicine Personalized Health
https://blogs.cdc.gov/genomics/2015/03/02/precision-­public/
§ beyond individualized treatment of sick individuals
§ promotes Health at Population level

5. ||ID | SIS 8 March 2018Diana Coman Schmid 5
Personalized Health (PH) > Data Driven
What is new is Data Driven Personalized treatment and prevention >> PH
Data: large volumes (unstructured) that exceed Storage and Computation capacity of standard PCs
Rare Disease Cancer Complex Disease
§ < 1 in 2,000 people (Europe)
§ 7,000 rare diseases (+5/week)
§ 80 % have a genetic component
§ 100,000 Genomes Project (UK)
20-­25 % are “potentially
actionable” diagnoses

6. ||ID | SIS 8 March 2018Diana Coman Schmid 6
Personalized Health > it helped them
What is new is Data Driven Personalized treatment and prevention >> PH
Data: large volumes (unstructured) that exceed Storage and Computation capacity of standard PCs
Rare Disease
§ < 1 in 2,000 people (Europe)
§ 7,000 rare diseases (+5/week)
§ 80 % have a genetic component
§ 100,000 Genomes Project (UK)
20-­25 % are “potentially
actionable” diagnoses
Jessica suffers from a rare condition that
was diagnosed through DNA analysis
§ Jessica: four-­year-­old
§ epilepsy, poorly coordinated
movement, slow mental
development
§ mutation in the gene SLC2A1
§ 1 month on a special diet:
improvement on her speech,
energy levels and general
steadiness (100K Genomes, UK)
https://www.ft.com/content/d2e21cea-­d684-­11e6-­944b-­e7eb37a6aa8e

7. ||ID | SIS 8 March 2018Diana Coman Schmid 7
Personalized Health > it helped them
What is new is Data Driven Personalized treatment and prevention >> PH
Data: large volumes (unstructured) that exceed Storage and Computation capacity of standard PCs
Rare Disease Cancer Complex Disease
§ e.g., type 2 diabetes
§ multifactorial (e.g., life style)
§ heterogenous
§ >150 common genetic variants
§ gut microbiome content
§ complex health record data
§ 39–95 % patients: “potentially
actionable” diagnoses
§ 570 studies, 32,149 patients1
extended overall
survival (median 13.7
months versus 8.9
months;; P = 0.0001)
§ < 1 in 2,000 people (Europe)
§ 7,000 rare diseases (+5/week)
§ 80 % have a genetic component
§ 100,000 Genomes Project (UK)
20-­25 % are “potentially
actionable” diagnoses
Carrasco-­Ramiro et al., 2017 Nature

8. ||ID | SIS 8 March 2018Diana Coman Schmid 8
Artificial Intelligence (AI) in Personalized Health > because of Data
What is new is Data Driven Personalized treatment and prevention >> PH
Data: large volumes (unstructured) that exceed Storage and Computation capacity of standard PCs >> AI
Google DeepMind IBM Watson Health
§ 2018: predicts heart disease
by looking at your eyes2
§ AI (n=297,360;; retina scans,
general medical data)
§ Accurate predictions (70 %
vs. 72 % standard blood tests)
§ Quicker (no blood tests)
§ Needs clinical testing
2 Poplin et al., 2018 Nature
§ 90 % concordance with tumor
board recommendation for
breast cancer3
§ AI 40 sec. vs. Doctor 12 min.
§ one patient (glioblastoma),
whole genome and RNA
sequencing4
§ AI 10 min. vs. Bioinformatician
160 h.
3 http://www.ascopost.com/News/44214
4 Wrzeszczynski et al., 2017 Neurology Genetics
Keep an eye on5:
§ Careskore (US): Zeus
§ Zephyr Health (US, UK, IN):
Illuminate
§ Oncora Medical (US): Unity,
Focus, Acumen
§ Atomwise (US): AtomNet
§ Sentrian (UK,US): Remote
Patient Intelligence
5 http://medicalfuturist.com/top-­artificial-­intelligence-­
companies-­in-­healthcare/

