The document discusses opportunities and challenges in e-governance and mHealth. It provides an overview of mHealth, including how mobile devices can be used for various aspects of healthcare. The document also discusses a vision for machine augmented mindfulness in healthcare, where machines could help monitor health, diagnose conditions, and potentially cure diseases at a molecular level through technologies like gene therapy and wearables. Realizing this vision faces challenges, but also opportunities to help the millions lacking access to proper healthcare through technical research and mobile health applications.

1. Opportuni)es
and
Challenges
in
e-­‐Governance:
mHealth
Case
Study
Vishnu Pendyala

2. A Great Quote
Economy
grows
as
more
and
more
people
join
its
core
echelons.
People
are
the
most
important
economic
resources
at
all
)mes.

3. Agenda
• The
Healthcare
Challenge
and
the
e-­‐
Governance
Opportunity.
• Mo)va)on,
Enabling
Factors
• mHealth:
An
Overview
• Vision:
Machine
Augmented
Mindfulness
• Medical
Diagnosis
• Realizing
the
Vision:
Challenges
• Future
Direc)ons
and
Conclusion

4. Questions for you
• Why
is
technical
research
important
for
effec)ve
e-­‐governance?
• How
is
mHealth
related
to
Machine
Learning,
Big
Data
Analy)cs
and
Internet
of
Things?
• How
can
the
millions
who
do
not
have
access
to
proper
healthcare
be
provisioned
with
it?
• Can
machines
replace
the
doctor
eventually?
• Can
machine
assisted
gene
therapy
become
a
cure-­‐all?
• What
are
some
of
the
opportuni)es
and
challenges
in
the
mHealth
space?

5. Source: ©World Health Organization

6. The
Healthcare
Challenge:
World
Map
of
Access
to
Physicians
© Copyright Sasi Group (University of Sheffield) and Mark Newman (University of Michigan).

7. Healthcare
Spending
Source: World Health Organization, World Health Statistics, 2010: Table 7

8. Propor)onal
Mortality
Rate
in
India
Source: World Health Organization

9. E-­‐Governance
in
Healthcare
• Governance
is
all
about
inclusion,
provisioning
and
common
good
–
control
is
a
small
part.
• Work
mostly
has
been
in
automa)ng
processes
and
dissemina)ng
informa)on
to
masses.
• Just
one
MMP
in
NeGP:
mcts.
• Governments
have
the
power
to
influence
and
provision
the
research
needs.
• Need
for
soWware
applica)ons
in
health
that
can
reach
the
masses
and
revolu)onize
care.
• What
are
some
such
applica)ons?

10. Mo)va)on
and
Enabling
Factors
• Millions
of
underprivileged
who
do
not
have
access
to
healthcare.
• Automated
diagnosis
has
been
a
formidable
challenge
for
the
past
4
decades.
• Prolifera)on
of
portable
compu)ng
devices,
wearables.
• Informa)on
Retrieval
has
come
of
age:
Self-­‐
Diagnosis
on
the
Internet
is
common.
• Cloud
Compu)ng
gives
ubiquitous
access
to
enormous
processing
power.

11. Gartner’s
Hype
Cycle
2014
Source: © Gartner

12. The
Reach
of
Mobile
Devices
in
Developing
Countries

13. mHealth:
An
Overview
• Use
of
mobile
devices
for
various
aspects
of
healthcare
is
part
of
eHealth,
called
mHealth.
• From
Telemedicine
to
awareness
campaigns.
• Connect
pa)ents,
community
health
workers
and
physicians
to
serve
at
the
point
of
care.
• Phone
as
a
point-­‐of-­‐care
device:
devices
such
as
ultrasound
probe
plugged
into
the
phone.
• Scores
of
projects
already
func)oning
successfully,
par)cularly
in
India.

14. Machine
Augmented
Mindfulness
• Mind
monitors
health,
diagnoses
condi)ons
and
even
cures
at
a
molecular
level.
• Harvard
studies
established
that
mindfulness
creates
a
feedback
loop
to
govern
the
health.
• Even
deadly
diseases
such
as
cancer
are
found
to
be
caused
by
the
imbalances
in
the
mind.
• Can
machines
take-­‐on
mindfulness,
like
they
took
over
reasoning,
compu)ng
and
thinking?

15. Machine
Augmented
Mindfulness:
Current
State
• Machines
have
successfully
diagnosed
diseases.
• Molecular
level
cure
is
possible
via
gene
therapy
that
is
a_rac)ng
huge
investments.
• Wearables
make
it
possible
to
constantly
monitor
the
state
of
health.
• Mind
cannot
be
replaced,
but
some
of
its
func)onality
can
be
replicated.
• We
just
put
all
these
pieces
of
the
puzzle
together
to
unfold
the
vision!

