Hasso Plattner gave this presentation about how in-memory technology can support analysis of big medical data at the 2013 World Health Summit in Berlin. It consists real-world examples showing latest results of partners, such as the Hasso Plattner Institute, Stanford, Charité, and SAP. For background details, please refer to http://we.analyzegenomes.com

1. Gaining
Time
–
Real-­‐.me
Analysis
of
Big
Medical
Data
Prof.
Dr.
Hasso
Pla,ner
Chairman
of
the
Supervisory
Board,
SAP
AG
and
Professor,
Hasso
Pla,ner
Ins?tute

2. Growing Data Volumes in
Diverse Healthcare Systems
Human
genome/biological
data
800
MB
per
full
genome
15
PB+
in
databases
of
leading
ins?tutes
Human
proteome
160
Mil.
data
points
(2.4
GB)
per
sample
3.7
TB
raw
proteome
data
in
ProteomicsDB
Clinical
informa?on
management
systems
OMen
more
than
50
GB
PubMed
biomedical
ar?cle
database
Cancer
pa?ent
records
Medical
sensor
data
160,000
at
NCT
Heidelberg
Prescrip?on
data
1.5
Bil.
records
from
10,000
doctors
and
10
Mil.
Pa?ents
(100
GB)
23+
Mil.
ar?cles
Scan
of
a
single
organ
in
1s
creates
10GB
of
raw
data
Clinical
trials
Currently
more
than
30,000
recrui?ng
on
ClinicalTrials.gov
2

3. Innovation in Medicine can be Driven
Using a Design Thinking Approach
Clinicians
Researchers
Human
Factors
Business
Factors
Desirability
Administra.on
&
Opera.ons
Staff
Technical
Factors
Viability
Feasibility
3

4. Only
a
Collabora.ve
Effort
can
be
Viable
From
a
Business
Perspec.ve
Clinical
Pharma
Care
Circles
Pa.ents &
Consumers
Payers
SAP
HANA
Research
Desirability
Providers
Viability
Feasibility
4

5. SAP
HANA
is
the
Technology
Enabler
for
This
Vision
Advances
in
Hardware
• Mul?-­‐core
Architectures,
e.g.
16
CPUs
x
10
Cores
on
Each
Node
• Scaling
Across
Servers,
e.g.
100
Nodes
x
160
Cores
• 64
bit
Address
Space
–
12TB
in
Current
Servers
• 25GB/s
Data
Throughput
• Cost-­‐Performance
Ra?o
Improving
A
Advances
in
SoLware
Reduced
Footprint
Multi-Core
Parallelization
Compression
Desirability
No
aggregate
tables
Viability
Federation
Feasibility
Complex
Algorithms
5

6. More
Than
Just
a
Faster
Database,
SAP
HANA
is
a
Revolu.onary
Compu.ng
PlaOorm
+
Desirability
Viability
Feasibility
6

7. Selected
SAP
HANA
Usage
Scenarios
Clinicians
Decision
Support
Researchers
Personalized
Proteome
medicine
Diagnos?cs
Medical
Knowledge
Cockpit
Medical
Explorer
Genomics
for
Personalized
Medicine
SAP
HANA
Prescrip?on
Analysis
Healthcare
Administra.on
Op?mized
Opera?ons
Pa?ent
Management
(IS-­‐H)
Analy?cs
7

8. Research
Genome
Variant
Analysis
For
personalized/preventa?ve
medicine
§
§
Analysis
on
125
variants
in
629
people
Multi-Core
in
parallel;
was
not
Parallelization possible
before
“
”
Researchers
want
to
iden?fy
and
chart
amount
of
varia?on
in
one
gene
across
a
popula?on
§
Mul.-­‐Core
Paralleliza.on
Full
human
genome
is
3.2
billion
characters
long
With
SAP
HANA,
researchers
can
compare
gene?c
variants
of
diseased
&
healthy
cohorts
in
real-­‐?me
§
Using
SAP
HANA,
Stanford
has
seen
“spectacular”
findings:
Type
2
diabetes
disease
risk
is
very
different
across
popula?ons
"We
have
been
thrilled
to
work
with
SAP
and
HPI
on
a
collabora?on
to
accelerate
DNA
sequence
analysis.
In
our
pilot
projects,
we
are
seeing
drama?c
speedups
in
compu?ng
on
human
genome
varia?on
data
from
many
samples.
We
are
dreaming
of
what
will
soon
be
possible
as
we
8
integrate
phenotype,
genomics,
proteomics,
and
exposome
data
to
empower
complex
trait
mapping
using
millions
of
health
records.”
-­‐
Professor
Carlos
D.
Bustamante
at
the
Stanford
University
School
of
Medicine

9. Proteome-­‐based
Cancer
Diagnos?cs
Plamorm
for
Researchers
and
Clinicians
Research
§
Diagnosis
can
be
done
by
analysing
proteome
“fingerprint”
from
just
one
drop
of
blood
§
Proteome
analysis
yields
very
large
data
sets
(160Mil
data
points/sample)
§
Fingerprint
recogni.on
on
high
resolu?on
data
now
possible
Intui.ve
interface
for
complex
analysis
pipeline
Researchers
can
model
a
detec?on
pipeline
interac?vely
on
SAP
HANA
§
Researchers
can
manipulate
the
detec?on
pipeline
interac?vely
§
Minimally
invasive
diagnos?cs
made
possible
by
large
scale
studies
9