9. ||ID | SIS 8 March 2018Diana Coman Schmid 9
PH can only happen in presence of AI > because of Data
§ omics (gen-­, epigen-­, transcript-­, etc.) >> Bioinformatics: domain specific and/or integrative analyses
§ eHealth (electronic health records, histopathological and radiological images, etc.) >> AI: machine learning
§ citizen data (digital footprints, mobile apps) >> AI: machine learning
“We are drowning in information but starved for knowledge” John Naisbitt (1982)
16,439,169,142,390,369
(2018)
80 % Unstructured Data
20 % Structured Data

10. ||ID | SIS 8 March 2018Diana Coman Schmid 10
Artificial Intelligence: it’s efficient, smart & quick but is it Strong?
Machine Learning:
The use of algorithms that
find patterns in data without
explicit instruction (Hutson
2017, Science)
(Deep) Neural Networks:
A highly abstracted and
simplified model of the human
brain used in machine learning
(Hutson 2017, Science)
Deep learning: computers, not their human programmers, find the meaningful relationships embedded in large
volumes of (unstructured) data (Webb 2018, Nature)
Human cognitive features:
Perceive
Reason
Learn
Interact with the environment
Problem solving
Ability to understand
Think
Language
Creative
(Wikipedia)
Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence displayed
by humans and other animals (Wikipedia).
https://becominghuman.ai/
Microbiome

11. ||ID | SIS 8 March 2018Diana Coman Schmid 11
Personalized Health: in action worldwide
2015 The Precision
Medicine Initiative
(National Institute of
Health, USA): 1
million whole human
genomes $215 million
1996 deCODE Iceland:
genotypic and medical data
from more than 160,000 ( >
50 % of the adult icelandic
population)
2016 China Precision
Medicine Initiative: 100
million whole human
genomes $ 9.2 billion
ES, 2000UK, 2012
DE, 2013
CH, 2017
2015 Australia’s “My
Health Record”: 5.4
million EHR;; extend to
all australians

12. ||ID | SIS 8 March 2018Diana Coman Schmid 12
Research in Personalized Health needs Hybrids
Why: prevent, diagnose and treat unfavorable health conditions more precisely
Who: hybrid ecosystem: medical, IT, computational and statistical experts
How: security;; “Big Data”: storage, management and analysis;; data exchange & interoperability
* https://understandingpatientdata.org.uk/

13. ||ID | SIS 8 March 2018Diana Coman Schmid 13
Personalized Health Data Services at the SIS, ETHZ
Leonhard Med: dedicated computing
environment for secure data driven
biomedical research
Leonhard Med
§ Legal: ETH;; ISO/IEC 27001;; Federal
Act on Research involving Human
Being;; Federal Act on Data
Protection;; EU-­GDPR;; HIPAA
§ Multi-­factor authentication
§ Data encryption
§ Controlled in/out traffic
§ Auditable user activity (logged)

14. ||ID | SIS 8 March 2018Diana Coman Schmid 14
Switzerland > Complex PH Ecosystem & AI incubator
Zurich
Bern
Geneva Lausanne
Basel
§ Distributed Data Sources (Data
Lakes)
§ Secure Data Transfer
§ Interoperability
§ Metadata Harmonization
§ Workflows & Containers
§ Powerful Computing Platforms
(AI tools, CDW, HPC, GPU) in
Secure environments

15. ||ID | SIS 8 March 2018Diana Coman Schmid 15
People will have Virtual Twins to help monitor their health ?
EPFL: “Health EU” Proposal, 2018
https://actu.epfl.ch/news/with-­health-­eu-­everyone-­will-­have-­an-­avatar-­to-­man/

16. ||ID | SIS 8 March 2018Diana Coman Schmid 16
EPFL: “Health EU” Proposal, 2018
§ Data driven PH Research is happening worldwide as we speak
§ Artificial Intelligence is instrumental for PH
§ Hybrid expertise is paramount
§ In clinical practice involving PH, AI assists Human
Concluding remarks

17. ||ID | SIS 8 March 2018Diana Coman Schmid 17
EPFL: “Health EU” Proposal, 2018
Acknowledgement
Thank you !
We are hiring: https://apply.refline.ch/845721/6023/pub/1/index.html
SIS, ETHZ colleagues:
Thomas Wüst
Bernd Rinn
Christian Bolliger
Michal Okoniewski
GeekGirlsCarrots, Zurich Swiss Re
Susanne Müller