16. The
Vision:
E-­‐Governance
in
Healthcare
What happens in the cloud is the Research Focus
Government
owns the
cloud and the
processes in it
Medical diagnosis is
just one example of
what happens in
the cloud

17. Approaches
to
Medical
Diagnosis
• Conven)onal:
First-­‐Order
Logic
based
reasoning;
Rule-­‐bases
in
olden
days;
Seman)c
Web
now.
• MYCIN
from
Stanford
had
600
rules
and
did
be_er
than
Medical
Experts.
• Latest
Trend:
Using
Machine
Learning
and
Informa)on
Retrieval.
• None
of
the
solu)ons
so
far
are
general
purpose
and
suitable
for
mass
deployment.
• Our
approach
considers
it
as
a
problem
in
Text
Mining:
Given
a
set
of
discharge
sheets,
iden)fy
the
one
that’s
closest
to
the
given
symptoms.

18. Guidelines
Need
a
solu)on
for
medical
diagnosis
that
• Is
based
on
available
data:
past
diagnoses
• Is
general,
inexpensive,
and
adequate
enough
to
be
used
by
the
masses
• Can
be
implemented
using
current
technologies
for
faster
availability
• Does
not
need
a
whole
lot
of
knowledge
engineering
or
ongoing
expert
maintenance.

19. A
Sample
Discharge
Sheet
Diagnosis: Allergic Bronchitis with Asthma
Case Summary: Patient 36 years male was admitted with complaints
of breathlessness & cough for last 7 days. At the time of admission
Pulse 126/min, BP 130/90 mmHg, RR 24/min, SpO2 94 with O2,
Chest spasms wheezing+, ronchi++. Patient was investigated &
treated conservatively with I/V antibiotics, I/V fluids, Nebulization &
other supportive treatment. Now the patient is being discharged in
satisfactory condition.
Treatment Advice:
* Tab. Augmentin 1 gm 1 tab. twice daily
* Syp. Rapitus 2 TSF thrice daily
* Tab. Deriphyllin-R 150 mg 1 tab. twice daily
* Forocort Rotacap 1 cap. Twice daily with Rotahaler

20. Wordcloud
from
the
Discharge
Sheets

21. Text
mining
the
Corpus
• Preprocessing:
Remove
stopwords,
numbers,
punctua)on,
tags,
sparse
terms
and
convert
case.
• Each
document
is
represented
as
a
vector
(a
point)
in
a
mul)-­‐dimensional
space.
• Each
dimension
is
a
word
in
the
corpus
=>
thousands
of
dimensions.
• Each
document
has
a
score
(TF-­‐IDF)
for
each
word
used
in
it
that
determines
its
posi)on.
• Each
point
is
labeled
with
the
diagnosis.

22. 3D
Visualiza)on
of
the
Corpus

23. Enter:
The
Pa)ent
• The
closer
the
points
in
the
vector
space,
the
more
similar
the
documents.
• Document
with
pa)ent’s
symptoms
are
also
represented
as
a
vector
in
the
same
vector
space.
• The
label
on
the
closest
point
to
this
symptoms
document
in
the
vector
space
is
the
diagnosis.
• Find
K-­‐Nearest
Neighbors
(K-­‐NN)
of
the
symptoms
document
to
suggest
possible
alterna)ves.
• The
K
nearest
neighboring
discharge
sheets
and
the
symptoms
doc
can
be
used
to
find
K
relevant
ar)cles
for
further
reference
and
decision
support.

24. Simple
Math
with
Profound
Impact
• TF.IDF
Score
is
computed
as:
!.idft,d
=
!t,d
*
idft
where
• Similarity
between
two
discharge
sheets
is
where
the
norm
of
a
vector,
|
|
is
being
the
m.idf
value
of
a
feature
(term
/
word),
i

25. Challenges
• Privacy
and
Security:
Hacking
can
be
fatal.
• Cost:
Technology
is
s)ll
in
commercializa)on
zone.
• Dataset:
Government
mandate
may
be
needed.
• Human
Exper)se:
Interven)on
needed
at
)mes.
• Quality
of
available
dataset.
• Mul)lingual
support:
22
official
languages,
1,652
different
"mother
tongues"
in
India
alone.
• High
Cost
of
Type
I
and
II
errors.
• Acceptance:
Skep)cism
–
government
can
again
help
here
to
influence
posi)ve
outlook.