10. ProteomicsDB
www.proteomicsdb.org

11. ProteomicsDB
www.proteomicsdb.org

12. ProteomicsDB
www.proteomicsdb.org

13. ProteomicsDB
www.proteomicsdb.org

14. ProteomicsDB
www.proteomicsdb.org

15. ProteomicsDB
www.proteomicsdb.org

16. ProteomicsDB
www.proteomicsdb.org

17. ProteomicsDB
www.proteomicsdb.org

18. Clinic
Medical
Explorer
Cancer
pa?ent
treatment
and
research
§
§
to
mul?ple
formerly
disjoint
data
sources
Flexible
Analy.cs
t
on
historical
data
Clinical
records
and
inclusion
criteria
are
very
complex
§
Clinical
data
from
different
sources
is
combined
in
one
SAP
HANA
system
§
Unified
access
Oncologists
need
to
find
the
best
treatment
op?on
for
pa?ents
à
Find
pa?ents
eligible
for
clinical
trials
Doctors
can
filter
pa?ent
cohorts
based
on
any
clinical
a,ribute
à
Pa?ents
eligible
for
clinical
trials
can
be
found
in
seconds
“In
the
future
we
would
like
to
use
SAP
HANA
at
every
diagnos?c
and
therapeu?c
step
in
the
fight
against
cancer
as
every
cancer
is
different
18
and
can
vary
immensely
from
one
pa?ent
to
the
next.“
-­‐
Prof.
Dr.
Christof
von
Kalle,
Head
of
Na?onal
Center
for
Tumor
Diseases
Heidelberg,
Germany

19. Medical
Knowledge
Cockpit
Clinic
Relevant
scien?fic
findings
at
a
glance
§
Search
for
affected
genes
in
distributed
and
heterogeneous
data
sources
§
Immediate
explora?on
of
relevant
informa?on,
such
as
§ Gene
descrip?ons,
§ Molecular
impact
and
related
pathways,
§ Scien?fic
publica?ons,
and
§ Suitable
clinical
trials.
Unified
access
to
structured
and
unstructured
data
sources
Automa.c
clinical
trial
matching
using
HANA
text
analysis
features
§
No
manual
search
for
hours
or
days
–
SAP
HANA
translates
manual
searching
into
interac?ve
finding
19

20. Pa?ent
Management
(IS-­‐H)
Analy?cs
Real-­‐?me
analysis
of
hospital
pa?ent
management
data
§
Medical
Controllers
need
to
check
occupancy
for
different
wards
frequently
§
Current
systems
too
slow
for
real-­‐?me
analysis
à
no
what-­‐if
scenarios
possible
§
HANA
made
sub-­‐second
response
?mes
possible
§
Admin
New
analy?cal
applica?ons
can
now
help
drive
cost-­‐savings
and
more
efficient
resource
alloca?on
Flexible
analysis
–
no
need
for
materialized
aggregates
20

21. Admin
Prescrip?on
Data
Analysis
Understanding
the
who,
where,
and
what
of
drug
prescrip?ons
§
Which
is
prescribed
e.g.
for
migraine?
§
Specialists
might
prescribe
different
drugs
than
general
prac??oners
§
SAP
HANA
cloud
system
holds
1.5
Bil.
Prescrip?on
records
for
around
10
Mil.
pa?ents
and
10,000
doctors
§
Data
can
be
explored
and
visualized
interac?vely
with
SAP
Lumira
in
seconds
Answers
in
1
sec.
instead
of
1
hour
Intui.ve
analysis
using
data
graphics
"SAP
Health
Data
on
Demand
reduces
the
?me
it
takes
to
analyze
our
more
than
1.5
bn
data
records
from
1
hour
to
1
second.
As
a
result,
we
21
are
able
to
offer
our
customers
new
online
services,
establish
a
new
business
model
and
generate
addi?onal
revenue.”
-­‐
Franz-­‐Xaver
Thalmeir,
Managing
Director,
Medimed
GmbH

22. Healthcare
Projects
on
SAP
HANA
HANA
helps
gain
?me
and
enables
completely
new
scenarios
Speedups
achieved
Pa?ent
Management
(IS-­‐H)
Analy?cs
50x
(55
seconds
à
800
milliseconds)
Virtual
Pa?ent
Plamorm
5000x
(4
hours
à
2-­‐3
seconds)
Prescrip?on
analysis
3600x
(1
hour
à
1
second)
DNA
Sequence
Alignment
17x
(85
hours
à
5
hours)
Proteome-­‐based
Cancer
Diagnos?cs
22x
(15
minutes
à
40
seconds)
New
usage
scenarios
Medical
Explorer
Genome
Analysis
Clinical
Trial
Matching
ProteomicsDB
Genome
Browser
Biological
Pathway
Analysis
Large
Pa?ent
Cohort
Analysis
HANA
Data
Scien?st
Genome
Data
Processing
and
Pipeline
Modeling
22

23.
Demo
23

24. The
Power
of
Mul.disciplinary
Teams
Only
Strong
Partners
Build
Strong
Co-­‐Opera?ve
Success
Stories
SAP:
Global
SoMware
Vendor
and
Expert
for
Enterprise
Technologies
World-­‐Wide
+
Hasso
PlaYner
Ins.tute:
Academic
Research
Ins?tute
for
IT
Systems
Engineering
+
Carlos
Bustamante
Lab:
Leading
Stanford
Lab
On
Human
Popula?on
Genomics
and
Global
Health
+
Charité
–
Universitätsmedizin
Berlin:
One
of
the
largest
university
hospitals
in
Europe
+
Na.onal
Center
for
Tumor
Diseases
Heidelberg
(NCT):
One
of
the
leading
ins?tu?ons
for
cancer
research
and
pa?ent
care
Design
Thinking
Teams
You
Join Us!
24

25. New
Ways
of
Real-­‐Time
Collabora.ve
Personal
Medicine
al
Me dic r
re
Explo
Thank
you!
25