26. Future
Direc)ons
• Processing
images,
video,
and
audio
(including
ultrasound)
in
conjunc)on
with
text
informa)on.
• Real-­‐)me,
stateful
big
data
processing.
• Aggrega)on
of
health
data
to
detect
or
predict
epidemics
and
health
trends.
• Add
a
QA
interface
using
NLP,
IVR,
Machine
Transla)on.
• Extend
the
ideas
to
monitor
and
proac)vely
remedy
abnormali)es.

27. Conclusion
• ICT
for
Healthcare
is
a
formidable
challenge
and
a
huge
opportunity
for
e-­‐governance.
• Mobile
is
the
www
of
90’s
–
the
conduit
to
take
solu)ons
to
the
masses
and
manifold
the
RoI.
• Good
)me
to
revisit
‘70s
ideas
–
we
now
have
the
compu)ng
resources
that
weren’t
there
then.
• And
more
importantly…

28. You
can
be
a
Grand
Winner
too…
Source: http://www.npr.org/blogs/goatsandsoda/2014/09/26/351515298/and-the-million-dollar-hult-prize-goes-to-a-doc-in-a-box

29. Even
more
importantly…

30. A
Great
Quote
Math
is
the
heart
of
ma_er.
Once
expressed
in
math,
the
ma_er
dissolves
and
yields,
just
like
when
you
touch
a
person’s
heart,
he
dissolves
and
yields.

31. Questions for you
• Why
is
technical
research
important
for
effec)ve
e-­‐governance?
• How
is
mHealth
related
to
Machine
Learning,
Big
Data
Analy)cs
and
Internet
of
Things?
• How
can
the
millions
who
do
not
have
access
to
proper
healthcare
be
provisioned
with
it?
• Can
machines
replace
the
doctor
eventually?
• Can
machine
assisted
gene
therapy
become
a
cure-­‐all?
• What
are
some
of
the
opportuni)es
and
challenges
in
the
mHealth
space?

32. Resources
h_p://www.ecgcheck.com/
h_p://www.alivecor.com/home
h_p://www.mobisante.com
h_p://www.technologyreview.com/news/421827/
ultrasound-­‐gets-­‐more-­‐portable/
h_ps://www.google.com/search?q=%22Mobile
%20Alliance%20for%20Maternal%20Ac)on
h_p://en.wikipedia.org/wiki/Scanadu
h_p://en.wikipedia.org/wiki/Lab-­‐on-­‐a-­‐chip
h_p://nrhm-­‐mcts.nic.in

33. References
[1]
Pendyala,
V.S.,
Fang,
Y.,
Holliday,
J.,
Zalzala,
A.,
(2014,
October).
A
Text
Mining
Approach
to
Automated
Healthcare
for
the
Masses.
In
Global
Humanitarian
Technology
Conference
(GHTC),
2014,
IEEE
Interna)onal
Conference
on.
IEEE.
[2]
Pendyala,
V.S.,
&
Shim,
S.
S.
(2009).
The
Web
as
the
Ubiquitous
Computer.
IEEE
Computer,
42(9)
90-­‐92.
[3]
Pendyala,
V.S.,
&
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J.
(2010,
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Performing
intelligent
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in
the
cloud
using
seman)c
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34. References
(Contd.)
[6]
Anderson,
C.
M.
(2000).
From
molecules
to
mindfulness:
How
ver)cally
convergent
fractal
)me
fluctua)ons
unify
cogni)on
and
emo)on*.
Consciousness
&
Emo)on,
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193-­‐226.
[7]
Amritanshuram,
R.,
Nagendra,
H.
R.,
Shastry,
A.
S.
N.,
Raghuram,
N.
V.,
&
Nagarathna,
R.
(2013).
A
psycho-­‐oncological
model
of
cancer
according
to
ancient
texts
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yoga.
Journal
of
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and
Physical
Therapies,
3,
129.
[8]
Reibel,
D.
K.,
Greeson,
J.
M.,
Brainard,
G.
C.,
&
Rosenzweig,
S.
(2001).
Mindfulness-­‐based
stress
reduc)on
and
health-­‐related
quality
of
life
in
a
heterogeneous
pa)ent
popula)on.
General
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psychiatry,
23(4),
183-­‐192.
[9]
Chandwani,
K.
D.,
Chaoul-­‐Reich,
A.,
Biegler,
K.
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&
Cohen,
L.
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&
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J.
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300(11),
1350-­‐1352.

35. Q&A

36. MORE Q&A?
vishnu@pendyalas.org
h3ps://www.linkedin.com/in/pendyala
Twi3er:
@vishnupendyala